Agent Trace

Debug agent execution: $ARGUMENTS

Before Starting

1. Read the tracing infrastructure:
   • src/agents/shared/agent-tracer.ts — AgentTracer class, event recording
   • src/agents/shared/hooks.ts — lifecycle hooks (onStart, onToolCall, onToolComplete, onComplete, onError)
   • src/lib/database/schema.ts — search for agent_traces and agent_metrics tables

Trace Event Types

The AgentTracer records these events during execution:

Querying Traces

Recent traces for an agent

SELECT id, agent_name, trace_type, duration, tool_calls_count, error_message,
       created_at
FROM agent_traces
WHERE agent_name = '<agent-name>'
ORDER BY created_at DESC
LIMIT 20;

Traces with errors

SELECT id, agent_name, error_message, trace_data, created_at
FROM agent_traces
WHERE error_message IS NOT NULL
ORDER BY created_at DESC
LIMIT 10;

Slow traces (over 10s)

SELECT id, agent_name, duration, tool_calls_count, trace_data, created_at
FROM agent_traces
WHERE duration > 10000
ORDER BY duration DESC
LIMIT 10;

Tool call breakdown

SELECT
  trace_data->'events' AS events,
  tool_calls_count,
  duration
FROM agent_traces
WHERE agent_name = '<agent-name>'
  AND tool_calls_count > 0
ORDER BY created_at DESC
LIMIT 5;

Agent metrics summary

SELECT agent_name, date,
       usage_count, avg_response_time, success_rate,
       cost_total, tokens_total, error_count
FROM agent_metrics
WHERE agent_name = '<agent-name>'
ORDER BY date DESC
LIMIT 30;

Debug Flags

Set these environment variables to enable verbose logging:

AI_LAB_DEBUG_TOOLS=true          # Log tool selection & validation
AI_LAB_DEBUG_CONTEXT=true        # Log context receipt & changes
AI_LAB_DEBUG_INSTRUCTIONS=true   # Log assembled instructions (caution: large)
AI_LAB_DEBUG_RETRIEVAL=true      # Log file_search queries, sources, scores

Analysis Patterns

Diagnosing slow responses

1. Query traces with high duration
2. Check events array for tool_call → tool_complete durations
3. Identify which tool is the bottleneck
4. Check if the tool is hitting an external service (file_search, search API)
5. Consider caching or timeout adjustments

Diagnosing tool failures

1. Query traces where error_message is not null
2. Look at the tool_call event immediately before the error event
3. Check if the tool params were valid
4. Check if the external service was available

Diagnosing guardrail rejections

1. Guardrail rejections show as error events with the guardrail name
2. Check the reason field for why the message was rejected
3. Review the input message and guardrail logic

Comparing agent performance over time

1. Query agent_metrics table for daily aggregates
2. Check trends in avg_response_time, success_rate, error_count
3. Correlate spikes with deployment dates or config changes

Rules

• Use the Supabase MCP (mcp__supabase__execute_sql) for querying traces directly
• Never delete traces — they're the audit trail for agent behavior
• When reporting findings, include specific trace IDs for reference
• Debug flags should only be enabled temporarily — they generate large log volumes
• If traces reveal a systemic issue, recommend a fix in the relevant layer (tool, instructions, guardrail)