Observability

In standard web apps, you log "Request -> Response". In agents, a single request triggers a cascade: Thought -> Action -> Observation -> Thought...

Observability is the art of visualizing this chain to understand why the agent did what it did.

What to Trace

1. The Full Trace (Tree)

You need to see the entire execution tree.

  • Span: A single unit of work (e.g., an LLM call, a Tool execution).
  • Trace: The parent object containing all spans for a user session.

Tools like LangSmith or Helicone provide this visualization out of the box.

2. Token Usage & Cost

Track tokens per span. This helps identify "Token Hogs".

  • Example: You realize your "Summarizer" tool is consuming 80% of your budget because it's re-reading the entire document history on every loop.

3. Latency Waterfalls

Which step is slow?

  • Is it the model generation? (Time to First Token)
  • Is it the vector DB search?
  • Is it the external API tool?

Debugging Workflow

The "Why did it fail?" Loop:

  1. Open the Trace: Find the failed Session ID.
  2. Inspect the Context: Look at the exact prompt sent to the LLM at the step before failure.
    • Did the Context window cut off key info?
    • Did the RAG search return irrelevant documents?
    • Did the previous tool return an error message?
  3. Playground Replay: Copy that exact prompt into a Playground and edit it until it works.
  4. Patch: Update the prompt template or code logic.

Summary

Agents are non-deterministic black boxes. Observability opens the box. Without it, you are debugging by guessing. Tracing gives you the X-Ray vision needed to optimize performance and fix logic bugs.