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.
Introduce an agent orchestrator service that supports three
execution patterns (ReAct, plan-then-execute, supervisor) with
LLM-based meta-routing to select the appropriate pattern and task
type per request. Update the agent schema to support
orchestration fields (correlation, sub-agents, plan steps) and
remove legacy response fields (answer, thought, observation).
The metadata field (list of triples) in the pipeline Metadata class
was redundant. Document metadata triples already flow directly from
librarian to triple-store via emit_document_provenance() - they don't
need to pass through the extraction pipeline.
Additionally, chunker and PDF decoder were overwriting metadata to []
anyway, so any metadata passed through the pipeline was being
discarded.
Changes:
- Remove metadata field from Metadata dataclass
(schema/core/metadata.py)
- Update all Metadata instantiations to remove metadata=[]
parameter
- Remove metadata handling from translators (document_loading,
knowledge)
- Remove metadata consumption from extractors (ontology, agent)
- Update gateway serializers and import handlers
- Update all unit, integration, and contract tests
* Changed schema for Value -> Term, majorly breaking change
* Following the schema change, Value -> Term into all processing
* Updated Cassandra for g, p, s, o index patterns (7 indexes)
* Reviewed and updated all tests
* Neo4j, Memgraph and FalkorDB remain broken, will look at once settled down
* Tweak the structured query schema
* Structure query service
* Gateway support for nlp-query and structured-query
* API support
* Added CLI
* Update tests
* More tests
