How do you diagnose AI agent errors? A practical checklist for end-to-end tracing and alerting
Agent errors can originate from the model, knowledge retrieval, tool parameters, permissions, or external APIs. Without unified tracing, teams can only modify prompts based on guesswork. With end-to-end events, they can identify the first anomalous step and verify the fix.
Why traditional logs are not enough
A single agent task may involve planning, retrieval, model calls, tool calls, retries, approvals, and aggregation. An incorrect final answer does not necessarily mean that the model itself was wrong. It may also result from outdated knowledge snippets, incorrectly generated parameters, permission denials, or external API timeouts. Observability must connect all these steps under the same trace.
What to record for every event
- A unified trace ID, task ID, and step ID.
- Versions of the agent, prompt, model, skill, and knowledge base.
- Start time, duration, status, retries, and dependent steps.
- Redacted input and output summaries, along with references to the original objects.
- A summary of tool parameters, the returned status, and the final external state.
- The result, reviewer, and time of any human approval.
Do not write complete prompts, responses, credentials, or personal information to logs by default. Depending on the risk, use redaction, summaries, hashes, or controlled object storage.
Segment metrics across five categories
| Category | Key metrics to monitor |
|---|---|
| Reliability | Task success, tool failures, retries, and human intervention |
| Performance | End-to-end and per-step latency, and queueing |
| Quality | Citation consistency, rule failures, and human rejections |
| Cost | Per-task model and tool consumption |
| Security | Unauthorized access attempts, sensitive data matches, and approval bypasses |
Averages can conceal tail-end problems. Metrics should be segmented by task type, version, tool, and tenant, with high-percentile latency monitored as well.
Alerts must directly guide action
“The error rate has increased” does not indicate the scope of impact or the remediation path. An effective alert should identify the affected tool and tasks, state when the problem began, and be linked to an owner, runbook, and recovery criteria. Switch automatically only when an alternative path has been validated. For write operations, duplicate execution must also be prevented.
The sequence for troubleshooting an issue
- Confirm the final state shown to the user and the trace ID.
- Locate the first anomalous step rather than the last error message.
- Verify the versions, input summary, retrieved evidence, and tool response at that time.
- Determine whether the cause was a one-off data issue, a dependency failure, or a version regression.
- After applying the fix, replay the original failed sample and add it to the regression set.
Common pitfalls
- Recording only model calls while ignoring knowledge, permissions, and tools.
- Storing all raw text, thereby increasing privacy risks.
- Collecting numerous metrics without defining thresholds, owners, or actions.
- Adding tracing only after launch, making it impossible to reconstruct past failures.
Frequently asked questions
What is the difference between observability and APM?
APM focuses on services and APIs. Agent observability must also correlate prompts, retrieval, tool decisions, quality, cost, and approvals. The two should complement each other.
Is it necessary to store complete inputs and outputs?
No. Depending on business risk, you can use redaction, summaries, hashes, sampling, or controlled references, while also setting access controls and retention periods.
What should you implement first?
First, standardize the trace ID and the definition of task success. Then add step events, quality evaluations, and actionable alerts.
What to do next
Start by standardizing trace and version fields in one critical SmaugBrain workflow. Select a historical failure and perform a complete replay to confirm that the logs can both pinpoint the problem and avoid exposing sensitive information.