Debugging Production Agents

Debug production AI agents systematically: trace analysis through the full pipeline, log correlation from user complaint to specific LLM call, handling common production failures (timeouts, context overflow, tool errors), and structured post-mortems for AI incidents.

Quick Reference

→Trace analysis: follow a single request through user input → routing → retrieval → LLM call → tool calls → response
→Log correlation: user complaint → trace ID → specific LLM call → prompt and response that caused the issue
→Top 3 production failures: timeout cascades (38%), context window overflow (25%), tool permission errors (18%)
→Capture-and-replay: record production inputs + state to reproduce issues in a local environment
→Post-mortem template: timeline, root cause, contributing factors, action items, eval suite additions

Trace Analysis: Following a Request End-to-End

Agent traces are not like API traces

A traditional API trace follows a linear path: request → handler → database → response. An agent trace is a graph: request → router → retrieval → LLM call → tool decision → tool execution → second LLM call → response. Multiple LLM calls, branching tool paths, and retry loops make agent traces fundamentally more complex.

Structured trace capture for agent pipelines

▸Every span should capture: operation name, input/output (truncated), latency, tokens used, and any errors
▸Parent-child span relationships let you visualize the trace as a tree (e.g., Jaeger, LangSmith)
▸Store traces for at least 30 days — production issues are often reported days after they occur
▸Truncate input/output to 2KB per span to avoid exploding storage costs on long conversations

Log Correlation: Complaint to Root Cause

When a user reports 'the agent gave me wrong information about X', you need a fast path from complaint to root cause. The chain is: user complaint → response ID → trace ID → specific LLM call span → the exact prompt and retrieved context that produced the wrong answer. Without this chain, debugging is guesswork.

Common Production Failures

Failure Type	Frequency	Symptoms	Root Cause	Fix
Timeout cascade	38%	Requests pile up, latency spikes to 30s+	One slow LLM call blocks the agent loop, retry adds more load	Set per-step timeouts (5s for LLM, 3s for tools), circuit breaker at 3 failures
Context window overflow	25%	Agent crashes or truncates critical context silently	Long conversation + large retrieval results + system prompt exceed model's max tokens	Token budget manager: reserve tokens for response, trim oldest messages first
Tool permission error	18%	Agent repeatedly retries a tool call that always fails	API key expired, rate limit hit, or tool endpoint changed	Health checks on all tools every 60s, disable tool and inform agent if unhealthy
Infinite loop	10%	Token usage spikes, cost alert fires, agent never responds	Agent calls the same tool repeatedly or enters a reasoning cycle	Max step limit (10-15), detect repeated tool calls, force termination after limit
Prompt injection via retrieval	9%	Agent follows instructions embedded in retrieved documents	Malicious or poorly formatted content in knowledge base	Separate data and instructions, validate retrieved content before including in prompt

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