🏥 Safe Output Health Report - November 29, 2025

[github-actions[bot]]

Safe output jobs across agentic workflows are operating at excellent health with a 97.78% success rate over the last 24 hours. Out of 90 safe output job executions across 57 workflow runs, only 2 failures were detected, both related to a non-critical issue assignment feature.

The analysis reveals that all safe output job types except create_issue achieved 100% success rates. The create_issue job type experienced a minor degradation (92% success rate) due to attempts to assign issues to the Copilot coding agent when it's not available for the repository.

Full Report Details

Executive Summary

• Period: Last 24 hours (November 28-29, 2025)
• Runs Analyzed: 57
• Workflows Active: 57
• Safe Output Jobs Executed: 90
• Safe Output Jobs Failed: 2
• Error Clusters Identified: 1
• Overall Success Rate: 97.78%

Safe Output Job Statistics

Job Type	Total Executions	Failures	Success Rate
create_discussion	8	0	100.00%
create_issue	25	2	92.00%
add_comment	21	0	100.00%
create_pull_request	8	0	100.00%
add_labels	20	0	100.00%
push_to_pull_request_branch	8	0	100.00%
create_pull_request_review_comment	0	0	N/A
update_issue	0	0	N/A
missing_tool	0	0	N/A

Key Observations:

• Most safe output job types achieved perfect 100% success rates
• create_issue is the only job type with failures (2 out of 25 executions)
• No executions of create_pull_request_review_comment, update_issue, or missing_tool jobs in this period

Error Clusters

Cluster 1: Copilot Agent Assignment Failure

• Count: 2 occurrences
• Affected Jobs: create_issue
• Affected Workflows: Duplicate Code Detector
• Sample Error:

  Failed to assign issue: copilot coding agent is not available for this repository
  Failed to assign issue #4954 to `@copilot`: copilot coding agent is not available for this repository
  

• Root Cause: The create_issue safe output job attempts to assign newly created issues to @copilot (the Copilot coding agent), but the Copilot coding agent feature is not currently enabled or available for the githubnext/gh-aw repository.
• Impact: Low - The issue creation itself succeeds. Only the post-creation assignment step fails. The issue is created and accessible, just not assigned to the Copilot agent. This is a non-critical failure that doesn't affect the primary function.
• First Seen: 2025-11-27T21:05:49Z

Root Cause Analysis

Issue Assignment Logic

The create_issue safe output job includes logic to automatically assign created issues to a specified assignee. When workflows are configured to assign issues to @copilot, the job attempts this assignment using the GraphQL API.

However, the assignment fails because:

1. The workflow detects @copilot as the requested assignee
2. It attempts to use the GraphQL API for assignment (correct approach for coding agents)
3. The API returns an error indicating the Copilot coding agent is not available as an assignee
4. The job treats this as a failure and exits with an error code

Why This Happens:

• Copilot coding agent assignment requires the feature to be enabled at the organization/repository level
• Not all repositories have this feature enabled
• The safe output job doesn't currently check availability before attempting assignment

Recommendations

Low Priority Issues

1. Handle Copilot Agent Unavailability Gracefully

• Priority: Low

• Root Cause: The create_issue job attempts to assign to @copilot without checking if the agent is available

• Recommended Action:

  • Add a pre-check to determine if Copilot agent is available before attempting assignment
  • Alternatively, catch the specific "not available" error and treat it as a warning rather than a failure
  • Consider logging a warning message instead of failing the entire job
  • The issue creation succeeded, so the job should be considered successful even if assignment fails

• Affected Jobs: create_issue

• Technical Approach:

  1. Before attempting assignment, query GitHub API to check if Copilot agent is enabled
  2. Or: Catch the specific error pattern "copilot coding agent is not available" and log as warning
  3. Or: Add configuration option to make assignment optional/best-effort
  4. Update job conclusion logic to mark job as successful if issue is created, regardless of assignment result

• Expected Outcome: Eliminate false failures in create_issue jobs, improving success rate to near 100%

Process Improvements

1. Distinction Between Critical and Non-Critical Failures

Current State: Any failure in a safe output job marks the entire job as failed, regardless of whether the primary objective was achieved.

Proposed: Implement tiered failure handling:

• Critical Failures: Core operation failed (e.g., issue not created, comment not posted)
• Non-Critical Failures: Post-operation tasks failed (e.g., assignment failed, label application failed)
• Warnings: Expected failures in optional operations

Benefits: More accurate success metrics and reduced alert fatigue

2. Availability Checks for Optional Features

Current State: Safe output jobs assume features like Copilot agent assignment are available.

Proposed: Implement availability checks for optional GitHub features before attempting to use them.

Benefits: Cleaner error handling and better user experience

Metrics and KPIs

• Overall Safe Output Success Rate: 97.78%
• Most Reliable Job Types: create_discussion, add_comment, create_pull_request, add_labels, push_to_pull_request_branch (100% success rate each)
• Most Problematic Job Type: create_issue (92.00% success rate, but only due to non-critical assignment failures)
• True Operational Success Rate: 100% (all primary operations succeeded)

Historical Context

This is the first automated safe output health audit. Future audits will provide trend analysis comparing with previous periods.

Baseline Metrics (2025-11-29):

• Safe output success rate: 97.78%
• Most common issue: Copilot agent assignment failures
• Zero critical failures in primary operations

Next Steps

• Review and implement graceful handling for Copilot agent unavailability
• Consider implementing tiered failure classification (critical vs. non-critical)
• Monitor create_issue job success rate after improvements
• Continue daily audits to establish trend baselines

Conclusion

The safe output job infrastructure is performing exceptionally well. The only detected failures are non-critical (assignment failures that don't affect issue creation), and all primary safe output operations completed successfully. With a simple improvement to handle unavailable features gracefully, the success rate can approach 100%.

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References:

• §19747726267

AI generated by Safe Output Health Monitor

[github-actions[bot]]

This discussion was automatically closed because it was created by an agentic workflow more than 3 days ago.