AI Agent Observability: Tracing, Spans, and Replay Debugging

AI agents need OpenTelemetry-style tracing with one span per LLM call and tool call, plus full input/output capture for replay debugging in production.

What AI does well here

• Emitting structured span data when given a tracing tool
• Including correlation IDs across distributed calls
• Logging tool inputs and outputs at decision boundaries
• Producing replayable traces when prompts are deterministic

What AI cannot do

• Self-instrument without explicit tracing infrastructure
• Identify the root cause of multi-turn behavior changes alone

Practice this safely

Use a small project example from your own work. The useful move is to compare the AI's draft against your goal, sources, and constraints before you trust it.

1. 1Ask AI to explain tracing in plain language, then underline anything that sounds uncertain or too broad.
2. 2Give it one detail from "AI Agent Observability: Tracing, Spans, and Replay Debugging" and ask for two possible next steps plus one reason each step might be wrong.
3. 3Check spans against a trusted source, teacher, adult, expert, or original document before you use it.