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+---
+title: "49. Semantic observability and improvement loop"
+status: Accepted
+relates_to:
+  - operational-observability
+  - testing-agents
+topics:
+  - observability
+  - harness
+  - retro
+  - evaluation
+---
+
+# 49. Semantic observability and improvement loop
+
+Date: 2026-06-16
+
+## Status
+
+Accepted
+
+## Context
+
+[ADR 0021](0021-jsonl-reasoning-trace-exposure.md) gives fullsend per-run JSONL
+transcripts — the full prompt/completion/tool record. That is necessary for
+debugging and replay, but expensive to scan at factory scale and weak for
+trend questions: stuck agents, tool loops, phase drift, recurring failure modes.
+
+[operational-observability.md](../problems/operational-observability.md) calls
+for structured traces, human feedback, and replay; [testing-agents.md](../problems/testing-agents.md)
+calls for golden-set regression and behavioral monitoring. The retro agent
+([#131](https://github.com/fullsend-ai/fullsend/issues/131)) closes the loop
+after a workflow ends, but its input today is mostly raw JSONL.
+
+We took inspiration from an external long-running agent platform ([The Darwin
+Project](https://github.com/The-Darwin-Project/Blackboard)) that separates
+**raw LLM traces** from **semantic run signals**,
+**read-only observation**, **shadowed interventions**, and **structured lesson
+extraction**. This ADR adapts those ideas to fullsend's batch, sandbox-scoped
+execution model.
+
+OTel-compatible LLM observability platforms can already store tool calls,
+trace metadata, sessions, and custom scores. The gap is not another raw trace
+format — it is **derived signals**: fullsend-specific heuristics computed from
+stream-json that no generic backend infers on its own.
+
+## Options
+
+### Option A: Artifact-only enrichment
+
+Compute derived signals post-run and store them only in GHA artifacts alongside
+JSONL. No external observability backend.
+
+**Trade-offs:** Works offline and matches ADR 0021's artifact model. Poor for
+fleet-wide trends, cross-run queries, and cost dashboards at org scale.
+
+### Option B: External trace backend only
+
+Export JSONL as traces to an OTel-compatible LLM observability platform. Rely on
+that platform for tool spans, metadata, and scores. No local signal artifact.
+
+**Trade-offs:** Strong query and dashboard surface. Retro and offline debugging
+depend on backend availability and org credentials.
+
+### Option C: Hybrid (recommended)
+
+Export traces to an OTel-compatible backend as the primary query surface.
+Optionally mirror derived signals to run artifacts when the backend is
+unavailable. Same signal schema in both sinks — not two data models.
+
+**Trade-offs:** Adds integration and credential management. Preserves ADR 0021
+forensic fidelity and offline retro fallback.
+
+Backend vendor selection (e.g. Langfuse, Phoenix, self-hosted OTel collector) is
+deferred to a follow-on decision. See
+[operational-observability.md](../problems/operational-observability.md).
+
+## Decision
+
+Adopt option C. Introduce four layered capabilities on top of JSONL extraction:
+
+### 1. Derived signals
+
+A host-side enricher parses stream-json during and after each run and emits
+**derived signals** — compact, machine-readable indicators beyond raw
+prompt/completion pairs. Examples:
+
+- Tool and phase markers (largely redundant with trace spans; useful for
+  artifact-only consumers)
+- Fullsend-specific patterns: repeated tool use, validation retry loops,
+  defer/monitoring velocity, stuck-run heuristics
+
+Signals are attached to the run's trace record (metadata, child spans, or
+named scores) on the observability backend. An optional artifact mirror uses
+the same schema for offline retro when no backend is configured.
+
+JSONL remains the forensic source ([ADR 0021](0021-jsonl-reasoning-trace-exposure.md)).
+Derived signals are enrichments, not a replacement.
+
+### 2. Observer (read-first)
+
+A host-side **observer** analyzes a run or workflow using trace backend APIs
+and/or local JSONL and signals:
+
+- **v1:** Post-run, read-only analysis producing a human-readable report. No
+  write access to the forge.
+- **v2 (optional):** Harness stage between pipeline steps that emits a
+  scorecard only (e.g. escalate-to-human), still read-only.
+
+The observer is an analyst, not a fixer — consistent with the retro agent
+design ([#131](https://github.com/fullsend-ai/fullsend/issues/131)).
+
+CLI naming, harness wiring, and report format are implementation details
+deferred to [ADR 0024](0024-harness-definitions.md) or a follow-on ADR.
+
+### 3. Shadow mode for observer actions
+
+When the observer gains write capabilities (forge comments, labels, issues),
+they are **shadowed by default**:
+
+- Shadow on: proposed actions are recorded with `delivered: false`; nothing
+  reaches the forge.
+- Shadow off: explicit per-harness allowlist of permitted actions.
+
+Configuration mechanism (env var, harness field, or org policy) is deferred.
+This de-risks rollout before any automated intervention ships.
+
+### 4. Lesson extraction
+
+After retro or observe, a step extracts **structured lessons** — title, pattern,
+anti-pattern, keywords, and references to the originating run or PR.
+
+Lessons are proposed into the config repo via PR for human review, then
+consumed by the eval harness ([testing-agents.md](../problems/testing-agents.md))
+as golden-set cases. Narrative retro issues remain; lessons are searchable,
+testable memory. Schema and storage location are deferred; git is the source
+of truth for reviewed lessons.
+
+### Rollout order
+
+1. Derived-signal enricher + trace export
+2. Observer read-only report
+3. Shadow action log
+4. Lesson extraction wired to retro output
+
+### Non-goals
+
+- Replacing JSONL traces or ADR 0021 security model
+- A bespoke trace store parallel to OTel-compatible backends
+- Real-time long-lived observer sessions inside sandboxes
+- Mandating a specific observability vendor or eval platform in this ADR
+
+## Consequences
+
+**Easier:**
+
+- Operators and retro agent reason over signals, not full transcripts
+- One query surface for tools, costs, patterns, and scores when a backend is
+  configured
+- Safe path to automated suggestions before live forge actions
+- Closed loop from production runs → lessons → regression tests
+- Correlation across multi-agent workflows via shared run and workflow
+  identifiers on traces
+
+**Harder:**
+
+- Observability backend deployment and credential management per org
+- Signal heuristics need tuning to avoid noisy or misleading scores
+- Hybrid mode requires keeping artifact mirror and backend schema aligned
+- Lesson quality depends on human review discipline in the config repo
+
+**Follow-ups:**
+
+- ADR or design doc for trace-backend selection and export format
+- Harness layout for signal artifacts, shadow logs, and lesson files
+  ([ADR 0024](0024-harness-definitions.md))
+- Per-SIG dashboards and aggregation on trace scores and tags
+- Observer write tools and action allowlist policy