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Agent explainability tech specs (#655)
* Query time provenance tech spec * Extraction provenance placeholder
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docs/tech-specs/extraction-time-provenance.md
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docs/tech-specs/extraction-time-provenance.md
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# Extraction-Time Provenance: Source Layer
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## Status
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Notes - Not yet started
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## Overview
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This document captures notes on extraction-time provenance for future specification work. Extraction-time provenance records the "source layer" - where data came from originally, how it was extracted and transformed.
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This is separate from query-time provenance (see `query-time-provenance.md`) which records agent reasoning.
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## Current State
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Source metadata is already partially stored in the knowledge graph (~40% solved):
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- Documents have source URLs, timestamps
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- Some extraction metadata exists
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## Scope
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Extraction-time provenance should capture:
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### Source Layer (Origin)
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- URL / file path
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- Retrieval timestamp
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- Funding sources
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- Authorship / authority
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- Document metadata (title, date, version)
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### Transformation Layer (Extraction)
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- Extraction tool used (e.g., PDF parser, table extractor)
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- Extraction method / version
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- Confidence scores
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- Raw-to-structured mapping
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- Parent-child relationships (PDF → table → row → fact)
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## Key Questions for Future Spec
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1. What metadata is already captured today?
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2. What gaps exist?
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3. How to structure the extraction DAG?
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4. How does query-time provenance link to extraction-time nodes?
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5. Storage format - RDF triples? Separate schema?
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## References
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- Query-time provenance: `docs/tech-specs/query-time-provenance.md`
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- PROV-O standard for provenance modeling
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- Existing source metadata in knowledge graph (needs audit)
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docs/tech-specs/query-time-provenance.md
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# Query-Time Provenance: Agent Explainability
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## Status
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Draft - Gathering Requirements
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## Overview
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This specification defines how the agent framework records and communicates provenance during query execution. The goal is full explainability: tracing how a result was obtained, from final answer back through reasoning steps to source data.
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Query-time provenance captures the "inference layer" - what the agent did during reasoning. It connects to extraction-time provenance (source layer) which records where facts came from originally.
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## Terminology
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| Term | Definition |
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|------|------------|
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| **Provenance** | The record of how a result was derived |
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| **Provenance Node** | A single step or artifact in the provenance DAG |
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| **Provenance DAG** | Directed Acyclic Graph of provenance relationships |
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| **Query-time Provenance** | Provenance generated during agent reasoning |
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| **Extraction-time Provenance** | Provenance from data ingestion (source metadata) - separate spec |
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## Architecture
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### Two Provenance Contexts
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1. **Extraction-time** (out of scope for this spec):
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- Generated when data is ingested (PDF extraction, web scraping, etc.)
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- Records: source URL, extraction method, timestamps, funding, authorship
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- Already partially implemented via source metadata in knowledge graph
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- See: `docs/tech-specs/extraction-time-provenance.md` (notes)
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2. **Query-time** (this spec):
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- Generated during agent reasoning
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- Records: tool invocations, retrieval results, LLM reasoning, final conclusions
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- Links to extraction-time provenance for retrieved facts
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### Provenance Flow
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```
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Agent Session
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│
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├─► Tool: Knowledge Query
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│ │
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│ ├─► Retrieved Fact A ──► [link to extraction provenance]
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│ └─► Retrieved Fact B ──► [link to extraction provenance]
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│
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├─► LLM Reasoning Step
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│ │
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│ └─► "Combined A and B to conclude X"
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│
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└─► Final Answer
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│
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└─► Derived from reasoning step above
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```
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### Storage
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- Provenance stored in knowledge graph infrastructure
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- Segregated in a **separate collection** for distinct retrieval patterns
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- Query-time provenance references extraction-time provenance nodes via IRIs
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- Persists beyond agent session (reusable, auditable)
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### Real-Time Streaming
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Provenance events stream back to the client as the agent works:
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1. Agent invokes tool
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2. Tool generates provenance data
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3. Provenance stored in graph
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4. Provenance event sent to client
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5. UX builds provenance visualization incrementally
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## Provenance Node Structure
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Each provenance node represents a step in the reasoning process.
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### Node Identity
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Provenance nodes are identified by IRIs containing UUIDs, consistent with the RDF-style knowledge graph:
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```
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urn:trustgraph:prov:550e8400-e29b-41d4-a716-446655440000
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```
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### Core Fields
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| Field | Description |
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|-------|-------------|
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| `id` | IRI with UUID (e.g., `urn:trustgraph:prov:{uuid}`) |
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| `session_id` | Agent session this belongs to |
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| `timestamp` | When this step occurred |
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| `type` | Node type (see below) |
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| `derived_from` | List of parent node IRIs (DAG edges) |
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### Node Types
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| Type | Description | Additional Fields |
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|------|-------------|-------------------|
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| `retrieval` | Facts retrieved from knowledge graph | `facts`, `source_refs` |
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| `tool_invocation` | Tool was called | `tool_name`, `input`, `output` |
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| `reasoning` | LLM reasoning step | `prompt_summary`, `conclusion` |
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| `answer` | Final answer produced | `content` |
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### Example Provenance Nodes
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```json
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{
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"id": "urn:trustgraph:prov:550e8400-e29b-41d4-a716-446655440001",
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"session_id": "urn:trustgraph:session:7c9e6679-7425-40de-944b-e07fc1f90ae7",
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"timestamp": "2024-01-15T10:30:00Z",
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"type": "retrieval",
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"derived_from": [],
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"facts": [
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{
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"id": "urn:trustgraph:fact:9b1deb4d-3b7d-4bad-9bdd-2b0d7b3dcb6d",
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"content": "Swallow airspeed is 8.5 m/s"
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}
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],
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"source_refs": ["urn:trustgraph:extract:1b9d6bcd-bbfd-4b2d-9b5d-ab8dfbbd4bed"]
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}
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```
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```json
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{
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"id": "urn:trustgraph:prov:550e8400-e29b-41d4-a716-446655440002",
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"session_id": "urn:trustgraph:session:7c9e6679-7425-40de-944b-e07fc1f90ae7",
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"timestamp": "2024-01-15T10:30:01Z",
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"type": "reasoning",
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"derived_from": ["urn:trustgraph:prov:550e8400-e29b-41d4-a716-446655440001"],
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"prompt_summary": "Asked to determine average swallow speed",
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"conclusion": "Based on retrieved data, average speed is 8.5 m/s"
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}
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```
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## Provenance Events
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Events streamed to the client during agent execution.
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### Design: Lightweight Reference Events
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Provenance events are lightweight - they reference provenance nodes by IRI rather than embedding full provenance data. This keeps the stream efficient while allowing the client to fetch full details if needed.
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A single agent step may create or modify multiple provenance objects. The event references all of them.
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### Event Structure
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```json
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{
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"provenance_refs": [
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"urn:trustgraph:prov:550e8400-e29b-41d4-a716-446655440001",
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"urn:trustgraph:prov:550e8400-e29b-41d4-a716-446655440002"
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]
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}
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```
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### Integration with Agent Response
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Provenance events extend `AgentResponse` with a new `chunk_type: "provenance"`:
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```json
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{
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"chunk_type": "provenance",
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"content": "",
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"provenance_refs": ["urn:trustgraph:prov:..."],
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"end_of_message": false
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}
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```
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This allows provenance updates to flow alongside existing chunk types (`thought`, `observation`, `answer`, `error`).
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## Tool Provenance Reporting
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Tools report provenance as part of their execution.
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### Minimum Reporting (all tools)
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Every tool can report at minimum:
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- Tool name
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- Input arguments
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- Output result
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### Enhanced Reporting (tools that can describe more)
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Tools that understand their internals can report:
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- What sources were consulted
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- What reasoning/transformation was applied
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- Confidence scores
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- Links to extraction-time provenance
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### Graceful Degradation
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Tools that can't provide detailed provenance still participate:
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```json
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{
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"type": "tool_invocation",
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"tool_name": "calculator",
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"input": {"expression": "8 + 5"},
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"output": "13",
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"detail_level": "basic"
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}
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```
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## Design Decisions
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### Provenance Node Identity: IRIs with UUIDs
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Provenance nodes use IRIs containing UUIDs, consistent with the RDF-style knowledge graph:
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- Format: `urn:trustgraph:prov:{uuid}`
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- Globally unique, persistent across sessions
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- Can be dereferenced to retrieve full node data
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### Storage Segregation: Separate Collection
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Provenance is stored in a separate collection within the knowledge graph infrastructure. This allows:
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- Distinct retrieval patterns for provenance vs. data
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- Independent scaling/retention policies
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- Clear separation of concerns
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### Client Protocol: Extended AgentResponse
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Provenance events extend `AgentResponse` with `chunk_type: "provenance"`. Events are lightweight, containing only IRI references to provenance nodes created/modified in the step.
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### Retrieval Granularity: Flexible, Multiple Objects Per Step
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A single agent step can create multiple provenance objects. The provenance event references all objects created or modified. This handles cases like:
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- Retrieval returning multiple facts (each gets a provenance node)
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- Tool invocation creating both an invocation node and result nodes
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### Graph Structure: True DAG
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The provenance structure is a DAG (not a tree):
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- A provenance node can have multiple parents (e.g., reasoning combines facts A and B)
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- Extraction-time nodes can be referenced by multiple query-time sessions
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- Enables proper modeling of how conclusions derive from multiple sources
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### Linking to Extraction Provenance: Direct IRI Reference
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Query-time provenance references extraction-time provenance via direct IRI links in the `source_refs` field. No separate linking mechanism needed.
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## Open Questions
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### Provenance Retrieval API
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Base layer uses the existing knowledge graph API to query the provenance collection. A higher-level service may be added to provide convenience methods. Details TBD during implementation.
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### Provenance Node Granularity
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Placeholder to explore: What level of detail should different node types capture?
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- Should `reasoning` nodes include the full LLM prompt, or just a summary?
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- How much of tool input/output to store?
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- Trade-offs between completeness and storage/performance
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### Provenance Retention
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TBD - retention policy to be determined:
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- Indefinitely?
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- Tied to session retention?
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- Configurable per collection?
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## Implementation Considerations
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### Files Likely Affected
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| Area | Changes |
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|------|---------|
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| Agent service | Generate provenance events |
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| Tool implementations | Report provenance data |
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| Agent response schema | Add provenance event type |
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| Knowledge graph | Provenance storage/retrieval |
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### Backward Compatibility
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- Existing agent clients continue to work (provenance is additive)
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- Tools that don't report provenance still function
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## References
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- PROV-O (PROV-Ontology): W3C standard for provenance modeling
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- Current agent implementation: `trustgraph-flow/trustgraph/agent/react/`
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- Agent schemas: `trustgraph-base/trustgraph/schema/services/agent.py`
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- Extraction-time provenance notes: `docs/tech-specs/extraction-time-provenance.md`
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