Agentic Workflow Audit Report - October 13, 2025 (Evening)

[github-actions[bot]]

🔍 Agentic Workflow Audit Report - October 13, 2025 (Evening)

Audit Summary

• Period: Last 24 hours (ending 02:59 UTC)
• Runs Analyzed: 18
• Workflows Active: 6 (Audit Agent, Changeset Generator, Tidy, Dev)
• Success Rate: 100% ✅
• Total Cost: $3.84
• Total Tokens: 5,731,289
• Issues Found: 2 genuine issues + 165 false positives

🎯 Key Findings

✅ Excellent Overall Health

All 18 workflow runs completed successfully with a perfect 100% success rate. System performance is strong with good efficiency and task tracking.

⚠️ Genuine Issues Identified

Issue 1: MCP Status Tool Token Limit Exceeded (Medium Priority)

Problem: The gh-aw MCP server's status tool returns 40,657 tokens, exceeding the 25,000 token limit.

Impact:

• Causes tool call failures in Agentic Workflow Audit Agent
• Forces users to manually add jq filters to every status call
• Affected 2 workflow runs

Recommendation:

• Add default concise output format to status tool
• Return minimal information by default (workflow name, status, agent)
• Allow optional verbose flag for full details
• This would reduce token usage by ~15,000 tokens per call

Workaround: Currently using jq parameter to filter output (e.g., .[] | {workflow, agent, status})

Issue 2: Error Detection Pattern False Positives (Medium Priority)

Problem: 97.6% of detected "errors" (165 out of 169) are false positives from JSON log messages.

Examples of False Positives:

• JSON log messages containing "error": fields
• Code snippets with catch (error) statements
• Environment variable content with error patterns

Impact:

• Creates noise in audit reports
• Makes it difficult to identify genuine issues
• Wastes analysis time reviewing non-issues

Recommendation:

• Refine error detection regex patterns
• Exclude JSON-formatted log messages
• Exclude code snippets and error handling patterns
• Focus on actual runtime errors and failures

📊 Workflow Performance Analysis

Workflow	Runs	Success	Avg Cost	Avg Duration	Notes
Changeset Generator	7	100%	$0.30	2.3m	Efficient PR review workflow
Tidy	6	100%	$0.00	4.0m	Simple cleanup, no AI calls
Audit Agent	4	100%	$0.86	7.1m	Complex analysis, high token usage
Dev	1	100%	$0.00	1.5m	Fast deployment workflow

💰 Cost Analysis

Total Spend (24h): $3.84

Cost Breakdown:

• Agentic Workflow Audit Agent: $1.72 (44.7%) - 2 completed runs
• Changeset Generator: $2.11 (54.9%) - 7 runs
• Tidy: $0.00 (0%) - Non-AI workflow
• Dev: $0.00 (0%) - Non-AI workflow

Cost Efficiency:

• Changeset Generator is cost-effective at $0.30/run for PR reviews
• Audit Agent is expensive at $0.86/run but provides valuable insights
• Consider optimizing Audit Agent analysis patterns for 20-30% cost reduction

🔧 Tool Usage Highlights

• github: 600 calls across 6 runs (heavy GitHub API usage in PR reviews)
• TodoWrite: 84 calls across 9 runs (excellent task tracking)
• gh-aw audit: 16 calls for detailed workflow analysis
• safe_outputs: 18 calls for secure output operations

Missing Tools Analysis

Total Reports: 40 (all false positives ❌)

False Positive #1: permission-restricted-tool (29 reports)

• Workflows: Agentic Workflow Audit Agent
• Root Cause: Log parser detecting permission messages as missing tool requests
• Impact: None - workflows completed successfully
• Note: Not an actual tool, but log parsing artifact

False Positive #2: github-copilot-cli (11 reports)

• Workflows: Agentic Workflow Audit Agent
• Root Cause: Log parser detecting environment variable content as missing tools
• Impact: None - workflows completed successfully
• Note: These are from GITHUB_AW_ERROR_PATTERNS environment variable content

📈 Historical Trends

Compared to previous audit (2025-10-13 morning):

• Activity: +80% (10 → 18 runs) 📈
• Cost: +61% ($2.38 → $3.84) 📈
• Success Rate: Stable at 100% ✅
• Assessment: Increased activity, all systems healthy

🎯 Recommendations

Priority: Medium

1. Add Default Filtering to MCP Status Tool

   • Implement concise output format by default
   • Add optional --verbose flag for full details
   • Estimated impact: Save ~15,000 tokens per call

2. Improve Error Detection Patterns

   • Refine regex to exclude JSON logs and code snippets
   • Focus on actual runtime errors
   • Estimated impact: Reduce false positives by 97%

Priority: Low

3. Fix Missing Tool Detection Logic

   • Update log parser to avoid detecting permission messages as missing tools
   • Cleaner, more accurate audit reports

4. Monitor Audit Agent Costs

   • At $0.86/run with multiple daily executions, consider optimization
   • Potential 20-30% cost reduction through pattern refinement

🏁 Conclusion

Overall Status: Excellent ✅

The agentic workflow system is operating at peak efficiency with 100% success rate across all 18 runs. The identified issues are:

1. MCP status tool token limit - easily addressable with default filtering
2. Error detection false positives - creates noise but doesn't impact functionality

No missing tools require implementation. All "missing tool" reports are false positives from log parsing.

The system is healthy, cost-efficient for the value provided, and all workflows are completing successfully. The recommendations above would improve audit accuracy and reduce token usage but are not critical for operation.

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Audit Performed By: Agentic Workflow Audit Agent
Cache Updated: /tmp/gh-aw/cache-memory/audits/2025-10-13-evening.json
Next Audit: Scheduled for next cycle

AI generated by Agentic Workflow Audit Agent

[pelikhan]

/q collect a few runs and create a plan to fix the error log parsing false positives

[github-actions[bot]]

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