🔍 Agentic Workflow Audit Report - 2025-11-23

[github-actions[bot]]

🔍 Agentic Workflow Audit Report - 2025-11-23

This audit covers workflow runs from the last 24 hours, analyzing 94 agentic workflow executions for health, performance, errors, and missing tools.

Executive Summary

Over the past 24 hours, the agentic workflow system demonstrated strong overall health with an 88.3% success rate. The system processed 33.1M tokens with an estimated cost of $23.38. While most workflows executed successfully, several areas require attention, including missing tool requests (primarily Playwright in Copilot context) and a few failing workflows.

Key Highlights:

• 94 workflow runs analyzed (83 successful, 5 failed, 4 cancelled, 2 skipped)
• 88.3% success rate across all workflows
• 11 missing tool requests identified across 5 different tools
• 0 MCP server failures detected
• Top consumer: Go Logger Enhancement (6.4M tokens, $2.89)

📈 Workflow Health Trends

Success/Failure Patterns

The trend chart shows consistent workflow execution over the past 4 days with generally high success rates. The most recent day (Nov 22) shows the highest activity with 40+ successful runs and a success rate above 90%. The pattern indicates healthy, stable operations with occasional failures that are being appropriately handled.

Token Usage & Costs

Token consumption shows significant variation by day, with Nov 21 seeing the highest usage (10M+ tokens, $5+ estimated cost). The 7-day moving averages smooth out the volatility, revealing a general upward trend in resource utilization. This suggests increasing workflow complexity or frequency, warranting monitoring for cost optimization opportunities.

Full Audit Report

Audit Methodology

Period: Last 24 hours (November 22-23, 2025)
Runs Analyzed: 94 workflow executions
Data Sources: GitHub Actions workflow run logs, MCP server logs, agent stdio logs
Tools Used: gh-aw MCP server, Python analysis scripts, pandas, matplotlib, seaborn

Missing Tools Analysis

Missing tool requests indicate functionality that agents attempted to use but was unavailable in their execution environment.

Summary

Tool	Request Count	Affected Workflows	Status
playwright	7	Smoke Copilot	⚠️ Tool not available in Copilot context
Python Scientific Libraries	1	Daily Firewall Logs Collector and Reporter	⚠️ Libraries not installed
pip/pip3 package installer	1	Daily Firewall Logs Collector and Reporter	⚠️ Package manager not available
gh CLI (GitHub CLI)	1	Daily Firewall Logs Collector and Reporter	⚠️ CLI not authenticated
GitHub CLI authentication	1	Daily Firewall Logs Collector and Reporter	⚠️ Auth issue in Copilot context

Detailed Analysis

Playwright MCP Tool (7 requests)

• Affected: Smoke Copilot workflow
• Issue: The Playwright MCP server is not available in the Copilot agent execution context
• Impact: Browser automation smoke tests cannot be performed by Copilot agents
• Recommendation: Either enable Playwright MCP for Copilot agents or migrate smoke tests requiring browser automation to Claude/Codex engines

Python Scientific Libraries (1 request)

• Affected: Daily Firewall Logs Collector and Reporter
• Issue: pandas, matplotlib, seaborn, and numpy are not available in the agent environment
• Impact: Cannot generate data analysis and visualizations for firewall logs
• Recommendation: These libraries are now available via the Python environment setup. The workflow should be updated to use the new /tmp/gh-aw/python/ directory structure.

Package Manager & GitHub CLI (3 requests)

• Affected: Daily Firewall Logs Collector and Reporter
• Issue: pip installer and gh CLI authentication unavailable
• Impact: Cannot install dependencies or authenticate with GitHub API
• Recommendation: Workflow should use pre-installed tools and MCP GitHub tools instead of gh CLI

MCP Server Failures

✅ No MCP server failures detected during the audit period. All configured MCP servers (GitHub, Playwright, gh-aw, safeoutputs) functioned correctly.

Error Analysis

Failed Workflows

5 workflows failed during the audit period:

1. Copilot PR Conversation NLP Analysis

• Run ID: §19566933497
• Errors: 55 error log entries
• Analysis: High error count suggests workflow encountered significant issues during execution. Most errors appear to be JSON log entries rather than actual failures, but investigation recommended.

2. Glossary Maintainer

• Run ID: §19567040121
• Errors: 3 error log entries
• Analysis: Relatively low error count suggests a specific issue rather than systemic failure.

3. Changeset Generator (2 failures)

• Run IDs: §19584497219, §19586088247
• Errors: 5 error log entries each
• Analysis: Repeated failures suggest a persistent issue with this workflow that needs investigation.

4. Tidy

• Run ID: §19590718399
• Errors: 2 error log entries
• Analysis: Single failure with minimal errors, likely a transient issue.

Error Patterns

Most "errors" in the logs are actually JSON-formatted log entries from the agent stdio logs and do not represent actual failures. The log collection system captures these as errors due to their format. True errors need manual investigation of the specific workflow run logs.

Performance Metrics

Token Usage Statistics

Metric	Value
Total Tokens (24h)	33,140,498
Average per Run	352,558
Highest Single Run	6,447,819 (Go Logger Enhancement)
Total Estimated Cost	$23.38
Average Cost per Run	$0.25

Top Token Consumers

Workflow	Tokens	Cost	Runs
Go Logger Enhancement	6,447,819	$2.89	1
Static Analysis Report	6,416,494	$3.25	2
Semantic Function Refactoring	3,386,126	$2.26	2
Go Pattern Detector	2,583,804	$2.85	15
Instructions Janitor	2,505,955	$1.81	2

Workflow Success Rates

Most workflows maintained high success rates. Notable statistics:

• Go Pattern Detector: 15 runs, 100% success rate
• Smoke Claude: 7 runs, 100% success rate
• Static Analysis Report: 2 runs, 100% success rate
• Multi-Device Docs Tester: 2 runs, 100% success rate

Duration Analysis

Job durations were reasonable across all workflows, with most completing within expected timeframes. No timeout issues detected.

Workflow Statistics by Type

By Engine

Based on available data:

• Claude workflows: Generally longer execution times but higher success rates
• Copilot workflows: Faster execution but some missing tool issues (Playwright)
• Codex workflows: Stable performance across analyzed runs

High-Frequency Workflows

These workflows ran most frequently in the past 24 hours:

1. Go Pattern Detector: 15 runs (100% success)
2. Smoke Claude: 7 runs (100% success)
3. Static Analysis Report: 2 runs (100% success)
4. Multi-Device Docs Tester: 2 runs (100% success)

Recommendations

Priority 1: Critical

1. Investigate Changeset Generator Failures

   • Two consecutive failures indicate a systematic issue
   • Review error logs and fix underlying problems
   • Consider adding retry logic or better error handling

2. Address Copilot PR NLP Analysis High Error Rate

   • 55 errors in a single run is concerning
   • Validate that these are log entries vs. actual errors
   • If actual errors, investigate root cause

Priority 2: High

3. Resolve Missing Tool Issues

   • Enable Playwright MCP for Copilot agents OR migrate browser tests to Claude/Codex
   • Update Daily Firewall Logs Collector to use new Python scientific stack
   • Replace gh CLI usage with MCP GitHub tools

4. Optimize High Token Consumers

   • Review Go Logger Enhancement workflow for efficiency opportunities (6.4M tokens)
   • Consider breaking large analysis tasks into smaller chunks
   • Implement caching for repeated operations

Priority 3: Medium

5. Monitor Token Usage Trends

   • Daily costs are increasing (Nov 21: $5.43, trending upward)
   • Set up alerts for unusual token consumption
   • Review workflows exceeding 1M tokens per run

6. Improve Error Logging

   • Current system captures JSON logs as "errors"
   • Implement better log parsing to distinguish real errors from log entries
   • Add structured error reporting

Priority 4: Low

7. Document Successful Patterns
   • Go Pattern Detector shows excellent reliability (15/15 success)
   • Document and share best practices from successful workflows
   • Use as templates for new workflow development

Historical Context

This is the first comprehensive audit using the new gh-aw MCP server infrastructure. Historical data is being collected for trend analysis:

• Audit data stored: /tmp/gh-aw/cache-memory/audits/2025-11-23.json
• Pattern database: /tmp/gh-aw/cache-memory/patterns/
• Future audits will include:
  • Week-over-week comparisons
  • Long-term cost trends
  • Pattern evolution tracking
  • Predictive failure analysis

Affected Workflows Summary

Workflows with Issues:

1. Copilot PR Conversation NLP Analysis (failed)
2. Glossary Maintainer (failed)
3. Changeset Generator (failed 2x)
4. Tidy (failed)
5. Smoke Copilot (missing tools)
6. Daily Firewall Logs Collector and Reporter (missing tools)

High-Performing Workflows:

1. Go Pattern Detector (15 runs, 100% success)
2. Smoke Claude (7 runs, 100% success)
3. Static Analysis Report (2 runs, 100% success)
4. Semantic Function Refactoring (2 runs, 100% success)
5. Multi-Device Docs Tester (2 runs, 100% success)

Next Steps

• Complete audit of last 24 hours
• Generate trend charts
• Store findings in cache memory
• Open issues for Priority 1 items
• Schedule follow-up investigation for failed workflows
• Update Daily Firewall Logs workflow to use Python stack
• Configure Playwright for Copilot or migrate smoke tests
• Implement cost monitoring alerts
• Review next audit in 24 hours

Conclusion

The agentic workflow system is operating with good overall health (88.3% success rate) but has specific areas requiring attention. The missing tools issues are addressable through configuration or workflow updates. Failed workflows need individual investigation. Token consumption is within reasonable bounds but trending upward, suggesting a need for ongoing monitoring and optimization.

The new audit infrastructure with trend visualization provides excellent visibility into system health. Continued daily audits will build historical context and enable predictive maintenance.

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References:

• §19570000940 - Go Logger Enhancement (highest token usage)
• §19566933497 - Copilot PR NLP Analysis (failed with 55 errors)
• §19584497219 - Changeset Generator (failed)

AI generated by Agentic Workflow Audit Agent

[github-actions[bot]]

This discussion was automatically closed because it was created by an agentic workflow more than 1 week ago.