AI agent tracing & observability
Multi-step agents are opaque. When the answer is wrong, you don't know if it was the retrieval, the tool call, or the reasoning. Traces make the steps legible, every prompt, every response, every state.
ProblemMulti-step agentic systems are opaque, when output is wrong, you can't see which tool call, which retrieval, or which reasoning step caused it.
How it works
Capture every prompt, model response, tool call, and intermediate state in a structured trace. Tie traces to evals and production user feedback. Adopt OpenTelemetry GenAI semantic conventions (standardised 2025).
What it catches
Tool-call failures, retrieval misses, latency cliffs, cost regressions, prompt-version effects, judge-vs-user disagreement. Required for any production agent.
Tools
Verdict by project size
Cost
| Project size | Setup | Maint / mo | Tool / mo | CI / run |
|---|---|---|---|---|
| Small <10k LOC | 4h | 1h | $0 | , |
| Medium 10–100k LOC | 2d | 5h | $200 | , |
| Large 100k–1M LOC | 10d | 25h | $2k | , |
| Extra-large >1M LOC | 40d | 120h | $15k | , |
Lifecycle & ownership
Reference implementations
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OpenTelemetry GenAI conventions
Reference semantic conventions for tracing LLM calls and agent operations.
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Langfuse examples
LLM tracing and observability examples for prompts, tools, and scores.
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Phoenix tracing tutorials
Agent and RAG tracing tutorials with evaluation feedback loops.
AI agent tracing & observability adopted which 2025 standard?
Save the lesson
Download SVG ↓
Screenshot for a 1:1, drop it in Slack, or download the SVG.