ObservabilityGuide5 min

Trace agent workflows for debugging

A guide to making multi-step AI behavior inspectable enough to fix when something goes wrong.

Real example

Example: debug why a proposal was marked low fit

A proposal manager disputes an AI recommendation. The output says low fit, but the team believes the tender is relevant.

Open the workflow trace: source document, extraction output, tool calls, qualification criteria, model response id, validation result, and reviewer notes. The trace shows the model missed one certificate from an appendix.

The fix is clear: improve document parsing for appendices. Without the trace, the team might have rewritten the prompt blindly.

Tutorial path

How to implement it

Step 01

Create a workflow run ID before the first model request.

Step 02

Attach every response, tool call, validation result, and approval decision to that run.

Step 03

Store safe summaries and IDs instead of full sensitive payloads where possible.

Step 04

Render an internal timeline for failed or disputed AI actions.

Step 05

Use traces to choose whether to change prompts, tools, data, or UX.

Checklist

Ready when these are true

Workflow run ID

Tool calls attached

Validation results stored

Sensitive data minimized

Debug timeline available

Field notes

What matters in practice

Debugging needs the request, tool calls, intermediate outputs, and final user-visible result.

Logs should explain behavior without exposing secrets or unnecessary personal data.

A trace is useful when a support or engineering person can replay the decision path.

Avoid these mistakes

Common failure modes

Do not log sensitive raw data everywhere just to debug.

Do not keep only the final answer.

Do not merge multiple workflow runs under one vague log entry.

Practical tip

A good trace helps you decide whether the bug is prompt, data, tool, model, or UI.

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