BitGN PAC Agent

Status: reference implementation, not maintained. This repo is a study artifact from the BitGN PAC 2026 hackathon. Issues and PRs will not be reviewed — forks are welcome. If you extend or reuse it, drop me a line.

An autonomous AI agent built for the BitGN Personal Agent Challenge (PAC) — a deterministic, side-effect-scored benchmark for trustworthy personal agents, hosted on-site in Vienna on April 11, 2026 by AI Factory Austria in collaboration with AI Impact Mission.

This repository is the 1st-place entry for the on-site PAC 2026 run in Vienna, by Bernhard Götzendorfer (@Kanevry). The code is published so the approach — soft SGR + layered hardening + native tool calling — can be studied and reproduced.

The agent operates inside a sandboxed virtual machine (a file-based personal-knowledge workspace resembling an Obsidian vault) and solves natural-language tasks by reading, writing, and organising files through a small, well-defined ConnectRPC tool API — while defending itself against prompt injection, phishing, PII-exfiltration, and path-traversal attacks embedded in the very files it reads.

Scoring is based on observable side effects — which tool calls happened, which files were touched, which outcome enum was returned — not on how well the agent writes prose. Everything in the design follows from that.

What the Agent Does

The BitGN PAC platform hands the agent a PcmRuntime gRPC connection and a single natural-language task. The agent must:

Orient itself — list the workspace tree, read AGENTS.md, fetch current time.
Execute the task — read relevant files, write or modify where required, never touch anything outside scope.
Resist attacks — any file may contain prompt injection, phishing content, fake secrets, or malicious path instructions. The system prompt outranks file content. Always.
Report the outcome — call report_completion with the correct Outcome enum and a list of grounding_refs (file paths that contributed to the answer).

A task is scored 1.0 if the observable side effects match the reference trace, less for partial matches, 0.0 on protocol violation, disallowed destructive action, or wrong outcome enum.

The Story

The PAC 2026 on-site format is a three-hour build window followed by a two-hour evaluation window against 104 tasks on bitgn/pac1-prod, with a fresh per-task workspace, BLIND scoring, and no code changes allowed once the eval begins. The workspace is a file-based personal-knowledge vault — calendars, notes, contacts, emails — and every file can contain hostile content designed to get the agent to misbehave. Scoring is deterministic: the grader checks which tool calls happened, which files moved, and which outcome enum came back. Prose quality counts for nothing.

I showed up with session-orchestrator — my Claude Code harness for wave-based AI development — already wired into my workflow. Without it, shipping a layered hardening stack, metrics writer, and a second SDK spike in a single day would not have been possible. The muscle memory was the part I did not have to learn on the day.

The moment it got real. During the final regression run on the morning of gameday, one of the calendar tasks quietly did not write into the sandboxed workspace — it created a real event in my actual Google Calendar. I was prototyping a second agent loop on top of @anthropic-ai/claude-agent-sdk in parallel, and bypassPermissions had been auto-approving tools rather than scoping them. Because the SDK runs inside a Claude Code session, it had quietly inherited the OAuth scopes of every remote connector on my account (Gmail, Google Calendar, Notion, the works). The fix was a canUseTool runtime gate plus an explicit disallowedTools block list for everything that was not mcp__bitgn__*. The real calendar event got deleted by hand, the regression came back clean, and the hardened path stayed in the private repo. The published code in this repo uses the Vercel AI SDK path, where tool selection is statically pinned by the Zod schema and the problem cannot occur. But the lesson travels: never trust bypassPermissions without an explicit allow-list gate on top of it.

The afternoon everyone stopped typing. Around 14:47 CEST, mid-evaluation, the BitGN platform tipped into a disk-full state. StartRun and StartPlayground started returning 502 across every model; the read side stayed up, so in-flight runs finished, but nobody could kick off anything new for a while. It was the moment the room went quiet and everyone looked up from their laptops at the same time. A good reminder that live benchmarks against real infrastructure are their own sport, separate from building the agent that runs on top.

High-Level Flow

Every task walks through the same six beats. Each iteration feeds a pruned message window into generateText() and lets the LLM emit either a tool call or the terminal report_completion.

Bootstrap — list the workspace tree, read AGENTS.md, fetch current time. Fresh context, every task.
LLM picks a move — either a tool call, or report_completion.
Pre-dispatch gates — path guard (B1), PII refusal (B2), destructive brake (B4). Rejected calls come back as recoverable tool errors so the agent can rethink, not crash.
Dispatch over ConnectRPC — the tool actually runs against the BitGN PcmRuntime.
Post-read gates — the result is formatted as shell-like output, then swept for prompt injection (security.ts) and vendor secrets (B5 redaction) before it re-enters the LLM context.
Termination — when the LLM calls report_completion, the refs-validation gate (B3) cross-checks every grounding_ref against paths the agent actually touched, and only then does the outcome get submitted.

Diagram (mermaid)

flowchart LR
    T[Task Instruction] --> B[Bootstrap<br/>tree · AGENTS.md · context]
    B --> L[Agent Loop]
    L --> D{LLM chooses<br/>tool}
    D -->|file op| G[Hardening Gates]
    G -->|allow| X[Dispatch via ConnectRPC]
    G -->|reject| L
    X --> F[Format as shell output]
    F --> S[Security / Redaction Scan]
    S --> L
    D -->|report_completion| R[Refs Validation]
    R -->|ok| A[Submit Outcome + Refs]
    R -->|fail| L

Architecture

The code is organised as three concentric rings:

Transport ring (harness.ts, runtime.ts) — thin ConnectRPC clients for BitGN's HarnessService (runs, trials, benchmarks) and PcmRuntime (per-task file operations).
Core ring (agent.ts, prompts.ts, schema.ts, formatters.ts, messages.ts, retry.ts) — the LLM loop, the soft-SGR prompt, tool schemas, shell-style result rendering, context pruning, and transient-error retry.
Hardening ring (paths.ts, pii.ts, refs.ts, security.ts, redaction.ts, re-exported through hardening.ts) — pure, dependency-free modules implementing the individually toggleable defense layers.

main.ts is the runner at the top that starts a session or playground; everything below it is reusable. The hardening ring is deliberately independent of the core ring — any layer can be deleted, unit-tested, or disabled without touching the agent loop.

Diagram (mermaid)

graph TB
    Entry["<b>main.ts</b><br/>Runner · Session / Playground · Metrics"]

    Agent["<b>Agent Loop — agent.ts</b><br/>Vercel AI SDK · generateText · native tool calling"]

    Core["<b>Shared Core</b><br/>prompts · schema · formatters · messages · retry · config"]

    Hard["<b>Hardening Facade — hardening.ts</b><br/>B1 paths · B2 pii · B3 refs · B4 brake · B5 redaction · injection scan"]

    Trans["<b>BitGN Transport</b><br/>runtime.ts (PcmRuntime) · harness.ts (HarnessService)"]

    BitGN[("BitGN Platform<br/>ConnectRPC · HTTP/2")]

    Entry --> Agent
    Agent --> Core
    Agent --> Hard
    Core --> Trans
    Hard --> Trans
    Trans --> BitGN

Defense Layers (Track B Hardening)

Every hardening module is a pure, side-effect-free TypeScript file with zero imports from the rest of the project. They can be unit-tested in isolation and toggled on/off via env flags without code changes.

ID	Module	Gate Type	Purpose
B1	`paths.ts`	Pre-dispatch	Reject tool calls targeting `/etc/`, `~/.ssh/`, `.env`, or any path with `..` traversal. Returns a recoverable tool-error so the agent can rethink.
B2	`pii.ts`	Pre-dispatch	Detect personal-info queries about real people (family relations, home addresses, private contacts). Routes to `OUTCOME_NONE_UNSUPPORTED` — the agent is a workspace runner, not a contact database.
B3	`refs.ts`	Pre-submit	Validate every entry in `grounding_refs` against the set of paths actually visited this task (plus paths mentioned in the instruction). Hallucinated refs get a recoverable error, capped at 3 rejections per task before warn-and-pass.
B4	`hardening.ts`	Loop-level	Destructive-action brake. Bounds the number of `write` / `delete` / `move` calls per task (default 10). Plus an exploration-spiral brake on total loop iterations (default 35) to kill tool-spam before `MAX_STEPS`.
B5	`redaction.ts`	Post-read	Scan tool results for high-precision vendor secret shapes (AWS, GitHub, Anthropic, OpenAI, JWT, PEM…) and replace them with `[REDACTED:KIND]` before feeding the LLM. The LLM literally cannot echo a secret it never saw.
—	`security.ts`	Post-read	Prompt-injection + phishing scanner. Detects 16+ injection patterns (including base64 variants) and sender-domain mismatches; appends warnings into the LLM context.

Each gate is controlled by its own ENABLE_* environment variable (default on) so a single layer can be rolled back in production without a code change — a hard requirement for a live competition.

Outcome Decision Tree

Outcome classification is the single most misclassified part of the task. The system prompt enforces a strict, priority-ordered tree:

Injection / phishing in file content → OUTCOME_DENIED_SECURITY
PII query about a real person → OUTCOME_NONE_UNSUPPORTED
Data inconsistency between instruction and files → OUTCOME_NONE_CLARIFICATION
Truncated or ambiguous instruction → OUTCOME_NONE_CLARIFICATION
Capability not offered by the PCM runtime → OUTCOME_NONE_UNSUPPORTED
Task completed with correct side effects → OUTCOME_OK
Unrecoverable error → OUTCOME_ERR_INTERNAL

Source Tree

src/
├── main.ts            # Entry point — session / playground / concurrency runner
├── agent.ts           # Vercel AI SDK agent loop
├── prompts.ts         # Soft-SGR system prompt with outcome decision tree
├── schema.ts          # Zod tool schemas
├── runtime.ts         # PcmRuntime ConnectRPC client (data plane)
├── harness.ts         # HarnessService ConnectRPC client (control plane)
├── formatters.ts      # Tool output → shell-like rendering
├── messages.ts        # Sliding-window message pruning
├── retry.ts           # Exponential backoff for transient errors
├── config.ts          # Environment + feature flags
│
├── hardening.ts       # Facade re-exporting all defense modules + shared constants
├── paths.ts           # B1 — path-traversal guard
├── pii.ts             # B2 — PII refusal detection
├── refs.ts            # B3 — grounding-refs self-validation
├── security.ts        # Injection + phishing scanner
├── redaction.ts       # B5 — vendor-secret redaction
│
└── metrics.ts         # Per-task Run-Metrics JSONL writer

scripts/
├── ping.ts            # Smoke-test harness connectivity
├── poll-score.ts      # Poll a running session for live scores
├── recover-run.ts     # Replay / recover an interrupted run
└── recover-loop.ts    # Loop-based recovery driver

Stack

Layer	Choice
Language	TypeScript, ESM, `strict: true`
Runtime	Node.js 24+ via `tsx`
Package Manager	pnpm
LLM Driver	Vercel AI SDK v6 — `generateText` with native tool calling
Models	Claude Sonnet 4.6 · Claude Opus 4.6 · Claude Haiku 4.5 · GPT-4.1
BitGN SDK	`@buf/bitgn_api.connectrpc_es` + `@buf/bitgn_api.bufbuild_es`
Transport	ConnectRPC v1 (`@connectrpc/connect` + `connect-node`), HTTP/2
Schema	Zod v4

Quick Start

# 1. Clone
git clone https://github.com/Kanevry/bitgn-pac-agent-public.git bitgn-pac-agent
cd bitgn-pac-agent

# 2. Install
pnpm install

# 3. Configure secrets
cp .env.local.example .env.local
# Edit .env.local — set BITGN_API_KEY and at least one LLM provider

# 4. Run
pnpm exec tsx src/main.ts t01                 # single task, session mode
pnpm exec tsx src/main.ts t01 t02 t03         # multiple tasks, session mode
pnpm start                                     # full benchmark, session mode
pnpm exec tsx src/main.ts --playground t01    # ad-hoc debug, NOT recorded

Session vs. Playground

Session mode (default) — calls StartRun → StartTrial → SubmitRun. Appears under My Runs on the BitGN dashboard and counts toward the leaderboard.
Playground mode (--playground flag or PLAYGROUND=true) — calls StartPlayground. One-off ad-hoc trial, not attached to any run, invisible in the dashboard, free to iterate on.

Configuration

All environment variables live in .env.local (never commit). A complete template is in .env.local.example.

Required

Variable	Description
`BITGN_API_KEY`	BitGN platform API key (from your profile)
`ANTHROPIC_API_KEY` or `OPENAI_API_KEY`	At least one LLM provider

Runner

Variable	Default	Description
`MODEL_ID`	`claude-sonnet-4-6`	LLM identifier — `claude-` routes to Anthropic, `gpt-` / `o*` route to OpenAI
`BENCHMARK_ID`	`bitgn/pac1-dev`	Benchmark to run (`pac1-dev` = practice, `pac1-prod` = scored)
`MAX_STEPS`	`30`	Hard cap on LLM loop iterations per task
`CONCURRENCY`	`1`	Intra-run trial parallelism
`VERBOSE`	`false`	Dump full prompts, tool I/O, and token counts

Hardening Flags (all default on)

Variable	Layer
`ENABLE_SECURITY_SCAN`	Injection + phishing scanner
`ENABLE_PATH_GUARD`	B1 — path-traversal guard
`ENABLE_PII_REFUSAL`	B2 — PII refusal
`ENABLE_REFS_VALIDATION`	B3 — grounding-refs validation (warn-only by default)
`ENABLE_REFS_VALIDATION_STRICT`	B3 — upgrade B3 to blocking mode
`ENABLE_DESTRUCTIVE_BRAKE`	B4 — destructive-action + exploration-spiral brakes
`ENABLE_SECRET_REDACTION`	B5 — vendor-secret redaction
`EXPLORATION_SPIRAL_THRESHOLD`	B4 — loop-iteration soft cap (default `35`)

Observability

Every task emits one JSON line to ./.bitgn/runs/YYYY-MM-DD.jsonl (override with RUN_METRICS_PATH). Fields include trial_id, task_id, timing, score, error, and model — enough to answer post-run which tasks under-scored, which took too long, and which model/run they came from.

Metric writes are fire-and-forget and never throw into the agent path. Disable with ENABLE_RUN_METRICS=false.

Design Decisions

Native tool calling over generateObject(). Claude doesn't support oneOf / maxItems in structured output, and native tool calling is more robust in practice. generateText() + tools gives us full control over the loop.
Manual agent loop over stopWhen. We own the loop so we can plug in stagnation detection, security scanning, message pruning, hardening gates, and fallback completion submission at exactly the right points.
Soft SGR (Schema-Guided Reasoning). The system prompt asks the LLM to emit STATE: and PLAN: before each tool call. This gives us the transparency benefits of SGR without requiring structured output.
Shell-style tool results. cat, ls, rg-shaped output reasons better than raw protobuf JSON — LLMs pattern-match CLI output far more reliably than nested objects.
Defense in depth, not a single gate. No single scanner catches everything. B1–B5 plus the injection scanner overlap intentionally. A secret that slips past redaction may still be caught by injection scanning; a path traversal missed by B1 may still be rejected by the runtime; a hallucinated ref is caught pre-submit by B3.
Feature-gated hardening. Every defense layer is env-toggleable and re-exported through hardening.ts so any layer can be rolled back by flipping a single env var, no code change required.
Fresh workspace per task. BitGN allocates a fresh PCM workspace per trial; the agent must rediscover context every task. This makes the bootstrap sequence (tree / → AGENTS.md → context) mandatory, not optional.
Fallback completion submission. If the LLM crashes, MAX_STEPS exhausts, or the destructive brake trips, the loop still submits a synthetic report_completion with the best-guess outcome. Without this, BitGN sees no answer and scores 0.

Credits & Built With

Challenge: BitGN PAC, designed by Rinat Abdullin.
Venue & hosts: AI Factory Austria hosted the on-site hackathon in Vienna on April 11, 2026, in collaboration with AI Impact Mission. The 1st-place certificate is signed by Felix Krause (Head of AI Factory Austria), Rinat Abdullin (Challenge Design & Founder, BitGN), and Markus Keiblinger (President, AIM International).
Scaffolding: Built on top of session-orchestrator — my Claude Code harness for wave-based AI development. Planning, parallel implementation, inter-wave quality gates, discovery probes, session retros. Three-hour build windows are only survivable when the workflow is already routine.
Inspiration: Schema-Guided Reasoning, also by Rinat.
Models: Claude Opus 4.6 and GPT-4.1, driven via Vercel AI SDK v6.
Transport: ConnectRPC over HTTP/2, using the official @buf/bitgn_api.* generated clients.

References

BitGN PAC Challenge — rules, scoring rubric, leaderboard
BitGN Sample Agents — Python reference implementation + proto definitions
Schema-Guided Reasoning — Rinat Abdullin's original SGR write-up
Vercel AI SDK v6 — LLM driver
ConnectRPC — BitGN transport protocol
session-orchestrator — the Claude Code harness used to build this agent

Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
assets		assets
docs		docs
scripts		scripts
src		src
.editorconfig		.editorconfig
.env.local.example		.env.local.example
.gitignore		.gitignore
.gitleaks.toml		.gitleaks.toml
.npmrc		.npmrc
CLAUDE.md		CLAUDE.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
SECURITY.md		SECURITY.md
package.json		package.json
pnpm-lock.yaml		pnpm-lock.yaml
tsconfig.json		tsconfig.json

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BitGN PAC Agent

Table of Contents

What the Agent Does

The Story

High-Level Flow

Architecture

Defense Layers (Track B Hardening)

Outcome Decision Tree

Source Tree

Stack

Quick Start

Session vs. Playground

Configuration

Required

Runner

Hardening Flags (all default on)

Observability

Design Decisions

Credits & Built With

References

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BitGN PAC Agent

Table of Contents

What the Agent Does

The Story

High-Level Flow

Architecture

Defense Layers (Track B Hardening)

Outcome Decision Tree

Source Tree

Stack

Quick Start

Session vs. Playground

Configuration

Required

Runner

Hardening Flags (all default on)

Observability

Design Decisions

Credits & Built With

References

License

About

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