🔍 Agentic Workflow Audit Report - November 9, 2025 #3509
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/q remove deepwiki and context7 MCPs imports |
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Agentic Q triggered by this discussion comment. |
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This discussion was automatically closed because it was created by an agentic workflow more than 1 week ago. |
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🔍 Agentic Workflow Audit Report - November 9, 2025
This audit analyzes 95 agentic workflow runs from the last 5 days (November 5-9, 2025) to identify issues, track performance, and provide actionable recommendations for improvement.
Executive Summary
The audit reveals a healthy overall workflow ecosystem with an 87.37% success rate across 95 runs. However, specific workflows show recurring failures that require attention, particularly the Duplicate Code Detector (4 failures) and PR Nitpick Reviewer (2 failures). Two MCP server failures were detected in the Scout workflow, and one missing tool request was identified in the Daily Firewall Logs workflow.
Key Highlights:
📈 Workflow Health Trends
Success/Failure Patterns
The trend chart shows workflow health over the last 5 days. Notable observations:
Token Usage & Costs
Note: Token usage and cost data are not being recorded in the workflow run summaries. This represents a monitoring gap that should be addressed to track resource consumption and optimize costs.
Full Audit Report
Audit Statistics
Event Type Distribution
The workflow runs were triggered by various event types:
Observation: Scheduled workflows represent the largest category (45.3%), followed by push events (24.2%). This indicates heavy reliance on automated, time-based workflows.
Failed Workflows Analysis
Failure Breakdown by Workflow
Critical Failures Requiring Investigation
1. Duplicate Code Detector (4 failures)
Pattern: All 4 runs failed with no error details captured in metrics.
Analysis: This workflow has a 100% failure rate during the audit period. The failures occurred across both scheduled and manual triggers, suggesting a systemic issue rather than a timing or trigger-specific problem.
Recommendation:
2. PR Nitpick Reviewer 🔍 (2 failures)
Analysis: Both failures occurred on pull request events targeting branches with the "copilot/" prefix. This may indicate an issue with the workflow handling specific branch patterns or PR characteristics.
Recommendation:
3. Scout (1 failure with MCP issues)
Analysis: This failure coincided with 2 MCP server failures (deepwiki and context7), suggesting the failure was caused by unavailable external dependencies.
Recommendation:
Missing Tools
Total Reports: 1
Analysis: The Daily Firewall Logs workflow requested Python data visualization libraries that are not currently available in the execution environment. This aligns with the Python Data Visualization Guide in the workflow instructions, which assumes these libraries are installed.
Recommendation:
MCP Server Failures
Total Failures: 2 (both in the same run)
Analysis: Both MCP server failures occurred in the same Scout workflow run. The deepwiki and context7 servers are external dependencies used for research and context gathering. Their simultaneous failure suggests either:
Recommendation:
Performance Metrics
Token Usage and Cost
Status:⚠️ No data available
All workflow runs in the audit period show 0 tokens used and $0 estimated cost. This indicates that token usage metrics are not being properly captured or recorded.
Impact:
Recommendation:
Duration Analysis
Average Duration: Unable to calculate (many runs show 0 duration)
Longest Run: Data incomplete
Observation: Duration metrics are also inconsistently recorded, which limits our ability to identify performance bottlenecks or timeout issues.
Workflows Currently In Progress
Two workflows were still running at the time of the audit:
Tool Usage Statistics
Status: No tool usage data available in logs
The workflow run summaries do not contain detailed tool usage statistics, which would be valuable for:
Recommendation: Enable tool usage tracking in the workflow engine configuration.
Recommendations
Immediate Actions (Priority: High)
Fix Duplicate Code Detector - Investigate and resolve the 100% failure rate
Address MCP Server Reliability - Implement resilience for Scout workflow
Install Python Visualization Libraries - Enable firewall reporting
Short-term Actions (Priority: Medium)
Enable Token Usage Tracking - Critical for cost monitoring
Fix PR Nitpick Reviewer - Resolve copilot/* branch issues
Improve Metrics Collection - Better observability
Long-term Actions (Priority: Low)
Optimize Scheduled Workflows - Reduce unnecessary executions
Implement Proactive Monitoring - Catch issues early
Historical Context
This is a recurring audit. Previous audit data is available in the cache memory of past workflow runs, particularly run §19200772955, which contains historical audit records dating back to October 12, 2025.
Trend Analysis: Based on available data, the repository maintains a relatively stable 85-90% success rate, with occasional spikes in failures related to specific workflows. The current 87.37% success rate is within the historical norm.
Conclusion
The githubnext/gh-aw repository demonstrates a mature agentic workflow ecosystem with good overall health. The 87.37% success rate is commendable, though there's room for improvement to reach the target 90% threshold.
Priority focus areas:
The audit process itself is functioning well, with comprehensive log collection and analysis capabilities. The main limitation is the lack of detailed metrics (tokens, costs, tool usage) in the workflow run data, which should be addressed to enable deeper performance analysis.
Next Steps
References:
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