# HITL Step Recording Implementation - Complete Summary ## Overview HITL (Human-In-The-Loop) checkpoint decisions are now recorded as separate steps in `trace.json`, making the full checkpoint lifecycle available for audit and review alongside existing events in `run_events.jsonl`. ## Changes Made ### Modified File - **`src/data_agent_baseline/langgraph_agent/graph.py`** ### Implementation Details #### 1. New Helper Function: `_record_hitl_step()` Added function that: - Creates a standardized HITL step object matching the trace.json format - Captures decision type (approve/cancel/revise/timeout_auto_approve) - Records original and revised plans - Includes human steering instructions - Preserves checkpoint configuration (timeout_seconds, default_action_on_timeout) - Appends step to state.steps for inclusion in final trace.json - Emits optional callback event for run_events.jsonl **Location**: Lines ~44-107 in graph.py #### 2. Integration Points Function is called in `_maybe_handle_planner_checkpoint()` at 4 decision paths: **A) APPROVE Decision** (Lines ~365-378) ```python _record_hitl_step( state, checkpoint_id=checkpoint.checkpoint_id, decision=decision_record.decision, message="Checkpoint approved. Resuming execution.", original_plan=original_plan, revised_plan=original_plan, ... ) ``` **B) CANCEL Decision** (Lines ~403-416) ```python _record_hitl_step( state, checkpoint_id=checkpoint.checkpoint_id, decision=decision_record.decision, message="Task cancelled by human at planner checkpoint.", original_plan=original_plan, revised_plan=None, human_instruction=decision_record.human_instruction, ... ) ``` **C) REVISE Decision** (Lines ~445-458) ```python _record_hitl_step( state, checkpoint_id=checkpoint.checkpoint_id, decision=decision_record.decision, message="Revision submitted and replanning completed...", original_plan=original_plan, revised_plan=revised_plan, # After replanning human_instruction=decision_record.human_instruction, ... ) ``` **D) TIMEOUT_AUTO_APPROVE Decision** (Lines ~315-327) ```python _record_hitl_step( state, checkpoint_id=checkpoint.checkpoint_id, decision=InterventionDecision.TIMEOUT_AUTO_APPROVE.value, message=f"No human response within {policy.timeout_seconds}s...", original_plan=original_plan, revised_plan=original_plan, ... ) ``` ## Result Format in trace.json HITL steps appear in `trace.json["steps"]` as: ```json { "step_index": , "action": "planner_review", "phase": "planner_review", "thought": "Human-in-the-loop checkpoint review (checkpoint_id=cp_...)", "action_input": { "checkpoint_id": "cp_...", "decision": "approve|cancel|revise|timeout_auto_approve" }, "raw_response": "(human decision)", "observation": { "ok": true, "tool": "planner_review", "content": { "checkpoint_id": "cp_...", "decision": "...", "message": "...", "original_plan": { /* Full plan object */ }, "revised_plan": { /* Full plan object or null */ }, "human_instruction": "...", "timeout_seconds": 60, "default_action_on_timeout": "continue" } }, "ok": true } ``` ## Example Usage ### View HITL steps in trace.json ```bash # View all HITL review steps jq '.steps[] | select(.action == "planner_review")' trace.json # View the decision for a specific checkpoint jq '.steps[] | select(.action == "planner_review") | .observation.content' trace.json # Count HITL interactions jq '[.steps[] | select(.action == "planner_review")] | length' trace.json ``` ### Review approval decisions ```bash jq '.steps[] | select(.action == "planner_review" and .observation.content.decision == "approve")' trace.json ``` ### Compare original vs revised plans ```bash jq '.steps[] | select(.action == "planner_review" and .observation.content.decision == "revise") | {original: .observation.content.original_plan, revised: .observation.content.revised_plan}' trace.json ``` ## Backward Compatibility ✅ **Fully backward compatible**: - Existing non-HITL runs unaffected - HITL steps appended to steps array (no structure changes) - Existing event emission to run_events.jsonl unchanged - All existing visualization tools continue to work ## Data Retention The implementation ensures: - **Immutable Audit Trail**: HITL decisions persisted in trace.json - **Full Context**: Original and revised plans stored together - **Human Intent**: Steering instructions preserved - **Timing**: Checkpoint timeout configuration captured - **Traceability**: checkpoint_id links to run_events.jsonl events ## Testing Comprehensive test file validates all decision paths: - ✅ Approval decisions - ✅ Cancel decisions - ✅ Revision decisions with plan changes - ✅ Timeout auto-approve scenarios - ✅ Multiple HITL steps with proper indexing - ✅ JSON serialization compatibility **Test file**: `test_hitl_step_recording.py` Run with: `python test_hitl_step_recording.py` ## Verification To verify the implementation in a live run: ```bash # 1. Run a guided task with HITL cd /workspace/ainn-cm-poc-data-agent python -m data_agent_baseline.cli run-lang-task --task-id task_145 --guided # 2. Check trace.json for HITL step cat artifacts/runs/*/task_145/trace.json | jq '.steps[-1]' # 3. Verify it appears with correct structure jq 'if .steps[-1].action == "planner_review" then "✅ HITL step found" else "❌ HITL step not found" end' trace.json ``` ## Benefits ✨ **Key Advantages**: 1. **Audit & Compliance**: Complete record of human decisions 2. **Review**: Easy visualization of checkpoint lifecycle 3. **Analysis**: Patterns in human steering decisions 4. **Debugging**: Understand why plans changed 5. **Reproducibility**: Exact conditions for replanning captured 6. **Integration**: Works with existing observatory tools ## Notes - HITL steps have `"ok": true` (they're decisions, not tool failures) - `phase` is always `"planner_review"` (distinguishes from other actions) - Multiple HITL steps per task are possible (e.g., approve, then revise) - Each step has independent checkpoint_id for linking to events