Debug Agent Execution Errors from Runtime Logs

Debug Agent Execution Errors from Runtime Logs

You open the runtime logs after an agent run fails and need to pinpoint the exact step that triggered the error. The Agent Command Center surfaces those logs with execution traces, token counts and approval decisions in one view.

This guide shows how to read the logs, map errors to your versioned config object and route fixes through the approvals inbox.

Key Takeaways

• Runtime logs contain per-step traces, token usage and cost estimates for every agent you run.
• Every sensitive action must pass the approvals inbox before it touches your agent runtime backend.
• All role prompts, tools and autonomy levels live inside a single versioned config object.
• Human review of error traces prevents repeated failed executions.

Locate Runtime Logs for a Specific Execution

Open the Agent Command Center and filter the execution list by agent name and time window. Select the failed run to open its dedicated log view.

The log view streams intermediate outputs, model responses and any exception messages in chronological order. When multiple agents share the same runtime backend, apply additional filters for execution ID and role name to isolate the trace quickly.

• Filter by execution ID
• Search for error keywords
• Export the full trace as JSON
• Compare against the prior successful run
• Note the exact token count at failure
• Review intermediate outputs for partial progress
• Check schedule metadata attached to the run

Read Error Traces and Token Usage

Each trace line shows the agent role, tool call and model response. Token usage appears after every model step so you can spot spikes that preceded the error. Following recommendations in the NIST guide to computer security log management helps teams structure these traces for faster root-cause analysis.

Look for lines that contain exception text or non-zero exit codes. These entries usually reference the line in your config object that defined the failing tool or prompt. High token counts often indicate repeated retries or overly broad tool calls that exhausted the per-role limit.

• Role name and prompt version
• Tool name and input parameters
• Model name and temperature
• Token count before and after the call
• Timestamp of the failure
• Cost estimate at the moment of error
• Exception stack trace if available

Map Errors to the Versioned Config Object

Open the config object history for the agent. Compare the version active during the failed run with the version that succeeded previously.

Changes to role prompts, tool lists or autonomy levels appear as diff entries. Revert or edit the object, then schedule a new run that still requires human approval for sensitive steps. Teams that maintain strict version control reduce recurrence of the same execution errors by more than half in practice.

• Check prompt version numbers
• Review added or removed tools
• Inspect autonomy level changes
• Confirm schedule adjustments
• Validate model parameter updates
• Note any new retry rules introduced

Route Sensitive Failures Through the Approvals Inbox

When an error involves a real-world action, the trace lands in the approvals inbox even if the step failed. Review the proposed output, edit it if needed and approve or reject before any retry.

This step keeps every agent you run under human control on your agent runtime backend. Edit Agent Outputs in Approvals Inbox Before Execution shows the exact workflow.

• View the failed action payload
• Edit parameters before retry
• Add a note for the next operator
• Set a new approval rule in the config object
• Reject and archive the entry

Compare Log Fields Across Multiple Runs

Use the comparison view to place two execution traces side by side. The table below highlights fields that commonly differ between success and failure.

The differences often point to a recent change in the single config object that increased autonomy or added tools. Cross-reference these fields with the approvals inbox status to decide whether the error requires a config rollback or a rule adjustment.

Set Up Alerts on Recurring Error Patterns

Define alert rules inside the control plane that watch for specific exception strings or token thresholds. When a pattern repeats, the system creates an inbox item that requires your review before the next scheduled run.

• Exception contains "rate limit"
• Token count exceeds 4000
• Tool call duration longer than 30 seconds
• Autonomy level raised without approval
• Model fallback triggered
• Repeated tool timeout on the same endpoint

Document Execution Errors for Compliance Reviews

Runtime logs also serve as audit evidence when compliance teams examine agent activity. Store each failed trace alongside the active config object version and the approvals inbox decision that followed. This practice aligns with the NIST AI Risk Management Framework emphasis on measurable governance of autonomous systems.

Create a quarterly export that includes error frequency, average token burn per failure category and the number of human interventions required. These metrics help demonstrate that every sensitive action on your agent runtime backend receives documented oversight.

• Export traces filtered by error type
• Attach corresponding config object diffs
• Record inbox approval timestamps
• Calculate mean time to resolution
• Tag entries by sensitivity level
• Share anonymized summaries with audit teams

Control Plane Audit Needs for Compliance Teams explains how to structure these records for external review.

Test Fixes with Controlled Retries

After you update the config object, create a test execution that uses the same input but routes every real-world action through the approvals inbox. Monitor the new runtime logs for the same error.

Implement Retry Logic in Versioned Agent Config explains how to add timeout and fallback rules while keeping human oversight.

• Run the test with reduced autonomy
• Compare token usage against the prior trace
• Confirm no new errors appear
• Approve the retry only after inbox review
• Archive the original error entry
• Verify cost estimates remain within per-role limits

Conclusion

Review the runtime logs for the next failed execution, map the trace to the active config object version and send any sensitive retry through the approvals inbox. This process keeps every agent you run traceable on your agent runtime backend.

Frequently Asked Questions

How do runtime logs help debug agent execution errors?

Runtime logs show the exact sequence of tool calls, model responses and token counts that led to the failure, allowing you to isolate the step inside the versioned config object.

Do all failed actions require human approval?

Yes. Any action that would touch the real world routes to the approvals inbox regardless of whether the step succeeded or failed.

Can I compare token usage across different config versions?

The Agent Command Center displays token usage and cost estimates for every execution, making it simple to see the impact of changes stored in the single config object.

What fields appear in an execution trace?

Traces include role name, tool input, model parameters, token count, timestamp and any exception text.

How often should I review the approvals inbox?

Check the inbox before every scheduled batch or immediately after an error alert so that fixes maintain human oversight on your agent runtime backend.