diff --git a/.agents/skills/a2ui-add-eval-datapoint/SKILL.md b/.agents/skills/a2ui-add-eval-datapoint/SKILL.md
new file mode 100644
index 0000000000..40443c6ad1
--- /dev/null
+++ b/.agents/skills/a2ui-add-eval-datapoint/SKILL.md
@@ -0,0 +1,106 @@
+---
+name: a2ui-add-eval-datapoint
+description: Step-by-step workflow for adding and verifying new evaluation data points in the A2UI evaluation suite.
+---
+
+# Adding and verifying A2UI evaluation data points
+
+Use this skill when adding new data points or datasets to the A2UI evaluation framework in `eval/`.
+
+## References
+
+For full architecture and setup details, consult:
+
+- `eval/README.md`: Evaluation quickstart, Transcrypt setup, and CLI flags.
+- `eval/DESIGN.md`: Architecture, multi-stage scorers, and encryption-at-rest design.
+- `eval/datasets/dataset_schema.json`: Formal JSON Schema defining required fields and structural rules for evaluation data points.
+
+---
+
+## Step-by-step workflow
+
+### 1. Unlock Transcrypt
+
+Datasets in `eval/datasets/` are encrypted at rest. Unlock Transcrypt locally before authoring:
+
+```bash
+cd eval
+bin/transcrypt -y -c aes-256-cbc -p <PASSWORD>
+```
+
+### 2. Author the data point
+
+Create or update a dataset file in `eval/datasets/<dataset_name>.yaml` (e.g. `eval/datasets/my_dataset.yaml`).
+
+Every data point **must conform** to the JSON Schema in `eval/datasets/dataset_schema.json`.
+
+```yaml
+- name: sample_unique_identifier
+  dataset: my_dataset
+  description: Clear summary of what this scenario tests.
+  catalog: 'specification/{version}/catalogs/basic/catalog.json'
+  system_prompt: |
+    Optional domain-specific system instructions (e.g. company policies, triage rules).
+  messages:
+    - role: user
+      content: 'Initial user request...'
+    - role: assistant
+      content: 'Assistant response...'
+      tool_calls:
+        - id: call_001
+          type: function
+          function: query_database
+          arguments:
+            key: value
+    - role: tool
+      tool_call_id: call_001
+      function: query_database
+      content: '{"result": "data"}'
+    - role: user
+      content: 'Render the UI card with the retrieved data...'
+  target: |
+    Expected UI outcome and scoring criteria for the LLM judge.
+```
+
+### 3. Validate schema compliance
+
+Verify that all data points satisfy `eval/datasets/dataset_schema.json`:
+
+```bash
+cd eval
+uv run python -m pytest tests/test_dataset.py
+```
+
+### 4. Run an evaluation on the new data point
+
+Always execute an evaluation run on the newly added dataset to verify model inference and scoring:
+
+```bash
+cd eval
+# Quick validation on gemini-3.1-flash-lite
+uv run main.py --dataset my_dataset --sanity
+
+# Full evaluation check
+uv run main.py --dataset my_dataset
+```
+
+View the interactive traces and judging rationales:
+
+```bash
+uv run inspect view start
+```
+
+### 5. Verify encryption and commit
+
+When staging changes, Git applies the Transcrypt clean filter. Confirm that the staged file is encrypted ciphertext before committing:
+
+```bash
+# Stage the new dataset file
+git add eval/datasets/my_dataset.yaml
+
+# Verify staged content is encrypted ciphertext (not plaintext)
+git diff --cached eval/datasets/my_dataset.yaml
+
+# Commit the encrypted data point
+git commit -m "feat(eval): add my_dataset evaluation data points"
+```
diff --git a/eval/DESIGN.md b/eval/DESIGN.md
index c5375599ba..a63e9bb09c 100644
--- a/eval/DESIGN.md
+++ b/eval/DESIGN.md
@@ -14,12 +14,12 @@ The framework adopts the three-pillar architecture of Inspect AI—Tasks, Solver
 
 | Inspect Component | A2UI Implementation Detail                                                 | Primary Responsibility                                            |
 | :---------------- | :------------------------------------------------------------------------- | :---------------------------------------------------------------- |
-| Dataset           | Encrypted archives resolved into EvalSet JSON.                             | Providing structured prompts and ground-truth UI targets.         |
-| Solver            | Uses `A2uiSchemaManager` to inject system prompt, then generates response. | Orchestrating the model's attempt to generate a valid UI payload. |
-| Scorer            | A hybrid of SDK-based validation and LLM-as-a-judge.                       | Determining the accuracy and usability of the generated JSON.     |
+| Dataset           | Encrypted YAML files conforming to `datasets/dataset_schema.json`.         | Providing structured `messages` turns, catalogs, and UI targets.  |
+| Solver            | Uses `A2uiSchemaManager` to inject system prompt and protocol rules.       | Orchestrating the model's attempt to generate a valid UI payload. |
+| Scorer            | Algorithmic `a2ui_scorer` validation and `measured_model_graded_qa` judge. | Determining technical validity and semantic intent quality.       |
 | Model Provider    | Support for Gemini, OpenAI, Anthropic, and local vLLM.                     | Standardizing API interactions across diverse model families.     |
 
-The interaction flow begins with the resolution of a dataset sample. A solver then prepares the environment, using `A2uiSchemaManager` to provide the model with the correct protocol schemas and a "catalog" of available A2UI components—a critical constraint of the A2UI security model. The model generates a response, which is then parsed and corrected using the SDK's parser tools, and finally passed through a series of Scorers to assess its technical validity and semantic alignment with the user's intent.
+The interaction flow begins with loading a dataset sample. Each sample specifies a `catalog` path, optional domain `system_prompt`, and a list of `messages` representing the conversation history (including user requests, assistant responses, and tool calls). The solver merges the domain system prompt with A2UI protocol instructions and passes the conversational context to the model. The generated UI payload is parsed and evaluated by `a2ui_scorer` for schema validity and `measured_model_graded_qa` for semantic quality.
 
 ### **Integration with A2UI Python SDK**
 
diff --git a/eval/README.md b/eval/README.md
index 93a3ac7812..b4e2ae04da 100644
--- a/eval/README.md
+++ b/eval/README.md
@@ -1,15 +1,33 @@
 # A2UI Evaluation Framework
 
-This folder contains evaluation tests (aka evals) for the A2UI project.
-An evaluation test verifies that a prompt produces expected results conforming to the A2UI schema and semantic rules.
+This folder contains evaluation tests (aka evals) for the A2UI project using the [Inspect AI](https://inspect.aisi.org.uk/) framework.
+An evaluation test verifies that a prompt or conversational history produces expected UI results conforming to the A2UI schema and semantic rules.
 
 ## Design
 
-For a detailed overview of the design, secrets management, and contamination prevention, see the [DESIGN.md](DESIGN.md) file.
+For a detailed overview of the evaluation architecture, multi-stage scoring, and secret management, see [DESIGN.md](DESIGN.md).
+
+## Datasets and Schema
+
+Evaluation data points live in `datasets/*.yaml` files (e.g., `datasets/core_v0_9_1.yaml`, `datasets/core_v1_0.yaml`, `datasets/multi_turn_conversation_dataset.yaml`).
+
+Every dataset file must conform to the JSON schema defined in `datasets/dataset_schema.json`.
+
+### Dataset Structure
+
+Each sample in a dataset YAML file defines:
+
+- **`name`** (required): Unique identifier for the sample.
+- **`description`** (required): Human-readable summary of the test scenario.
+- **`catalog`** (required): Relative path to the component catalog (e.g., `"specification/{version}/catalogs/basic/catalog.json"`).
+- **`dataset`** (optional): Logical dataset grouping name (defaults to file basename).
+- **`system_prompt`** (optional): Domain-specific system instructions (e.g. clinical triage rules or travel policies).
+- **`messages`** (required): Chat conversation turns (`user`, `assistant` with optional `tool_calls`, `tool` returns, and `system`).
+- **`target`** (optional): Expected UI outcome description and grading criteria for the LLM judge.
 
 ## Running Evaluations
 
-To run the evaluations, you need to use the Inspect AI CLI via `uv`. Make sure you are in this directory (`evals/eval`).
+Make sure you are in the `eval/` directory.
 
 ### Prerequisites
 
@@ -20,21 +38,19 @@ To run the evaluations, you need to use the Inspect AI CLI via `uv`. Make sure y
    ```
 
 2. **Decrypt Datasets (First Time Setup)**:
-   The evaluation datasets are encrypted at rest in the repository to prevent base model contamination. To decrypt them in your repo for evaluation, you need to initialize Transcrypt with the shared password. From the `evals/eval` directory, run:
+   The evaluation datasets are encrypted at rest in the repository to prevent base model contamination. To decrypt them locally, initialize Transcrypt with the shared password:
 
    ```bash
    bin/transcrypt -p <PASSWORD>
    ```
 
-You can request the password from any member of the A2UI team (it's not really a secret, but it's also not going on Github in plaintext).
-
-After this one time setup, you will have local plaintext access to the decrypted datasets in the `datasets/` directory, and they will be encrypted and decrypted transparently by git.
+   After this setup, git transparently encrypts files on `git add` and decrypts them on checkout.
 
-### Upgrading transcrypt
+### Upgrading Transcrypt
 
 If you pull updates that change the encryption settings (such as transitioning from MD5 to PBKDF2), you may encounter decryption errors during `git pull` or see OpenSSL deprecation warnings.
 
-To upgrade your local transcrypt configuration to the latest settings:
+To upgrade your local Transcrypt configuration to the latest settings:
 
 1. Run the upgrade command:
 
@@ -45,27 +61,38 @@ To upgrade your local transcrypt configuration to the latest settings:
    This updates the local filter scripts in your `.git` directory while preserving your saved password.
 
 2. Force Git to re-decrypt the files:
+
    ```bash
    git checkout HEAD -- $(git ls-crypt)
    ```
+
    This runs the files through the newly upgraded smudge filter, decrypting them.
 
-## Dimensions
+### Executing Evals
+
+To run all datasets:
 
-Evaluations run across the following dimensions:
+```bash
+uv run main.py
+```
 
-- Strategies: Different ways of orchestrating A2UI generation
-- Samples: Data in `dataset/`
+To run a specific dataset or multiple datasets:
+
+```bash
+# Run a single dataset
+uv run main.py --dataset multi_turn_conversation_dataset
 
-### Run Evals
+# Run multiple datasets
+uv run main.py --datasets core_v0_9_1,multi_turn_conversation_dataset
+```
 
-To run the evaluations:
+To test across different inference formats (`direct` JSON, `express` XML tags, `elemental` DSL):
 
 ```bash
-uv run main.py
+uv run main.py --dataset multi_turn_conversation_dataset --strategies direct,express,elemental
 ```
 
-For a quick 2-sample validation using `gemini-3.1-flash-lite`, use the sanity flag:
+For a quick 2-sample validation using `gemini-3.1-flash-lite`:
 
 ```bash
 uv run main.py --sanity
@@ -73,29 +100,23 @@ uv run main.py --sanity
 
 ## Viewing Evaluation Results
 
-Inspect AI provides a web-based log viewer to explore the results of your evaluations.
-
-To start the log viewer:
+Inspect AI provides a web-based log viewer to explore interactive traces and judge rationales:
 
 ```bash
 uv run inspect view start
 ```
 
-This will start a local web server (usually at `http://localhost:7575`) and open the viewer in your browser. It will automatically load logs from the `logs/` directory.
+This starts a local web server (usually at `http://localhost:7575`).
 
-## Listing Available Models
-
-To list the available Gemini models supported by your API key:
+To print a console summary or markdown table from an eval log file:
 
 ```bash
-uv run inspect eval tasks.py -T list_models=True --model google/gemini-3-flash-preview
+uv run python bin/report_evals.py logs/<log_filename>.eval
 ```
 
-(the `--model` flag is required even though it is ignored)
-
-## Running Unit Tests
+## Running Unit Tests & Schema Validation
 
-To run the unit tests for the evaluation framework (dataset loader, solvers, scorers):
+To run the unit tests and validate all dataset files against `datasets/dataset_schema.json`:
 
 ```bash
 uv run python -m pytest
diff --git a/eval/a2ui_eval/dataset.py b/eval/a2ui_eval/dataset.py
index b5b2b67200..704bc1c6ea 100644
--- a/eval/a2ui_eval/dataset.py
+++ b/eval/a2ui_eval/dataset.py
@@ -15,22 +15,32 @@
 """Dataset loader for A2UI evaluation."""
 
 import json
-import os
+from pathlib import Path
+from typing import Any
 
 import jsonschema
 import yaml
 from inspect_ai.dataset import MemoryDataset, Sample
+from inspect_ai.model import (
+    ChatMessage,
+    ChatMessageAssistant,
+    ChatMessageSystem,
+    ChatMessageTool,
+    ChatMessageUser,
+)
+from inspect_ai.tool import ToolCall
 
+from a2ui_eval.shared.utils import GIT_ROOT
 from datasets.defaults import (
     DEFAULT_CATALOG_PATH,
-    DEFAULT_WORKFLOW_DESCRIPTION,
     DEFAULT_ROLE_DESCRIPTION,
+    DEFAULT_WORKFLOW_DESCRIPTION,
     FORMAT_AGNOSTIC_ROLE_DESCRIPTION,
     FORMAT_AGNOSTIC_WORKFLOW_DESCRIPTION,
 )
-from a2ui_eval.shared.utils import GIT_ROOT
 
 SCHEMA_PATH = GIT_ROOT / "eval" / "datasets" / "dataset_schema.json"
+DATASETS_DIR = GIT_ROOT / "eval" / "datasets"
 
 
 def _version_to_dir_name(version: str) -> str:
@@ -38,74 +48,203 @@ def _version_to_dir_name(version: str) -> str:
     return "v" + version.replace(".", "_")
 
 
+def _parse_tool_calls(
+    raw_tool_calls: list[dict[str, Any]] | None,
+) -> list[ToolCall] | None:
+    """Converts raw tool calls in native Inspect AI format into ToolCall objects."""
+    if not raw_tool_calls:
+        return None
+    parsed = []
+    for tc in raw_tool_calls:
+        func_name = tc.get("function")
+        if isinstance(func_name, dict):
+            func_args = func_name.get("arguments", {})
+            func_name = str(func_name.get("name", ""))
+        else:
+            func_name = str(func_name or "")
+            func_args = tc.get("arguments", {})
+
+        if isinstance(func_args, str):
+            try:
+                func_args = json.loads(func_args)
+            except (json.JSONDecodeError, TypeError):
+                # If it's not a valid JSON string, treat it as a raw string argument.
+                pass
+
+        parsed.append(
+            ToolCall(
+                id=str(tc.get("id", "")),
+                function=func_name,
+                arguments=func_args,
+                type=tc.get("type", "function"),
+            )
+        )
+    return parsed
+
+
+def _parse_messages(item: dict[str, Any]) -> list[ChatMessage]:
+    """Parses a sample's messages list or promptText into Inspect AI ChatMessage objects."""
+    messages_raw = item.get("messages")
+    if messages_raw:
+        parsed: list[ChatMessage] = []
+        call_id_to_func: dict[str, str] = {}
+        for m in messages_raw:
+            role = m.get("role", "user")
+            content = m.get("content", "")
+            if role == "user":
+                parsed.append(ChatMessageUser(content=content))
+            elif role == "assistant":
+                tool_calls = _parse_tool_calls(m.get("tool_calls"))
+                if tool_calls:
+                    for tc in tool_calls:
+                        if tc.id and tc.function:
+                            call_id_to_func[tc.id] = tc.function
+                parsed.append(
+                    ChatMessageAssistant(
+                        content=content,
+                        tool_calls=tool_calls,
+                    )
+                )
+            elif role == "tool":
+                call_id = m.get("tool_call_id", "")
+                func_name = (
+                    m.get("function")
+                    or m.get("name")
+                    or call_id_to_func.get(call_id, "tool_response")
+                )
+                parsed.append(
+                    ChatMessageTool(
+                        content=content,
+                        tool_call_id=call_id,
+                        function=func_name,
+                    )
+                )
+            elif role == "system":
+                parsed.append(ChatMessageSystem(content=content))
+            else:
+                parsed.append(ChatMessageUser(content=content))
+        return parsed
+    elif "promptText" in item:
+        return [ChatMessageUser(content=item["promptText"])]
+    else:
+        raise ValueError(
+            f"Sample {item.get('name')} must specify 'messages' or 'promptText'."
+        )
+
+
 def load_a2ui_dataset(
-    file_path: str,
+    file_path: str | Path | None = None,
+    dataset: str | list[str] | None = None,
     default_catalog_path: str | None = None,
     version: str | None = None,
     format_name: str | None = None,
 ) -> MemoryDataset:
-    """Loads A2UI evaluation samples from a YAML file.
-
-    Args:
-        file_path: The path to the YAML dataset file.
-        default_catalog_path: The default catalog path to use if not specified in
-          the sample.
-        version: Optional target version string to substitute into catalog paths.
-
-    Returns:
-        A MemoryDataset containing the resolved samples.
-
-    Raises:
-        FileNotFoundError: If the dataset file does not exist.
-    """
-    if not os.path.exists(file_path):
-        raise FileNotFoundError(f"Dataset file not found: {file_path}")
-
-    with open(file_path, "r", encoding="utf-8") as f:
-        data = yaml.safe_load(f)
-
+    """Loads A2UI evaluation samples from YAML dataset files."""
     with open(SCHEMA_PATH, "r", encoding="utf-8") as f:
         schema = json.load(f)
 
-    jsonschema.validate(instance=data, schema=schema)
+    filter_names = None
+    if dataset:
+        if isinstance(dataset, str):
+            filter_names = [d.strip() for d in dataset.split(",") if d.strip()]
+        else:
+            filter_names = list(dataset)
+
+    target_files: list[Path] = []
+    if file_path:
+        p = Path(file_path)
+        if not p.is_absolute():
+            p = (DATASETS_DIR / p).resolve() if not p.exists() else p.resolve()
+        if not p.exists():
+            raise FileNotFoundError(f"Dataset file not found: {p}")
+        target_files.append(p)
+    else:
+        for yaml_file in sorted(DATASETS_DIR.glob("*.yaml")):
+            target_files.append(yaml_file)
 
     is_json = format_name is None or format_name == "json"
+    samples: list[Sample] = []
+
+    for target_file in target_files:
+        with open(target_file, "r", encoding="utf-8") as f:
+            data = yaml.safe_load(f) or []
+
+        jsonschema.validate(instance=data, schema=schema)
+        inferred_dataset = target_file.stem
+
+        for item in data:
+            item_dataset = item.get("dataset") or inferred_dataset
+            if filter_names and item_dataset not in filter_names:
+                continue
+
+            # Skip datasets specifically targeted at another protocol version if version is set
+            if version == "0.9.1" and (
+                item_dataset.endswith("v1_0") or item_dataset == "core_v1_0"
+            ):
+                continue
+            if version == "1.0" and (
+                item_dataset.endswith("v0_9_1") or item_dataset == "core_v0_9_1"
+            ):
+                continue
+
+            catalog_path = (
+                item.get("catalog") or default_catalog_path or DEFAULT_CATALOG_PATH
+            )
+            if version and catalog_path:
+                catalog_path = catalog_path.replace(
+                    "{version}", _version_to_dir_name(version)
+                )
+
+            default_role = (
+                DEFAULT_ROLE_DESCRIPTION
+                if is_json
+                else FORMAT_AGNOSTIC_ROLE_DESCRIPTION
+            )
+            default_workflow = (
+                DEFAULT_WORKFLOW_DESCRIPTION
+                if is_json
+                else FORMAT_AGNOSTIC_WORKFLOW_DESCRIPTION
+            )
 
-    samples = []
-    for item in data:
-        catalog_path = (
-            item.get("catalog") or default_catalog_path or DEFAULT_CATALOG_PATH
-        )
-        if version and catalog_path:
-            catalog_path = catalog_path.replace(
-                "{version}", _version_to_dir_name(version)
+            protocol_role = (
+                item.get("protocol_role")
+                or item.get("role_description")
+                or default_role
+            )
+            generation_rules = (
+                item.get("generation_rules")
+                or item.get("workflow_description")
+                or default_workflow
             )
 
-        default_role = (
-            DEFAULT_ROLE_DESCRIPTION if is_json else FORMAT_AGNOSTIC_ROLE_DESCRIPTION
-        )
-        default_workflow = (
-            DEFAULT_WORKFLOW_DESCRIPTION
-            if is_json
-            else FORMAT_AGNOSTIC_WORKFLOW_DESCRIPTION
-        )
+            chat_messages = _parse_messages(item)
 
-        role_description = item.get("role_description") or default_role
-        workflow_description = item.get("workflow_description") or default_workflow
-
-        samples.append(
-            Sample(
-                input=item["promptText"],
-                target=item.get("target") or item["description"],
-                metadata={
-                    "name": item.get("name"),
-                    "description": item["description"],
-                    "catalog": catalog_path,
-                    "role_description": role_description,
-                    "workflow_description": workflow_description,
-                    "allowed_surface_ids": item.get("allowed_surface_ids") or ["main"],
-                },
+            sample_id = (
+                f"{item_dataset}_{item.get('name')}"
+                if item.get("name")
+                else str(len(samples))
+            )
+
+            samples.append(
+                Sample(
+                    id=sample_id,
+                    input=chat_messages,
+                    target=item.get("target") or item["description"],
+                    metadata={
+                        "name": item.get("name"),
+                        "dataset": item_dataset,
+                        "description": item["description"],
+                        "catalog": catalog_path,
+                        "system_prompt": item.get("system_prompt", ""),
+                        "protocol_role": protocol_role,
+                        "generation_rules": generation_rules,
+                        "role_description": protocol_role,
+                        "workflow_description": generation_rules,
+                        "allowed_surface_ids": (
+                            item.get("allowed_surface_ids") or ["main"]
+                        ),
+                    },
+                )
             )
-        )
 
     return MemoryDataset(samples=samples)
diff --git a/eval/a2ui_eval/strategies/format.py b/eval/a2ui_eval/strategies/format.py
index ecb39a1ce2..df731265e1 100644
--- a/eval/a2ui_eval/strategies/format.py
+++ b/eval/a2ui_eval/strategies/format.py
@@ -80,15 +80,29 @@ async def solve(state: TaskState, generate: Generate) -> TaskState:
         catalog_config = CatalogConfig.from_path("basic_catalog", resolved_catalog_path)
         strategy = _get_strategy(format_name, version, catalog_config)
 
-        role_description = state.metadata.get("role_description", "")
-        workflow_description = state.metadata.get("workflow_description", "")
+        role_description = state.metadata.get("protocol_role") or state.metadata.get(
+            "role_description", ""
+        )
+        workflow_description = state.metadata.get(
+            "generation_rules"
+        ) or state.metadata.get("workflow_description", "")
 
-        prompt = strategy.prompt_generator.generate(
+        a2ui_prompt = strategy.prompt_generator.generate(
             role_description=role_description,
             workflow_description=workflow_description,
             include_schema=True,
         )
-        state.messages.insert(0, ChatMessageSystem(content=prompt))
+
+        domain_prompt = state.metadata.get("system_prompt", "").strip()
+        if domain_prompt:
+            full_prompt = (
+                f"## Domain Instructions\n{domain_prompt}\n\n## UI Protocol"
+                f" Instructions\n{a2ui_prompt}"
+            )
+        else:
+            full_prompt = a2ui_prompt
+
+        state.messages.insert(0, ChatMessageSystem(content=full_prompt))
         return state
 
     return solve
diff --git a/eval/bin/report_evals.py b/eval/bin/report_evals.py
index c7cbc64ae2..74d17c67c8 100755
--- a/eval/bin/report_evals.py
+++ b/eval/bin/report_evals.py
@@ -25,16 +25,16 @@
 
 
 def extract_accuracy(log_data: dict[str, Any]) -> float:
-    """Extracts accuracy from parsed JSON log data.
+    """Extracts accuracy metric from parsed JSON log data.
 
     Args:
-        log_data: Parsed JSON data from inspect log dump.
+        log_data: Dictionary containing parsed evaluation log data.
 
     Returns:
-        The accuracy score as a float.
+        The accuracy score as a float between 0.0 and 1.0.
 
     Raises:
-        ValueError: If scores or accuracy are not found or invalid.
+        ValueError: If no scores or accuracy metrics are present.
     """
     scores = log_data.get("results", {}).get("scores", [])
     if not scores:
@@ -51,69 +51,95 @@ def extract_accuracy(log_data: dict[str, Any]) -> float:
 
 
 def print_results_summary(log_data: dict[str, Any]) -> None:
-    """Prints a summary of the results for each sample.
+    """Prints a summary of the evaluation results for each sample grouped by dataset.
 
     Args:
-        log_data: Parsed JSON data from inspect log dump.
+        log_data: Dictionary containing parsed evaluation log data.
     """
     samples = log_data.get("samples", [])
     print("\n=== Evaluation Results Summary ===")
-    durations = []
-    for sample in samples:
-        name = sample.get("metadata", {}).get("name") or f"Sample {sample.get('id')}"
-        scores = sample.get("scores", {})
 
-        # Algorithmic validity (a2ui_scorer)
-        a2ui_score = scores.get("a2ui_scorer", {})
-        a2ui_passed = a2ui_score.get("value") == 1.0
-        a2ui_str = "PASS" if a2ui_passed else "FAIL"
+    # Group by dataset
+    datasets: dict[str, list[dict[str, Any]]] = {}
+    for sample in samples:
+        d_name = sample.get("metadata", {}).get("dataset") or "default"
+        datasets.setdefault(d_name, []).append(sample)
+
+    all_durations = []
+    dataset_stats: dict[str, dict[str, int]] = {}
+
+    for d_name, d_samples in datasets.items():
+        total = len(d_samples)
+        passed = 0
+        print(f"\n--- Dataset: {d_name} ---")
+        for sample in d_samples:
+            name = (
+                sample.get("metadata", {}).get("name") or f"Sample {sample.get('id')}"
+            )
+            scores = sample.get("scores", {})
+
+            # Algorithmic validity (a2ui_scorer)
+            a2ui_score = scores.get("a2ui_scorer", {})
+            a2ui_passed = a2ui_score.get("value") == 1.0
+            a2ui_str = "PASS" if a2ui_passed else "FAIL"
+
+            # Judging results (measured_model_graded_qa)
+            qa_score = scores.get("measured_model_graded_qa", {})
+            qa_val = qa_score.get("value", "N/A")
+
+            inference_time = None
+            for event in sample.get("events", []):
+                if event.get("event") == "model":
+                    inference_time = event.get("working_time") or event.get("duration")
+                    break
+            if inference_time is None:
+                inference_time = sample.get("metadata", {}).get(
+                    "evaluation_duration_seconds"
+                )
+            inference_time_str = (
+                f"{float(inference_time):.2f}s" if inference_time is not None else "N/A"
+            )
 
-        # Judging results (measured_model_graded_qa)
-        qa_score = scores.get("measured_model_graded_qa", {})
-        qa_val = qa_score.get("value", "N/A")
+            sample_passed = a2ui_passed and qa_val == "C"
+            if sample_passed:
+                passed += 1
 
-        inference_time = None
-        for event in sample["events"]:
-            if event["event"] == "model":
-                inference_time = event.get("working_time") or event.get("duration")
-                break
-        if inference_time is None:
-            inference_time = sample.get("metadata", {}).get(
-                "evaluation_duration_seconds"
+            print(
+                f"{name:<25} | Algorithmic: {a2ui_str:<4} | Judging: {qa_val:<2} |"
+                f" Inference Time: {inference_time_str}"
             )
-        inference_time_str = (
-            f"{float(inference_time):.2f}s" if inference_time is not None else "N/A"
-        )
 
-        print(
-            f"{name:<25} | Algorithmic: {a2ui_str:<4} | Judging: {qa_val:<2} |"
-            f" Inference Time: {inference_time_str}"
-        )
-
-        if not a2ui_passed or qa_val != "C":
-            if not a2ui_passed:
-                print(f"  [Algorithmic Failure Reason]:")
-                expl = a2ui_score.get("explanation") or "No explanation provided."
-                for line in expl.splitlines():
-                    print(f"    {line}")
-            if qa_val != "C":
-                print(f"  [Judging Failure Reason (Grade {qa_val})]:")
-                expl = qa_score.get("explanation") or "No explanation provided."
-                for line in expl.splitlines():
-                    print(f"    {line}")
-
-        if inference_time is not None:
-            durations.append(float(inference_time))
-
-    print("==================================")
-    if durations:
-        avg_duration = statistics.mean(durations)
-        med_duration = statistics.median(durations)
+            if not sample_passed:
+                if not a2ui_passed:
+                    print(f"  [Algorithmic Failure Reason]:")
+                    expl = a2ui_score.get("explanation") or "No explanation provided."
+                    for line in str(expl).splitlines():
+                        print(f"    {line}")
+                if qa_val != "C":
+                    print(f"  [Judging Failure Reason (Grade {qa_val})]:")
+                    expl = qa_score.get("explanation") or "No explanation provided."
+                    for line in str(expl).splitlines():
+                        print(f"    {line}")
+
+            if inference_time is not None:
+                all_durations.append(float(inference_time))
+
+        dataset_stats[d_name] = {"passed": passed, "total": total}
+
+    print("\n==================================")
+    print("Dataset Summary:")
+    for d_name, stats in dataset_stats.items():
+        pct = (stats["passed"] / stats["total"] * 100) if stats["total"] else 0.0
+        print(f"  {d_name:<16}: {stats['passed']}/{stats['total']} passed ({pct:.2f}%)")
+
+    if all_durations:
+        avg_duration = statistics.mean(all_durations)
+        med_duration = statistics.median(all_durations)
         print(
             f"Inference Time - Average: {avg_duration:.2f}s | Median:"
             f" {med_duration:.2f}s"
         )
-        print("==================================")
+    print("==================================")
 
 
 def generate_markdown_summary(
@@ -122,12 +148,12 @@ def generate_markdown_summary(
     """Generates a markdown summary of the evaluation results.
 
     Args:
-        log_data: Parsed JSON data from inspect log dump.
-        accuracy_percentage: The calculated accuracy percentage.
-        threshold: The threshold to compare against.
+        log_data: Dictionary containing parsed evaluation log data.
+        accuracy_percentage: Pass percentage achieved in the evaluation.
+        threshold: Minimum pass percentage required by CI.
 
     Returns:
-        A string containing the markdown summary.
+        A formatted markdown string summarizing evaluation performance.
     """
     eval_spec = log_data.get("eval", {}) or {}
     task_name = eval_spec.get("task", "Unknown Task")
@@ -144,23 +170,66 @@ def generate_markdown_summary(
         f" `{threshold:.2f}%`)"
     )
     lines.append("")
+
+    samples = log_data.get("samples", []) or []
+
+    # Dataset performance summary table
+    datasets: dict[str, list[dict[str, Any]]] = {}
+    for sample in samples:
+        d_name = sample.get("metadata", {}).get("dataset") or "default"
+        datasets.setdefault(d_name, []).append(sample)
+
+    if any(k != "default" for k in datasets.keys()):
+        lines.append("#### Dataset Performance Summary")
+        lines.append(
+            "| Dataset Name | Total Samples | Algorithmic Pass Rate | Judging Pass Rate"
+            " | Dataset Pass Rate |"
+        )
+        lines.append("| :--- | :---: | :---: | :---: | :---: |")
+
+        for d_name, d_samples in datasets.items():
+            total = len(d_samples)
+            a2ui_pass_cnt = sum(
+                1
+                for s in d_samples
+                if s.get("scores", {}).get("a2ui_scorer", {}).get("value") == 1.0
+            )
+            qa_pass_cnt = sum(
+                1
+                for s in d_samples
+                if s.get("scores", {}).get("measured_model_graded_qa", {}).get("value")
+                == "C"
+            )
+            both_pass_cnt = sum(
+                1
+                for s in d_samples
+                if s.get("scores", {}).get("a2ui_scorer", {}).get("value") == 1.0
+                and s.get("scores", {}).get("measured_model_graded_qa", {}).get("value")
+                == "C"
+            )
+            a2ui_pct = (a2ui_pass_cnt / total * 100) if total else 0.0
+            qa_pct = (qa_pass_cnt / total * 100) if total else 0.0
+            both_pct = (both_pass_cnt / total * 100) if total else 0.0
+            lines.append(
+                f"| `{d_name}` | {total} | {a2ui_pct:.1f}% | {qa_pct:.1f}% |"
+                f" {both_pct:.1f}% |"
+            )
+        lines.append("")
+
     lines.append("#### Sample Results")
     lines.append("| Sample / Task | Algorithmic | Judging | Inference Time | Status |")
     lines.append("| :--- | :---: | :---: | :---: | :---: |")
 
-    samples = log_data.get("samples", []) or []
     failures = []
 
     for sample in samples:
         name = sample.get("metadata", {}).get("name") or f"Sample {sample.get('id')}"
         scores = sample.get("scores", {}) or {}
 
-        # Algorithmic validity (a2ui_scorer)
         a2ui_score = scores.get("a2ui_scorer", {}) or {}
         a2ui_passed = a2ui_score.get("value") == 1.0
         a2ui_str = "PASS" if a2ui_passed else "FAIL"
 
-        # Judging results (measured_model_graded_qa)
         qa_score = scores.get("measured_model_graded_qa", {}) or {}
         qa_val = qa_score.get("value", "N/A")
 
@@ -169,7 +238,6 @@ def generate_markdown_summary(
             f"{float(inference_time):.2f}s" if inference_time is not None else "N/A"
         )
 
-        # Overall sample status
         sample_passed = a2ui_passed and qa_val == "C"
         sample_status_str = "PASS" if sample_passed else "FAIL"
 
@@ -213,13 +281,13 @@ def generate_markdown_summary(
 
 
 def load_log_data(log_path: str) -> dict[str, Any]:
-    """Runs inspect log dump to get JSON and parses it.
+    """Loads and parses Inspect AI evaluation log JSON data using inspect log dump.
 
     Args:
         log_path: Path to the .eval log file.
 
     Returns:
-        Parsed JSON data as a dictionary.
+        A dictionary containing parsed evaluation log data.
     """
     dump_cmd = ["uv", "run", "inspect", "log", "dump", log_path]
     dump_output = subprocess.check_output(dump_cmd, text=True)
@@ -228,6 +296,7 @@ def load_log_data(log_path: str) -> dict[str, Any]:
 
 
 def main() -> None:
+    """Main entrypoint for reporting evaluation results."""
     parser = argparse.ArgumentParser(
         description="Report results from an Inspect AI eval log file."
     )
@@ -235,31 +304,20 @@ def main() -> None:
     args = parser.parse_args()
 
     if not os.path.exists(args.log):
-        print(f"Error: Log file not found: {args.log}")
+        print(f"Error: Log file not found: {args.log}", file=sys.stderr)
         sys.exit(1)
 
     print(f"Processing log file: {args.log}")
 
     try:
         log_data = load_log_data(args.log)
-
-        # Print summary of results per sample
         print_results_summary(log_data)
-
-        try:
-            accuracy = extract_accuracy(log_data)
-            percentage = accuracy * 100
-            print(f"Pass percentage: {percentage:.2f}%")
-            sys.exit(0)
-        except ValueError as e:
-            print(f"Error: {e}")
-            sys.exit(1)
-
-    except subprocess.CalledProcessError as e:
-        print(f"Error running inspect log dump: {e}")
-        sys.exit(e.returncode)
-    except Exception as e:
-        print(f"Error processing log file: {e}")
+        accuracy = extract_accuracy(log_data)
+        percentage = accuracy * 100
+        print(f"Pass percentage: {percentage:.2f}%")
+        sys.exit(0)
+    except (subprocess.CalledProcessError, ValueError) as e:
+        print(f"Error processing log file: {e}", file=sys.stderr)
         sys.exit(1)
 
 
diff --git a/eval/bin/run_ci_evals.py b/eval/bin/run_ci_evals.py
index f259128f49..f6e001cd81 100755
--- a/eval/bin/run_ci_evals.py
+++ b/eval/bin/run_ci_evals.py
@@ -18,23 +18,28 @@
 import argparse
 import datetime
 import glob
-import json
 import os
 import shutil
 import subprocess
 import sys
 
-from report_evals import extract_accuracy, print_results_summary, load_log_data, generate_markdown_summary
+from report_evals import (
+    extract_accuracy,
+    generate_markdown_summary,
+    load_log_data,
+    print_results_summary,
+)
+
 # Automatically override Inspect AI's connection rate-limiter limit to prevent queuing delays in latency measurements
 os.environ["INSPECT_MAX_CONNECTIONS"] = "50"
 
 
 def check_threshold(percentage: float, threshold: float) -> bool:
-    """Compares percentage with threshold.
+    """Compares pass percentage against the required threshold.
 
     Args:
         percentage: The calculated pass percentage.
-        threshold: The threshold to compare against.
+        threshold: The minimum pass percentage threshold.
 
     Returns:
         True if percentage >= threshold, False otherwise.
@@ -43,14 +48,14 @@ def check_threshold(percentage: float, threshold: float) -> bool:
 
 
 def build_main_command(args: argparse.Namespace, seed: str) -> list[str]:
-    """Builds the command line arguments for main.py.
+    """Builds the command line arguments for main.py execution.
 
     Args:
         args: Parsed command line arguments.
-        seed: The random seed to use.
+        seed: Date-based seed for sample shuffling and logging.
 
     Returns:
-        A list of strings representing the command.
+        A list of command line arguments for subprocess execution.
     """
     cmd = [
         "uv",
@@ -68,20 +73,37 @@ def build_main_command(args: argparse.Namespace, seed: str) -> list[str]:
         "--grading-model",
         args.grading_model,
     ]
-    if args.max_samples != 0:
+    if getattr(args, "dataset", None):
+        cmd.extend(["--dataset", args.dataset])
+    if getattr(args, "datasets", None):
+        cmd.extend(["--datasets", args.datasets])
+    if getattr(args, "max_samples", None) and args.max_samples != 0:
         cmd.extend(["--limit", str(args.max_samples)])
     return cmd
 
 
 def main() -> None:
+    """Main entrypoint for CI evaluation runner."""
     parser = argparse.ArgumentParser(description="Run A2UI evals for CI.")
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        default=None,
+        help="Evaluate only a specific dataset name",
+    )
+    parser.add_argument(
+        "--datasets",
+        type=str,
+        default=None,
+        help="Comma-separated list of datasets to evaluate",
+    )
     parser.add_argument(
         "--max-samples",
         type=int,
         default=100,
         help=(
-            "Maximum number of samples to evaluate. Set to 0 for all samples. Default"
-            " is 100."
+            "Maximum number of samples to evaluate. Set to 0 for all samples."
+            " Default is 100."
         ),
     )
     parser.add_argument(
@@ -104,7 +126,6 @@ def main() -> None:
     )
     args = parser.parse_args()
 
-    # Find eval root (directory above bin)
     script_dir = os.path.dirname(os.path.abspath(__file__))
     eval_root = os.path.dirname(script_dir)
 
@@ -143,9 +164,8 @@ def main() -> None:
 
             accuracy = extract_accuracy(log_data)
             percentage = accuracy * 100
-            print(f"Pass percentage: {percentage}%")
+            print(f"Pass percentage: {percentage:.2f}%")
 
-            # Generate and accumulate markdown summary
             try:
                 summary_md = generate_markdown_summary(
                     log_data, percentage, args.threshold
@@ -156,20 +176,16 @@ def main() -> None:
 
             if not check_threshold(percentage, args.threshold):
                 print(
-                    f"Error: Pass percentage {percentage}% is less than threshold"
-                    f" {args.threshold}%"
+                    f"Error: Pass percentage {percentage:.2f}% is less than"
+                    f" threshold {args.threshold:.2f}%"
                 )
                 all_passed = False
             else:
                 print("Pass percentage check passed.")
-        except ValueError as e:
-            print(f"Error extracting metrics: {e}")
-            all_passed = False
         except Exception as e:
             print(f"Error processing log file: {e}")
             all_passed = False
 
-    # Write consolidated summary if we have summaries
     if summaries:
         summary_path = os.path.join(eval_root, "eval_summary.md")
         try:
@@ -181,8 +197,8 @@ def main() -> None:
 
     if not all_passed:
         print(
-            "\nCI Failed: One or more evaluation strategies did not meet the threshold"
-            " or encountered an error."
+            "\nCI Failed: One or more evaluation strategies did not meet the"
+            " threshold or encountered an error."
         )
         sys.exit(1)
 
diff --git a/eval/datasets/core_v0_9_1.yaml b/eval/datasets/core_v0_9_1.yaml
new file mode 100644
index 0000000000..d2a89c97af
--- /dev/null
+++ b/eval/datasets/core_v0_9_1.yaml
@@ -0,0 +1,1185 @@
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diff --git a/eval/datasets/core_v1_0.yaml b/eval/datasets/core_v1_0.yaml
new file mode 100644
index 0000000000..a6d77fecea
--- /dev/null
+++ b/eval/datasets/core_v1_0.yaml
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diff --git a/eval/datasets/dataset_schema.json b/eval/datasets/dataset_schema.json
index 933bfef01f..b32973d283 100644
--- a/eval/datasets/dataset_schema.json
+++ b/eval/datasets/dataset_schema.json
@@ -1,45 +1,180 @@
 {
   "$schema": "http://json-schema.org/draft-07/schema#",
-  "description": "Schema for A2UI evaluation datasets.",
+  "title": "A2UI Evaluation Dataset Schema",
+  "description": "Schema for A2UI evaluation datasets and data points.",
   "type": "array",
   "items": {
     "type": "object",
     "properties": {
       "name": {
         "type": "string",
-        "description": "A unique identifier for this evaluation sample."
+        "description": "Unique identifier for this evaluation sample."
+      },
+      "dataset": {
+        "type": "string",
+        "description": "Name of the dataset this data point belongs to. If omitted, inferred from the YAML file name."
       },
       "description": {
         "type": "string",
-        "description": "A human-readable description of what this sample tests."
+        "description": "Human-readable explanation of what this sample tests."
+      },
+      "catalog": {
+        "type": "string",
+        "description": "Path to a custom catalog JSON file. Supports the '{version}' placeholder."
+      },
+      "messages": {
+        "type": "array",
+        "description": "Ordered chat completion messages preceding and including the final UI generation request.",
+        "items": {
+          "oneOf": [
+            {
+              "type": "object",
+              "title": "User Message",
+              "properties": {
+                "role": {
+                  "type": "string",
+                  "enum": ["user"]
+                },
+                "content": {
+                  "type": "string",
+                  "description": "Text content of the user message."
+                }
+              },
+              "required": ["role", "content"],
+              "additionalProperties": false
+            },
+            {
+              "type": "object",
+              "title": "Assistant Message",
+              "properties": {
+                "role": {
+                  "type": "string",
+                  "enum": ["assistant"]
+                },
+                "content": {
+                  "type": "string",
+                  "description": "Optional text response generated by the assistant."
+                },
+                "tool_calls": {
+                  "type": "array",
+                  "description": "List of tool calls made by the assistant in native Inspect AI ToolCall format.",
+                  "items": {
+                    "type": "object",
+                    "properties": {
+                      "id": {
+                        "type": "string",
+                        "description": "Unique identifier for the tool call."
+                      },
+                      "type": {
+                        "type": "string",
+                        "enum": ["function"],
+                        "default": "function"
+                      },
+                      "function": {
+                        "type": "string",
+                        "description": "Function name directly as a string."
+                      },
+                      "arguments": {
+                        "type": "object",
+                        "description": "JSON dictionary of function arguments."
+                      }
+                    },
+                    "required": ["id", "function", "arguments"],
+                    "additionalProperties": false
+                  }
+                }
+              },
+              "required": ["role"],
+              "additionalProperties": false
+            },
+            {
+              "type": "object",
+              "title": "Tool Response Message",
+              "properties": {
+                "role": {
+                  "type": "string",
+                  "enum": ["tool"]
+                },
+                "content": {
+                  "type": "string",
+                  "description": "Execution output returned by the tool."
+                },
+                "tool_call_id": {
+                  "type": "string",
+                  "description": "Identifier linking this tool response to its preceding assistant tool call."
+                },
+                "function": {
+                  "type": "string",
+                  "description": "Optional function name for the tool response."
+                }
+              },
+              "required": ["role", "content", "tool_call_id"],
+              "additionalProperties": false
+            },
+            {
+              "type": "object",
+              "title": "System Message",
+              "properties": {
+                "role": {
+                  "type": "string",
+                  "enum": ["system"]
+                },
+                "content": {
+                  "type": "string",
+                  "description": "System instruction prompt."
+                }
+              },
+              "required": ["role", "content"],
+              "additionalProperties": false
+            }
+          ]
+        }
       },
       "promptText": {
         "type": "string",
-        "description": "The user input prompt that is sent to the LLM."
+        "description": "Legacy single-turn user prompt (deprecated; automatically migrated to messages)."
       },
       "target": {
         "type": "string",
-        "description": "The expected outcome or grading criteria for the model's generated output. Used by the LLM-as-a-judge scorer. If omitted, defaults to the value of `description`."
+        "description": "Expected outcome and rubric criteria for LLM-as-a-judge scoring. If omitted, defaults to description."
       },
-      "catalog": {
+      "system_prompt": {
+        "type": "string",
+        "description": "Domain-specific system prompt unrelated to A2UI (e.g., application persona, business rules). Merged with the A2UI SDK system prompt during solver execution."
+      },
+      "protocol_role": {
         "type": "string",
-        "description": "Path to the catalog json file. Corresponds to the catalog file path loaded into the A2uiSchemaManager. If omitted, the default is loaded from eval/datasets/defaults.py. Supports `{version}` placeholder which dynamically resolves to the target version directory name (e.g. 'v0_9_1' or 'v1_0')."
+        "description": "A2UI protocol role instruction passed to the SDK prompt generator."
       },
       "role_description": {
         "type": "string",
-        "description": "Corresponds to the `role_description` parameter of `A2uiSchemaManager.generate_system_prompt()`. If omitted, the default is loaded from eval/datasets/defaults.py."
+        "description": "Legacy alias for protocol_role."
+      },
+      "generation_rules": {
+        "type": "string",
+        "description": "A2UI protocol workflow rules appended to the SDK workflow prompt."
       },
       "workflow_description": {
         "type": "string",
-        "description": "Corresponds to the `workflow_description` parameter of `A2uiSchemaManager.generate_system_prompt()`. If omitted, the default is loaded from eval/datasets/defaults.py."
+        "description": "Legacy alias for generation_rules."
       },
       "allowed_surface_ids": {
         "type": "array",
-        "items": {"type": "string"},
-        "description": "An optional list of allowed surface IDs. The first item is used as the default fallback."
+        "items": {
+          "type": "string"
+        },
+        "description": "Allowed surface IDs for the output."
       }
     },
-    "required": ["name", "description", "promptText"],
+    "required": ["name", "description", "catalog"],
+    "anyOf": [
+      {
+        "required": ["messages"]
+      },
+      {
+        "required": ["promptText"]
+      }
+    ],
     "additionalProperties": false
   }
 }
diff --git a/eval/datasets/multi_turn_conversation_dataset.yaml b/eval/datasets/multi_turn_conversation_dataset.yaml
new file mode 100644
index 0000000000..8feeba25ae
--- /dev/null
+++ b/eval/datasets/multi_turn_conversation_dataset.yaml
@@ -0,0 +1,404 @@
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diff --git a/eval/datasets/prompts_v0_9_1.yaml b/eval/datasets/prompts_v0_9_1.yaml
deleted file mode 100644
index 264461aab2..0000000000
--- a/eval/datasets/prompts_v0_9_1.yaml
+++ /dev/null
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diff --git a/eval/datasets/prompts_v1_0.yaml b/eval/datasets/prompts_v1_0.yaml
deleted file mode 100644
index 29126ceb74..0000000000
--- a/eval/datasets/prompts_v1_0.yaml
+++ /dev/null
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diff --git a/eval/main.py b/eval/main.py
index d2a6b4fa19..b97c2e50fc 100644
--- a/eval/main.py
+++ b/eval/main.py
@@ -14,7 +14,6 @@
 
 import os
 import sys
-import traceback
 import argparse
 from inspect_ai import eval_set
 from tasks import a2ui_v0_9_1_eval, a2ui_v1_0_eval
@@ -34,6 +33,20 @@ def main() -> None:
         action="store_true",
         help="Run a quick sanity check (2 samples, gemini-3.1-flash-lite, 0 retry)",
     )
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        default=None,
+        help=(
+            "Evaluate only a specific dataset (e.g. 'customer_a_data' or 'core_v0_9_1')"
+        ),
+    )
+    parser.add_argument(
+        "--datasets",
+        type=str,
+        default=None,
+        help="Comma-separated list of datasets to evaluate",
+    )
     parser.add_argument(
         "--model",
         type=str,
@@ -101,6 +114,11 @@ def main() -> None:
     retry_attempts = 0 if args.sanity else args.max_retries
     sample_shuffle = None if args.sanity else args.sample_shuffle
 
+    # Resolve dataset filters
+    selected_dataset = args.dataset
+    if args.datasets:
+        selected_dataset = args.datasets
+
     # Parse and validate strategies
     selected_strategies = []
     raw_strategies = args.strategies if args.strategies else ["direct", "subagent_tool"]
@@ -122,7 +140,11 @@ def main() -> None:
             if strat in ["express", "elemental", "atom"]
             else a2ui_v0_9_1_eval
         )
-        task_obj = task_func(strategy=strat, grading_model=args.grading_model)
+        task_obj = task_func(
+            strategy=strat,
+            grading_model=args.grading_model,
+            dataset=selected_dataset,
+        )
         if args.prompt:
             prompt_list = [p.lower() for p in args.prompt]
             filtered = [
@@ -146,6 +168,7 @@ def main() -> None:
         "retry_attempts": retry_attempts,
         "limit": limit,
         "sample_shuffle": sample_shuffle,
+        "log_dir_allow_dirty": True,
     }
     model_args = {}
     if args.thinking_budget is not None:
diff --git a/eval/tasks.py b/eval/tasks.py
index 8c9fe0bf95..ceda1a1252 100644
--- a/eval/tasks.py
+++ b/eval/tasks.py
@@ -16,22 +16,16 @@
 
 # pylint: disable=duplicate-code
 
-import sys
-from pathlib import Path
 from google import genai
 from google.genai import errors
-from inspect_ai import task, Task
+from inspect_ai import Task, task
 from inspect_ai.dataset import MemoryDataset, Sample
-from inspect_ai.scorer import scorer, Score, Scorer, Target
+from inspect_ai.scorer import Score, Scorer, Target, scorer
 from inspect_ai.solver import TaskState
+
 from a2ui_eval.dataset import load_a2ui_dataset
-from a2ui_eval.strategies import get_solver
 from a2ui_eval.scorers import a2ui_scorer, measured_model_graded_qa
-
-# Paths relative to the eval directory where we run inspect
-CURRENT_DIR = Path(__file__).resolve().parent
-DATASET_V0_9_1_PATH = (CURRENT_DIR / "datasets/prompts_v0_9_1.yaml").resolve()
-DATASET_V1_0_PATH = (CURRENT_DIR / "datasets/prompts_v1_0.yaml").resolve()
+from a2ui_eval.strategies import get_solver
 
 
 @scorer(metrics=[])
@@ -65,6 +59,7 @@ def a2ui_v0_9_1_eval(
     list_models: bool = False,
     grading_model: str = "google/gemini-3.5-flash",
     strategy: str = "direct",
+    dataset: str | list[str] | None = None,
 ) -> Task:
     """Evaluation task for A2UI v0.9.1 protocol generation.
 
@@ -72,6 +67,7 @@ def a2ui_v0_9_1_eval(
         list_models: Whether to list available Gemini models and exit.
         grading_model: The model to use for LLM-as-a-judge grading.
         strategy: The evaluation strategy to use (e.g., 'direct').
+        dataset: Specific dataset name or list of dataset names.
 
     Returns:
         An Inspect Task object configured for A2UI v0.9.1 evaluation.
@@ -92,21 +88,19 @@ def a2ui_v0_9_1_eval(
             scorer=[dummy_scorer()],
         )
 
-    active_dataset_path = DATASET_V0_9_1_PATH
     active_version = "0.9.1"
     default_catalog_path = "specification/v0_9_1/catalogs/basic/catalog.json"
-
     format_name = strategy if strategy in ["express", "elemental", "atom"] else "json"
 
-    dataset = load_a2ui_dataset(
-        str(active_dataset_path),
+    dataset_obj = load_a2ui_dataset(
+        dataset=dataset,
         default_catalog_path=default_catalog_path,
         version=active_version,
         format_name=format_name,
     )
 
     return Task(
-        dataset=dataset,
+        dataset=dataset_obj,
         solver=get_solver(strategy, version=active_version),
         scorer=[
             a2ui_scorer(version=active_version),
@@ -122,6 +116,7 @@ def a2ui_v1_0_eval(
     list_models: bool = False,
     grading_model: str = "google/gemini-3.5-flash",
     strategy: str = "express",
+    dataset: str | list[str] | None = None,
 ) -> Task:
     """Evaluation task for A2UI v1.0 protocol generation.
 
@@ -129,6 +124,7 @@ def a2ui_v1_0_eval(
         list_models: Whether to list available Gemini models and exit.
         grading_model: The model to use for LLM-as-a-judge grading.
         strategy: The evaluation strategy to use (e.g., 'express').
+        dataset: Specific dataset name or list of dataset names.
 
     Returns:
         An Inspect Task object configured for A2UI v1.0 evaluation.
@@ -149,21 +145,19 @@ def a2ui_v1_0_eval(
             scorer=[dummy_scorer()],
         )
 
-    active_dataset_path = DATASET_V1_0_PATH
     active_version = "1.0"
     default_catalog_path = "specification/v1_0/catalogs/basic/catalog.json"
-
     format_name = strategy if strategy in ["express", "elemental", "atom"] else "json"
 
-    dataset = load_a2ui_dataset(
-        str(active_dataset_path),
+    dataset_obj = load_a2ui_dataset(
+        dataset=dataset,
         default_catalog_path=default_catalog_path,
         version=active_version,
         format_name=format_name,
     )
 
     return Task(
-        dataset=dataset,
+        dataset=dataset_obj,
         solver=get_solver(strategy, version=active_version),
         scorer=[
             a2ui_scorer(version=active_version),
diff --git a/eval/tests/test_dataset.py b/eval/tests/test_dataset.py
index 790a6bb989..b2a00d23b3 100644
--- a/eval/tests/test_dataset.py
+++ b/eval/tests/test_dataset.py
@@ -12,27 +12,33 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
+"""Unit tests for dataset loading and tool call parsing."""
+
 from pathlib import Path
 import pytest
+from inspect_ai.model import ChatMessageAssistant, ChatMessageTool, ChatMessageUser
 from a2ui_eval.dataset import load_a2ui_dataset
 
 
 def test_load_a2ui_dataset(tmp_path: Path) -> None:
-    # Create a dummy YAML file
     d = tmp_path / "sub"
     d.mkdir()
     p = d / "dummy_prompts.yaml"
     p.write_text("""
 - name: testPrompt
   description: A test prompt.
-  promptText: "Test input"
+  catalog: "specification/{version}/catalogs/basic/catalog.json"
+  messages:
+    - role: user
+      content: "Test input"
 """)
 
-    dataset = load_a2ui_dataset(str(p))
+    dataset = load_a2ui_dataset(file_path=str(p))
 
     assert len(dataset) == 1
-    assert dataset[0].input == "Test input"
+    assert len(dataset[0].input) == 1
+    assert isinstance(dataset[0].input[0], ChatMessageUser)
+    assert dataset[0].input[0].content == "Test input"
     assert dataset[0].target == "A test prompt."
     assert dataset[0].metadata is not None
     assert dataset[0].metadata["name"] == "testPrompt"
@@ -40,7 +46,7 @@ def test_load_a2ui_dataset(tmp_path: Path) -> None:
 
 def test_load_a2ui_dataset_file_not_found() -> None:
     with pytest.raises(FileNotFoundError):
-        load_a2ui_dataset("non_existent_file.yaml")
+        load_a2ui_dataset(file_path="non_existent_file.yaml")
 
 
 def test_load_a2ui_dataset_with_version(tmp_path: Path) -> None:
@@ -50,12 +56,65 @@ def test_load_a2ui_dataset_with_version(tmp_path: Path) -> None:
     p.write_text("""
 - name: testPrompt
   description: A test prompt.
-  promptText: "Test input"
   catalog: "path/{version}/catalog.json"
+  messages:
+    - role: user
+      content: "Test input"
 """)
 
-    dataset = load_a2ui_dataset(str(p), version="0.9.1")
+    dataset = load_a2ui_dataset(file_path=str(p), version="0.9.1")
 
     assert len(dataset) == 1
     assert dataset[0].metadata is not None
     assert dataset[0].metadata["catalog"] == "path/v0_9_1/catalog.json"
+
+
+def test_load_a2ui_dataset_multi_turn(tmp_path: Path) -> None:
+    d = tmp_path / "sub"
+    d.mkdir()
+    p = d / "dummy_multi_turn.yaml"
+    p.write_text("""
+- name: multiTurnPrompt
+  dataset: customer_data
+  description: A multi turn prompt with tool calls.
+  catalog: "specification/v0_9_1/catalogs/basic/catalog.json"
+  system_prompt: "Domain prompt"
+  messages:
+    - role: user
+      content: "Lookup account 123"
+    - role: assistant
+      content: "Checking account"
+      tool_calls:
+        - id: call_1
+          type: function
+          function: lookup
+          arguments:
+            id: "123"
+    - role: tool
+      tool_call_id: call_1
+      function: lookup
+      content: '{"balance": 500}'
+    - role: user
+      content: "Render my balance card"
+""")
+
+    dataset = load_a2ui_dataset(file_path=str(p), dataset="customer_data")
+    assert len(dataset) == 1
+    assert len(dataset[0].input) == 4
+    assert isinstance(dataset[0].input[0], ChatMessageUser)
+
+    assistant = dataset[0].input[1]
+    assert isinstance(assistant, ChatMessageAssistant)
+    assert assistant.tool_calls is not None
+    assert len(assistant.tool_calls) == 1
+    assert assistant.tool_calls[0].id == "call_1"
+    assert assistant.tool_calls[0].function == "lookup"
+    assert assistant.tool_calls[0].arguments == {"id": "123"}
+
+    tool_msg = dataset[0].input[2]
+    assert isinstance(tool_msg, ChatMessageTool)
+    assert tool_msg.tool_call_id == "call_1"
+    assert tool_msg.function == "lookup"
+
+    assert dataset[0].metadata is not None
+    assert dataset[0].metadata["system_prompt"] == "Domain prompt"
diff --git a/specification/proposals/eval_suite_expansion.md b/specification/proposals/eval_suite_expansion.md
new file mode 100644
index 0000000000..1112ee2580
--- /dev/null
+++ b/specification/proposals/eval_suite_expansion.md
@@ -0,0 +1,566 @@
+# Evaluation dataset expansion and agentic data points design
+
+_Status: Draft_
+
+_Author: A2UI Team_
+
+_Created: 2026-07-23_
+
+## Background
+
+The A2UI evaluation framework located in [eval/](../../eval) tests whether Large Language Models generate valid and contextually appropriate A2UI JSON payloads. The current framework runs Inspect AI tasks ([eval/tasks.py](../../eval/tasks.py)) against prompt datasets stored in YAML format. Datasets are encrypted at rest using Transcrypt to prevent data contamination from web crawlers.
+
+While the existing system tests basic generation well, real-world agentic applications present scenarios that the current setup cannot evaluate:
+
+1. **Monolithic dataset files**: All evaluation samples reside in monolithic YAML files. There is no mechanism to isolate data points into separate modular datasets (such as `core_v0_9_1`, `customer_a_data`, or `banking_app`), execute only a specific dataset, or view separate reporting tables per dataset.
+2. **Disconnected single-string prompts**: The existing schema ([eval/datasets/dataset_schema.json](../../eval/datasets/dataset_schema.json)) uses a standalone `promptText` string. Real agentic applications pass a structured `messages` list containing prior user queries, assistant responses, and tool calls with execution outputs before the model generates UI.
+3. **Domain system prompts and custom catalogs**: In production applications, models receive domain-specific system prompts (such as customer support rules, flight booking domain logic, or medical guidelines) that are separate from A2UI protocol instructions. Applications also define custom component catalogs with specialized UI elements. The current eval engine only injects A2UI protocol instructions without merging custom domain system prompts or supporting multi-turn chat history.
+4. **Contributor onboarding**: Contributors and external teams creating new data points need a clear, step-by-step workflow covering decryption, schema verification, local evaluation execution, and safe encrypted commits.
+
+This proposal outlines the technical design for expanding the evaluation framework to support separate modular datasets, standard LLM chat completion message formats (`messages`), custom catalogs with merged domain system prompts, and a contributor guide. All existing data points are cleanly migrated into modular dataset files under `eval/datasets/`, eliminating legacy monolithic files and obsolete field aliases.
+
+---
+
+## Architecture overview
+
+The expanded evaluation pipeline adds dataset discovery, standard LLM `messages` array parsing, system prompt merging, and dataset-aware reporting to Inspect AI.
+
+```mermaid
+flowchart TD
+    subgraph Data Layer [Encrypted YAML Datasets]
+        CoreDataset["Core v0.9.1 Dataset<br>(core_v0_9_1.yaml)"]
+        CustDataset["Customer A Dataset<br>(customer_a_data.yaml)"]
+        CustomDatasetN["Custom Datasets<br>(*.yaml)"]
+    end
+
+    subgraph Loader [Dataset Loader & Schema Validator]
+        Validator["JSON Schema Validator<br>(dataset_schema.json)"]
+        DatasetResolver["Dataset Resolver<br>(filter by --dataset / --datasets)"]
+    end
+
+    subgraph Engine [Inspect AI Evaluation Engine]
+        PromptMerger["System Prompt Merger<br>(Domain Prompt + A2UI SDK Schema/Rules)"]
+        MessageBuilder["Chat Messages Builder<br>(User + Assistant + Tool Calls in messages)"]
+        Inference["Model Inference<br>(Final UI Generation Turn)"]
+    end
+
+    subgraph Scoring [Evaluation & Scoring]
+        AlgScorer["a2ui_scorer<br>(Catalog Schema & Tree Integrity)"]
+        JudgeScorer["measured_model_graded_qa<br>(LLM Judge on Target Criteria)"]
+    end
+
+    subgraph Reporting [Dataset-Aware Reporting]
+        ConsoleSummary["CLI Output Table<br>(Grouped by Dataset)"]
+        MarkdownReport["eval_summary.md<br>(Per-Dataset Breakdown)"]
+    end
+
+    CoreDataset --> Validator
+    CustDataset --> Validator
+    CustomDatasetN --> Validator
+    Validator --> DatasetResolver
+    DatasetResolver --> PromptMerger
+    DatasetResolver --> MessageBuilder
+    PromptMerger --> Inference
+    MessageBuilder --> Inference
+    Inference --> AlgScorer
+    Inference --> JudgeScorer
+    AlgScorer --> ConsoleSummary
+    JudgeScorer --> ConsoleSummary
+    ConsoleSummary --> MarkdownReport
+```
+
+---
+
+## 1. Separate datasets of data points
+
+### Directory organization
+
+Dataset files will be organized under [eval/datasets/](../../eval/datasets) as separate modular YAML files. The legacy monolithic files (`prompts_v0_9_1.yaml` and `prompts_v1_0.yaml`) are migrated directly into named dataset files:
+
+```
+eval/datasets/
+├── .gitattributes                  # Transcrypt filter rules
+├── dataset_schema.json             # Updated JSON schema for data points
+├── defaults.py                     # Default prompt templates and catalog paths
+├── core_v0_9_1.yaml                # Migrated core dataset for v0.9.1 protocol
+├── core_v1_0.yaml                  # Migrated core dataset for v1.0 protocol
+├── customer_a_data.yaml            # Customer A specific agentic scenarios
+└── flight_booking.yaml             # Domain-specific multi-turn dataset
+```
+
+Each YAML file in `eval/datasets/` represents a distinct evaluation dataset named after its filename (e.g. `customer_a_data.yaml` defines the `customer_a_data` dataset, and `core_v0_9_1.yaml` defines the `core_v0_9_1` dataset). Alternatively, a sample can specify an explicit `dataset` property in its metadata.
+
+### Dataset resolution and CLI execution
+
+The CLI runner in [eval/main.py](../../eval/main.py) and the CI runner in [eval/bin/run_ci_evals.py](../../eval/bin/run_ci_evals.py) will support filtering by dataset:
+
+```bash
+# Run all datasets (default)
+uv run main.py
+
+# Run only a specific dataset
+uv run main.py --dataset customer_a_data
+
+# Run multiple specific datasets
+uv run main.py --datasets customer_a_data,core_v0_9_1
+
+# Run a quick sanity check on a specific dataset
+uv run main.py --dataset customer_a_data --sanity
+```
+
+#### CLI argument specifications
+
+- `--dataset <name>`: Evaluates only samples belonging to the named dataset.
+- `--datasets <name1,name2>`: Evaluates samples belonging to any of the listed datasets (comma-separated).
+- When no dataset flag is supplied, the framework defaults to evaluating all active datasets or the default core datasets.
+
+### Dataset-aware reporting and summary tables
+
+The evaluation reporting tools in [eval/bin/report_evals.py](../../eval/bin/report_evals.py) will group and calculate metrics per dataset:
+
+#### Console output format
+
+```text
+=== Evaluation Results Summary ===
+
+--- Dataset: customer_a_data (Pass Rate: 100.00%) ---
+customer_a_flight_select  | Algorithmic: PASS | Judging: C  | Inference Time: 3.20s
+customer_a_seat_picker    | Algorithmic: PASS | Judging: C  | Inference Time: 2.80s
+
+--- Dataset: core_v0_9_1 (Pass Rate: 90.00%) ---
+contact_form              | Algorithmic: PASS | Judging: C  | Inference Time: 1.90s
+data_binding_list         | Algorithmic: PASS | Judging: P  | Inference Time: 2.10s
+  [Judging Failure Reason (Grade P)]:
+    Minor cosmetic variation in column label.
+
+==================================
+Dataset Summary:
+  customer_a_data : 2/2 passed (100.00%)
+  core_v0_9_1     : 9/10 passed (90.00%)
+Overall Pass Rate: 91.67% (Threshold: 90.00%)
+Inference Time - Average: 2.50s | Median: 2.45s
+==================================
+```
+
+#### Markdown summary format (`eval_summary.md`)
+
+The generated markdown summary file will include a top-level dataset summary table followed by detailed per-dataset breakdowns:
+
+```markdown
+### Evaluation Summary: a2ui_v0_9_1_eval
+
+- **Status**: PASS
+- **Model**: `google/gemini-3.5-flash`
+- **Overall Pass Percentage**: `91.67%` (Threshold: `90.00%`)
+
+#### Dataset Performance Summary
+
+| Dataset Name      | Total Samples | Algorithmic Pass Rate | Judging Pass Rate | Dataset Pass Rate |
+| :---------------- | :-----------: | :-------------------: | :---------------: | :---------------: |
+| `customer_a_data` |       2       |        100.0%         |      100.0%       |      100.0%       |
+| `core_v0_9_1`     |      10       |        100.0%         |       90.0%       |       90.0%       |
+
+#### Detailed Dataset Results: customer_a_data
+
+| Sample / Task            | Algorithmic | Judging | Inference Time | Status |
+| :----------------------- | :---------: | :-----: | :------------: | :----: |
+| customer_a_flight_select |    PASS     |    C    |     3.20s      |  PASS  |
+| customer_a_seat_picker   |    PASS     |    C    |     2.80s      |  PASS  |
+```
+
+---
+
+## 2. Standard LLM chat schema and field reference guide
+
+To align the evaluation format with industry-standard LLM chat completion APIs (such as OpenAI Chat Completions, Anthropic Messages API, and Inspect AI's internal `state.messages`), conversational turns and user prompts are consolidated under the familiar **`messages`** array. Every data point explicitly declares its required **`catalog`** path.
+
+### Standalone field reference
+
+| Field Name                | Type                     | Required? | Clear Explanation & Purpose                                                                                                                                                                                                                                                                                           |
+| :------------------------ | :----------------------- | :-------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **`name`**                | string                   | **Yes**   | Unique identifier for this evaluation sample (e.g. `customer_a_login_form`). Used in logs and result tables.                                                                                                                                                                                                          |
+| **`description`**         | string                   | **Yes**   | Human-readable explanation of what this test case verifies.                                                                                                                                                                                                                                                           |
+| **`catalog`**             | string                   | **Yes**   | **Path to the component catalog JSON file** defining available UI widgets and validation functions. Supports the `{version}` placeholder (e.g. `specification/{version}/catalogs/basic/catalog.json`). Required on every data point so solvers and scorers explicitly know which component schema and rules apply.    |
+| **`messages`**            | array of message objects | **Yes**   | **The conversation messages.** An ordered list of chat turns preceding and including the final UI request. Each item specifies a `role` (`user`, `assistant`, `system`, or `tool`), `content`, and optional `tool_calls` or `tool_call_id`.                                                                           |
+| **`target`**              | string                   | No        | **The primary grading criteria and expected UI outcome.** Used by the LLM-as-a-judge (`measured_model_graded_qa`) to evaluate whether the generated output meets requirements. If omitted, defaults to the value of `description`.                                                                                    |
+| **`dataset`**             | string                   | No        | The name of the dataset module this sample belongs to. If omitted, the loader infers it from the YAML filename.                                                                                                                                                                                                       |
+| **`system_prompt`**       | string                   | No        | **Domain-specific system instructions** representing the host application (e.g., application persona, customer service rules, or API constraints). The eval engine merges this domain prompt with the A2UI protocol instructions.                                                                                     |
+| **`protocol_role`**       | string                   | No        | **The A2UI persona instruction passed to the SDK prompt generator.** Specifies the high-level role for UI generation (Default: _"You are an AI assistant. Based on the following request, generate a stream of JSON messages that conform to the provided JSON Schemas."_). Most contributors can leave this omitted. |
+| **`generation_rules`**    | string                   | No        | **Technical protocol constraints appended to the SDK workflow prompt.** Defines formatting rules such as required surface ID naming, component root ID constraints, or adjacency list rules. Most contributors can leave this omitted to use standard protocol defaults.                                              |
+| **`allowed_surface_ids`** | array of strings         | No        | List of valid surface identifiers the model is allowed to target (Default: `["main"]`).                                                                                                                                                                                                                               |
+
+---
+
+### Detailed breakdown of key concepts
+
+#### 1. Why `catalog` is required
+
+Every A2UI evaluation requires a component catalog. Making `catalog` mandatory removes ambiguity for contributors, solver prompt generators, and validators. A contributor always explicitly declares the catalog path (such as `specification/{version}/catalogs/basic/catalog.json` for standard protocol widgets or a domain catalog path like `specification/{version}/catalogs/customer_a/catalog.json`).
+
+#### 2. Why `messages` is the preferred terminology
+
+Across the LLM ecosystem (OpenAI, Anthropic, LiteLLM, Ollama, and Inspect AI), `messages` is the universal term for chat interaction sequences:
+
+- **Direct framework alignment**: Inspect AI stores conversation turns in `state.messages`. Using `messages` in our YAML schema creates a 1:1 conceptual mapping.
+- **Avoids ambiguity**: The word "context" is overloaded in AI (often referring to RAG retrieval chunks, context windows, or A2UI action event contexts like `action.event.context`). `messages` unambiguously refers to the chat history.
+- **Consistent single-turn and multi-turn authoring**:
+  - **Single-turn prompt**: `messages` contains a single user turn (`role: user`, `content: "Create a login form"`).
+  - **Multi-turn conversation**: `messages` contains prior user, assistant, and tool turns, ending with the user turn that prompts the UI response.
+
+#### 3. `target` vs `description`
+
+- **`description`** is a high-level summary of what the test case does, used for human documentation and readability in test manifests.
+- **`target`** is the detailed rubric evaluated by the grading model. It specifies the exact expectations for the UI, such as required component types (e.g. "Must contain a Card with a TextField and a primary Button"), data bindings (e.g. "TextField must bind to `/auth/email`"), and interactive actions.
+
+#### 4. `system_prompt` vs `protocol_role`
+
+- **`system_prompt`** represents the **domain application persona**. In a real application, an agent has domain rules unrelated to A2UI (for example: _"You are the Acme Airlines Agent. Always confirm passenger baggage before checkout."_).
+- **`protocol_role`** represents the **A2UI protocol prompt**. This is the standard SDK instruction telling the LLM that it must output structured JSON conforming to the A2UI schema.
+- The eval engine merges both: domain instructions are presented first, followed by protocol instructions.
+
+#### 5. `generation_rules`
+
+- This field controls the technical A2UI rules injected into the prompt's `## Workflow Description:` section.
+- Standard protocol rules (e.g. _"Construct the UI on surfaceId 'main'"_ and _"Ensure there is one root component with id 'root'"_) are injected by default.
+- If a test case needs custom protocol behavior (for example, rendering to a surface named `'sidebar'`), the contributor specifies those extra rules in `generation_rules`.
+
+---
+
+### JSON schema specification
+
+The schema in [eval/datasets/dataset_schema.json](../../eval/datasets/dataset_schema.json) validates the clean dataset structure with `catalog` required:
+
+```json
+{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "title": "A2UI Evaluation Dataset Schema",
+  "description": "Schema for A2UI evaluation datasets and data points.",
+  "type": "array",
+  "items": {
+    "type": "object",
+    "properties": {
+      "name": {
+        "type": "string",
+        "description": "Unique identifier for this evaluation sample."
+      },
+      "dataset": {
+        "type": "string",
+        "description": "Name of the dataset this data point belongs to. If omitted, inferred from the YAML file name."
+      },
+      "description": {
+        "type": "string",
+        "description": "Human-readable explanation of what this sample tests."
+      },
+      "catalog": {
+        "type": "string",
+        "description": "Path to a custom catalog JSON file. Supports the '{version}' placeholder."
+      },
+      "messages": {
+        "type": "array",
+        "description": "Ordered chat completion messages preceding and including the final UI generation request.",
+        "items": {
+          "type": "object",
+          "properties": {
+            "role": {
+              "type": "string",
+              "enum": ["user", "assistant", "system", "tool"],
+              "description": "The role of the message sender."
+            },
+            "content": {
+              "type": "string",
+              "description": "Text content of the message."
+            },
+            "tool_calls": {
+              "type": "array",
+              "description": "List of tool calls made by the assistant in this turn.",
+              "items": {
+                "type": "object",
+                "properties": {
+                  "id": {"type": "string"},
+                  "type": {"type": "string", "enum": ["function"]},
+                  "function": {
+                    "type": "object",
+                    "properties": {
+                      "name": {"type": "string"},
+                      "arguments": {"type": "string"}
+                    },
+                    "required": ["name", "arguments"]
+                  }
+                },
+                "required": ["id", "function"]
+              }
+            },
+            "tool_call_id": {
+              "type": "string",
+              "description": "Identifier linking a tool response turn to its preceding assistant tool call."
+            }
+          },
+          "required": ["role"]
+        }
+      },
+      "target": {
+        "type": "string",
+        "description": "Expected outcome and rubric criteria for LLM-as-a-judge scoring. If omitted, defaults to description."
+      },
+      "system_prompt": {
+        "type": "string",
+        "description": "Domain-specific system prompt unrelated to A2UI (e.g., application persona, business rules). Merged with the A2UI SDK system prompt during solver execution."
+      },
+      "protocol_role": {
+        "type": "string",
+        "description": "A2UI protocol role instruction passed to the SDK prompt generator."
+      },
+      "generation_rules": {
+        "type": "string",
+        "description": "A2UI protocol workflow rules appended to the SDK workflow prompt."
+      },
+      "allowed_surface_ids": {
+        "type": "array",
+        "items": {"type": "string"},
+        "description": "Allowed surface IDs for the output."
+      }
+    },
+    "required": ["name", "description", "catalog", "messages"],
+    "additionalProperties": false
+  }
+}
+```
+
+### Example multi-turn agentic data point (YAML)
+
+Below is an example of an agentic data point representing a flight booking assistant that interacts via tools before rendering a custom A2UI interface:
+
+```yaml
+- name: customer_a_flight_confirmation
+  dataset: customer_a_data
+  description: Multi-turn flight booking agent renders confirmation card with custom catalog.
+  catalog: 'specification/{version}/catalogs/customer_a/catalog.json'
+  system_prompt: |
+    You are the Acme Airlines Booking Agent. Assist users with flight reservations.
+    Always confirm passenger details and baggage selections before finalizing bookings.
+  messages:
+    - role: user
+      content: 'I want to book flight AC-402 for Jane Doe.'
+    - role: assistant
+      content: 'Let me check the flight details and available seats for flight AC-402.'
+      tool_calls:
+        - id: call_flight_lookup_001
+          type: function
+          function:
+            name: lookup_flight
+            arguments: '{"flight_number": "AC-402"}'
+    - role: tool
+      tool_call_id: call_flight_lookup_001
+      content: '{"status": "available", "flight": "AC-402", "origin": "SFO", "dest": "JFK", "departure": "2026-08-15T08:00:00Z", "price": 350.00}'
+    - role: assistant
+      content: 'Flight AC-402 from SFO to JFK is available for $350.00. Would you like to select a seat and confirm?'
+    - role: user
+      content: 'Yes, please show me the booking confirmation UI with a button to confirm and an option to add baggage.'
+  target: |
+    The response must generate valid A2UI JSON containing:
+    1. A Card or Column root container.
+    2. Text components displaying the flight number (AC-402), route (SFO to JFK), and price ($350.00).
+    3. A CheckBox component bound to '/booking/addBaggage' or similar path.
+    4. A primary Button labeled 'Confirm Booking' with an event action.
+```
+
+### Example single-turn data point (YAML)
+
+Below is an example of a migrated single-turn core data point:
+
+```yaml
+- name: contact_form
+  dataset: core_v0_9_1
+  description: Renders a contact form with email and submit button.
+  catalog: 'specification/{version}/catalogs/basic/catalog.json'
+  messages:
+    - role: user
+      content: 'Create a contact form with an email text field and submit button.'
+  target: |
+    The response must contain a Card or Column with a TextField bound to '/contact/email' and a primary Button.
+```
+
+---
+
+## 3. Eval engine execution mechanics
+
+When executing an evaluation sample, the solver in [eval/a2ui_eval/strategies/format.py](../../eval/a2ui_eval/strategies/format.py) coordinates prompt merging, chat message list assembly, inference, and scoring.
+
+### Step 1: System prompt generation and merging
+
+The solver merges the domain-specific system prompt with the protocol instructions generated by the A2UI SDK:
+
+1. **SDK prompt generation**: The A2UI SDK `TransportPromptGenerator` loads the required `catalog` and generates protocol rules, JSON schemas, component catalog definitions, and output tag requirements (`<a2ui-json>`).
+2. **Domain prompt merging**: If the data point includes a `system_prompt`, the solver combines them into a unified system instruction:
+
+```python
+def merge_system_prompts(domain_prompt: str, a2ui_prompt: str) -> str:
+    """Merges domain-specific rules with A2UI protocol instructions."""
+    parts = []
+    if domain_prompt.strip():
+        parts.append(f"## Domain Instructions\n{domain_prompt.strip()}")
+    if a2ui_prompt.strip():
+        parts.append(f"## UI Protocol Instructions\n{a2ui_prompt.strip()}")
+    return "\n\n".join(parts)
+```
+
+3. The merged prompt is inserted as the first message (`ChatMessageSystem`) in Inspect AI's `state.messages`.
+
+### Step 2: Chat messages assembly
+
+The solver populates `state.messages` directly from the sample's `messages` array:
+
+- **User turns**: Converted to `ChatMessageUser(content=turn["content"])`.
+- **Assistant turns**: Converted to `ChatMessageAssistant(content=turn.get("content", ""), tool_calls=turn.get("tool_calls"))`.
+- **Tool turns**: Converted to `ChatMessageTool(content=turn["content"], tool_call_id=turn["tool_call_id"])`.
+
+The final turn in `messages` triggers the assistant's UI generation response.
+
+### Step 3: Model inference
+
+The solver invokes `measured_generate()`, passing the assembled message list to the model. The model generates its completion on the final turn.
+
+### Step 4: Multi-stage scoring
+
+1. **Programmatic validation ([a2ui_scorer](../../eval/a2ui_eval/scorers.py))**:
+   - Resolves the required `catalog` path specified in the data point.
+   - Parses JSON from `<a2ui-json>` tags.
+   - Runs the catalog validator ([A2uiValidator](../../agent_sdks/python/a2ui_agent/README.md)) to verify schema adherence, parent-child references, and root component presence.
+2. **LLM judge scoring ([measured_model_graded_qa](../../eval/a2ui_eval/scorers.py))**:
+   - Evaluates the generated UI against the `target` criteria using the grading model (e.g. `google/gemini-3.5-flash`).
+   - Produces a grade of `C` (Correct), `P` (Partial Credit), or `I` (Incorrect).
+
+---
+
+## 4. Contributor guide: checking in data points and encryption
+
+This step-by-step guide is for contributors adding or updating data points in the A2UI evaluation framework.
+
+### Why data points are encrypted
+
+Evaluation datasets are encrypted at rest in Git using **Transcrypt**. This prevents public search engines and automated LLM training web crawlers from indexing our test prompts and answers, avoiding artificial score inflation from model memorization.
+
+### Step 1: Initial decryption setup
+
+Before modifying or viewing datasets, initialize Transcrypt with the repository evaluation password:
+
+```bash
+cd eval
+bin/transcrypt -p <PASSWORD>
+```
+
+_(Request the password from an A2UI maintainer if you do not have it.)_
+
+Once decrypted, all YAML files in `eval/datasets/` appear as regular plaintext files on your local filesystem.
+
+### Step 2: Create or update a dataset file
+
+1. Create a new YAML file under `eval/datasets/<dataset_name>.yaml` (for example, `eval/datasets/customer_a_data.yaml`).
+2. Add your evaluation samples following the standard chat completion format with a required `catalog`:
+
+```yaml
+- name: customer_a_login_form
+  dataset: customer_a_data
+  description: Tests generation of a secure login card with email and password fields.
+  catalog: 'specification/{version}/catalogs/basic/catalog.json'
+  system_prompt: 'You are the Customer A Authentication Agent. Help users sign in.'
+  messages:
+    - role: user
+      content: 'Create a login form with email, password, and a submit button.'
+  target: |
+    The response must contain:
+    1. A Card or Column root component.
+    2. A TextField for email bound to '/auth/email'.
+    3. A TextField for password bound to '/auth/password'.
+    4. A primary Button labeled 'Sign In'.
+```
+
+### Step 3: Validate your dataset against the schema
+
+Run the automated dataset schema test before running evaluations:
+
+```bash
+uv run python -m pytest tests/test_dataset.py
+```
+
+You can also validate specific dataset files directly:
+
+```bash
+uv run python -m a2ui_eval.dataset --validate datasets/customer_a_data.yaml
+```
+
+If your YAML file violates the schema (e.g. missing required fields like `catalog` or unknown properties), the validator will output the exact line and error.
+
+### Step 4: Run a local evaluation test
+
+Test how your new data points perform against Gemini models:
+
+```bash
+# Set your API key
+export GEMINI_API_KEY="your_api_key"
+
+# Run a quick sanity check on your new dataset (2 samples, flash-lite)
+uv run main.py --dataset customer_a_data --sanity
+
+# Run the full evaluation on your dataset
+uv run main.py --dataset customer_a_data
+```
+
+### Step 5: Inspect results in the log viewer
+
+Start Inspect AI's interactive web viewer to examine model outputs, merged system prompts, and validator rationales:
+
+```bash
+uv run inspect view start
+```
+
+Open `http://localhost:7575` in your browser to view the trace of each sample.
+
+### Step 6: Commit and push changes
+
+Because `.gitattributes` configures Transcrypt filters for `*.yaml` files in the `eval/datasets/` directory, Git will automatically encrypt the files when staging:
+
+```bash
+git add eval/datasets/customer_a_data.yaml
+git commit -m "feat(eval): add customer_a_data evaluation dataset"
+git push origin your-branch
+```
+
+To verify that files are encrypted before pushing, run:
+
+```bash
+git diff --cached
+```
+
+If Transcrypt is active, the staged diff will show encrypted cipher text.
+
+---
+
+## 5. Migration path for existing data points
+
+The evaluation framework migrated legacy monolithic prompt files (`prompts_v0_9_1.yaml` and `prompts_v1_0.yaml`) into clean, modular dataset files (`core_v0_9_1.yaml` and `core_v1_0.yaml`) conforming to the new `messages` schema.
+
+### Migration strategy
+
+1. **Standardizing on `messages`**: Single-string `promptText` queries are converted into structured `messages` arrays with role-based conversation turns (`user`, `assistant`, `tool`, `system`).
+2. **Mandatory component catalogs**: Every sample specifies an explicit relative `catalog` path (e.g. `specification/{version}/catalogs/basic/catalog.json`).
+3. **Modular dataset separation**: Data points are partitioned into logical, manageable YAML files under `eval/datasets/` that are dynamically discovered by the dataset loader.
+4. **In-memory runtime fallback (loader safety)**: To prevent breakage if a developer branch or scratch file still contains legacy fields, the dataset loader in [eval/a2ui_eval/dataset.py](../../eval/a2ui_eval/dataset.py) includes a runtime normalizer that converts `promptText` to `messages` and maps legacy field aliases on the fly.
+
+---
+
+## Code changes roadmap
+
+| File Path                                                                        | Nature of Change | Purpose                                                                                                                                     |
+| :------------------------------------------------------------------------------- | :--------------- | :------------------------------------------------------------------------------------------------------------------------------------------ |
+| [eval/datasets/dataset_schema.json](../../eval/datasets/dataset_schema.json)     | Update           | Require `catalog`, use `messages` for chat turns, and include `dataset`, `system_prompt`, `protocol_role`, and `generation_rules` fields.   |
+| [eval/datasets/core_v0_9_1.yaml](../../eval/datasets)                            | Create           | Migrated v0.9.1 data points using the `messages` format and explicit `catalog`.                                                             |
+| [eval/datasets/core_v1_0.yaml](../../eval/datasets)                              | Create           | Migrated v1.0 data points using the `messages` format and explicit `catalog`.                                                               |
+| [eval/a2ui_eval/dataset.py](../../eval/a2ui_eval/dataset.py)                     | Update           | Add dataset discovery, `messages` array conversion to Inspect AI `ChatMessage` objects, required `catalog` enforcement, and validation CLI. |
+| [eval/a2ui_eval/strategies/format.py](../../eval/a2ui_eval/strategies/format.py) | Update           | Implement domain system prompt merging and `messages` chat history prepending.                                                              |
+| [eval/a2ui_eval/scorers.py](../../eval/a2ui_eval/scorers.py)                     | Update           | Use explicit `catalog` path in `a2ui_scorer`.                                                                                               |
+| [eval/tasks.py](../../eval/tasks.py)                                             | Update           | Point to migrated datasets and accept `dataset` and `datasets` filtering parameters.                                                        |
+| [eval/main.py](../../eval/main.py)                                               | Update           | Add `--dataset` and `--datasets` CLI flags.                                                                                                 |
+| [eval/bin/report_evals.py](../../eval/bin/report_evals.py)                       | Update           | Add per-dataset grouping in console summaries and markdown reports.                                                                         |
+| [eval/bin/run_ci_evals.py](../../eval/bin/run_ci_evals.py)                       | Update           | Pass dataset filtering arguments to `main.py`.                                                                                              |
+| [eval/tests/test_dataset.py](../../eval/tests/test_dataset.py)                   | Update           | Add unit tests for dataset loading, `messages` chat turn parsing, required `catalog` check, and schema validation.                          |
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