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Add agent explainability instrumentation and unify envelope field naming (#795)
Addresses recommendations from the UX developer's agent experience report. Adds provenance predicates, DAG structure changes, error resilience, and a published OWL ontology. Explainability additions: - Tool candidates: tg:toolCandidate on Analysis events lists the tools visible to the LLM for each iteration (names only, descriptions in config) - Termination reason: tg:terminationReason on Conclusion/Synthesis events (final-answer, plan-complete, subagents-complete) - Step counter: tg:stepNumber on iteration events - Pattern decision: new tg:PatternDecision entity in the DAG between session and first iteration, carrying tg:pattern and tg:taskType - Latency: tg:llmDurationMs on Analysis events, tg:toolDurationMs on Observation events - Token counts on events: tg:inToken/tg:outToken/tg:llmModel on Grounding, Focus, Synthesis, and Analysis events - Tool/parse errors: tg:toolError on Observation events with tg:Error mixin type. Parse failures return as error observations instead of crashing the agent, giving it a chance to retry. Envelope unification: - Rename chunk_type to message_type across AgentResponse schema, translator, SDK types, socket clients, CLI, and all tests. Agent and RAG services now both use message_type on the wire. Ontology: - specs/ontology/trustgraph.ttl — OWL vocabulary covering all 26 classes, 7 object properties, and 36+ datatype properties including new predicates. DAG structure tests: - tests/unit/test_provenance/test_dag_structure.py verifies the wasDerivedFrom chain for GraphRAG, DocumentRAG, and all three agent patterns (react, plan, supervisor) including the pattern-decision link.
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42 changed files with 1577 additions and 205 deletions
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@ -131,21 +131,21 @@ async def analyse(path, url, flow, user, collection):
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for i, msg in enumerate(messages):
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resp = msg.get("response", {})
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chunk_type = resp.get("chunk_type", "?")
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message_type = resp.get("message_type", "?")
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if chunk_type == "explain":
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if message_type == "explain":
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explain_id = resp.get("explain_id", "")
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explain_ids.append(explain_id)
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print(f" {i:3d} {chunk_type} {explain_id}")
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print(f" {i:3d} {message_type} {explain_id}")
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else:
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print(f" {i:3d} {chunk_type}")
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print(f" {i:3d} {message_type}")
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# Rule 7: message_id on content chunks
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if chunk_type in ("thought", "observation", "answer"):
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if message_type in ("thought", "observation", "answer"):
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mid = resp.get("message_id", "")
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if not mid:
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errors.append(
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f"[msg {i}] {chunk_type} chunk missing message_id"
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f"[msg {i}] {message_type} chunk missing message_id"
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)
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print()
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