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F035.4: Context Visibility — What the Model Actually Sees #249

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tfatykhov merged 1 commit into from
Apr 4, 2026
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F035.4: Context Visibility — What the Model Actually Sees #249

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8 changes: 6 additions & 2 deletions docs/features/F035-observability.md
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Expand Up @@ -32,7 +32,7 @@ Minsky's Chapter 6 ("Self-Knowledge is Dangerous") warns that unrestricted self-

---

## Architecture — Three Layers
## Architecture — Four Layers

```
┌─────────────────────────────────────────────────────────┐
Expand Down Expand Up @@ -62,6 +62,8 @@ Minsky's Chapter 6 ("Self-Knowledge is Dangerous") warns that unrestricted self-

**Layer 3 — Behavioral Drift Detection (F035.3):** Periodic snapshots of key metrics with trend analysis. Catches slow drift that individual events don't reveal. "Is the system changing in ways nobody intended?"

**Layer 4 — Context Visibility (F035.4):** Full transparency into what the LLM actually sees on every API call. Token breakdown by section, memory loading inventory, turn-over-turn diffs. "What did the model see when it made that choice?"

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## Sub-Specs
Expand All @@ -71,8 +73,9 @@ Minsky's Chapter 6 ("Self-Knowledge is Dangerous") warns that unrestricted self-
| F035.1 | Event Bus Observability | P1 | F006 |
| F035.2 | Autonomous Action Audit Trail | P1 | F035.1 |
| F035.3 | Behavioral Drift Detection | P2 | F035.2 |
| F035.4 | Context Visibility | P1 | F035 (umbrella) |

**Sequencing rationale:** F035.1 gives us the infrastructure (stats collection, endpoints). F035.2 adds causal metadata to events. F035.3 builds on both to detect trends. Each is independently useful.
**Sequencing rationale:** F035.1 gives us the infrastructure (stats collection, endpoints). F035.2 adds causal metadata to events. F035.3 builds on both to detect trends. F035.4 is independent — it hooks into the runner pipeline, not the event bus — and can be built in parallel with F035.1-3. Each sub-spec is independently useful.

---

Expand All @@ -89,3 +92,4 @@ Minsky's Chapter 6 ("Self-Knowledge is Dangerous") warns that unrestricted self-
3. Behavioral trends (fact growth rate, censor changes, check frequency drift) are tracked and anomalies flagged
4. The dashboard has an "Autonomous Activity" panel showing recent self-modifications
5. None of this adds measurable latency to the hot path (event processing)
6. For any API call, you can see the full token breakdown by context section and which memory items were loaded
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