[workflow-analysis] Weekly Workflow Analysis - Performance and Reliability Improvements (Week of Oct 13-20, 2025)

[dsyme]

Weekly Workflow Analysis Report

Period: October 13-20, 2025
Analysis Date: October 20, 2025

Executive Summary

This analysis examined 23 workflow runs over the past week, identifying critical reliability issues and performance optimization opportunities. The most significant finding is a Docker registry service outage affecting 100% of Daily News workflow runs, causing immediate failures.

Key Metrics

• Total Runs: 23
• Success Rate: 73.9% (17 successful, 6 failed)
• Total Duration: 2.0 hours
• Total AI Cost: $6.08
• Total Tokens Used: 8.7M tokens
• Total Errors: 263
• Total Warnings: 69

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🚨 Critical Issues

1. Docker Registry Service Outage (CRITICAL)

Severity: High | Impact: Workflow Blocking | Occurrences: Multiple

Issue:

Error response from daemon: Head "(redacted)": 
received unexpected HTTP status: 503 Service Unavailable

Affected Workflows:

• Daily News (run 18647360501) - Failed completely

Root Cause: External dependency on Docker Hub experiencing service degradation/outage.

Recommendations:

1. Implement retry logic with exponential backoff for Docker pulls
2. Add fallback registries (GitHub Container Registry, alternative mirrors)
3. Pre-cache Docker images in GitHub Actions cache to avoid pulling on every run
4. Add health checks before attempting Docker operations
5. Configure timeout limits to fail fast rather than hanging

Priority: CRITICAL - Implement immediately

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2. Issue Classifier Workflow Failures

Severity: Medium | Impact: User Experience | Failure Rate: 100% (5/5 runs)

Affected Runs:

• 18638718225, 18637365936, 18637299001, 18636637320 (all failed in agent step)

Pattern: All Issue Classifier runs from Oct 19-20 failed within 37-40 seconds during the agent execution step.

Recommendations:

1. Investigate agent initialization failures
2. Review GitHub token permissions (only 4 basic permissions vs 15 for Daily News)
3. Add detailed error logging to identify root cause
4. Consider agent timeout configuration

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📊 Performance Analysis

Workflow Duration Analysis

Longest Running Workflows:

1. MCP Inspector Agent - 24.1 minutes (failed)
2. Lockfile Statistics - 10.3 minutes (success)
3. GitHub MCP Tools Report - 8.3 minutes (success)
4. Agentic Workflow Audit - 7.9 minutes (success)

Fastest Workflows:

• Issue Classifier: 37-40 seconds (all failed)
• Daily News: 44 seconds (failed)
• Changeset Generator: 2.5-2.9 minutes (successful)

Token Usage & Cost Efficiency

Top Token Consumers:

1. Agentic Workflow Audit (Test coverage console formatting august 13 #38) - 2.1M tokens, $1.11, 97 turns
2. Lockfile Statistics - 1.6M tokens, $1.30, 93 turns
3. GitHub MCP Tools Report - 1.3M tokens, $1.07, 70 turns
4. Daily Doc Updater - 1.1M tokens, $0.79, 46 turns (failed at PR creation)

Cost per Turn Analysis:

• Average: $0.014 per turn
• Most efficient: Changeset Generator ($0.010-0.016/turn)
• Least efficient: Audit workflows ($0.011-0.015/turn with high error rates)

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🔍 Failure Patterns

Error Distribution

Total Errors by Workflow:

• Daily Doc Updater: 85 errors (highest)
• Agentic Workflow Audit: 52 errors
• Lockfile Statistics: 30 errors
• Dev workflow: 16 errors

Common Error Types:

1. MCP Tool Response Size Limit (Multiple workflows)

MCP tool "search_pull_requests" response (28551 tokens) exceeds maximum allowed tokens (25000)

Affected: Daily Doc Updater, multiple audit workflows

Recommendation: Implement pagination for large result sets, filter results client-side

2. Permission Issues

• Issue Classifier has minimal permissions (4) vs successful workflows (15+)
• May be causing agent initialization failures

3. PR Creation Failures

• Daily Doc Updater completed successfully but failed during PR creation step
• Suggests post-processing/safe-outputs integration issues

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🎯 Reliability Metrics

Workflow Success Rates (Last Week)

Perfect Success Rate (100%):

• Changeset Generator: 3/3 ✅
• Q workflow: 2/3 (67% - 1 failure)
• Tidy: 2/3 (67% - 1 failure)

Complete Failure Rate (0%):

• Issue Classifier: 0/5 ❌
• Daily News: 0/1 ❌

High Success Rate:

• Lockfile Statistics: 1/1 (100%)
• GitHub MCP Tools Report: 1/1 (100%)
• Agentic Workflow Audit: 2/2 (100%)

Turn Efficiency

Most Efficient (Low Turns per Minute):

• Changeset Generator: 8-11 turns/minute
• Daily Doc Updater: 10.7 turns/minute

Least Efficient (High Turns per Minute):

• GitHub MCP Tools Report: 8.4 turns/minute
• Lockfile Statistics: 9.0 turns/minute
• Agentic Workflow Audit: 12.3 turns/minute (with 52 errors)

---

🛠️ Tool Usage Analysis

Most Used Tools:

1. GitHub MCP - 357 total calls across 8 runs
2. TodoWrite - 52 calls (good task tracking)
3. Read - 37 calls
4. Safe Outputs - 25 calls

Performance Concerns:

• gh-aw_audit tool: Max 4.8 minutes duration
• TodoWrite: Max 8.5 minutes duration (unusually long)
• Multiple bash commands for file operations (should use specialized tools)

Missing Tools:

• Python code interpreter MCP server (requested by Dev workflow)
  • Reason: "Need to execute Python code for data analysis and visualization (matplotlib) but python3 execution is blocked in bash"

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💡 Optimization Opportunities

1. Reduce Token Usage via Response Pagination

Impact: Cost reduction, improved reliability
Estimated Savings: 15-20% token reduction

Implementation:

• Add perPage parameter to all GitHub API calls (default: 30-50)
• Use page parameter for iterating through results
• Filter results client-side instead of retrieving everything

2. Improve Error Handling

Impact: Reduced error count, better debugging

Recommendations:

• Add retry logic for transient failures (network, API rate limits)
• Implement circuit breakers for external dependencies
• Add structured error logging with context
• Create error recovery strategies for common failures

3. Optimize MCP Inspector Workflow

Current: 24.1 minutes, failed
Target: <10 minutes

Actions:

• Profile slow operations
• Parallelize independent inspections
• Cache MCP server responses
• Add timeout controls

4. Fix Daily Documentation Updater PR Creation

Issue: Agent completes successfully but PR creation fails
Impact: Wasted compute, failed objectives

Investigation needed:

• Review safe-outputs integration
• Check branch creation logic
• Verify git operations sequencing

5. Implement Docker Image Caching Strategy

Impact: Eliminate Docker Hub dependency failures

Strategy:

- uses: actions/cache@v4
  with:
    path: /tmp/docker-images
    key: docker-${{ hashFiles('**/Dockerfile') }}
- run: docker load -i /tmp/docker-images/image.tar || docker pull ...

6. Add Python Code Interpreter MCP Server

Requested by: Dev workflow
Use case: Data analysis, matplotlib visualizations

Action: Install and configure Python interpreter MCP server

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📈 Trend Analysis

Warning Patterns

• Total Warnings: 69
• High warning workflows correlate with high error counts
• Warnings often precede failures (early indicators)

Concurrency & Race Conditions

• No concurrency issues detected
• Workflows properly isolated

Time-Based Patterns

• Scheduled workflows (cron) generally more successful than event-triggered
• Late evening runs (22:00-00:00 UTC) show higher failure rates
• Early morning runs (09:00-10:00 UTC) more reliable

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🎬 Action Items

Immediate (This Week)

1. ✅ Implement Docker retry logic - Critical blocker
2. ✅ Debug Issue Classifier failures - 100% failure rate unacceptable
3. ✅ Add Docker image caching - Prevent registry outages
4. ✅ Fix Daily Doc Updater PR creation - Wasted compute

Short Term (Next 2 Weeks)

5. 🔄 Implement response pagination - Reduce token usage
6. 🔄 Add Python interpreter MCP - Unblock Dev workflow
7. 🔄 Optimize MCP Inspector - Reduce 24min runtime
8. 🔄 Improve error logging - Better debugging

Long Term (Next Month)

9. 📅 Create workflow health dashboard - Real-time monitoring
10. 📅 Implement automated alerting - Proactive incident response
11. 📅 Add performance benchmarks - Track improvements
12. 📅 Conduct cost optimization review - Reduce $6/week spend

---

📋 Summary Statistics

Metric	Value	Change from Previous Week
Total Runs	23	N/A (first analysis)
Success Rate	73.9%	N/A
Average Duration	5.2 min	N/A
Total Cost	$6.08	N/A
Tokens Used	8.7M	N/A
Errors	263	N/A
Warnings	69	N/A

Most Reliable Workflow: Changeset Generator (100% success)
Least Reliable Workflow: Issue Classifier (0% success)
Most Expensive Workflow: Lockfile Statistics ($1.30)
Most Efficient Workflow: Changeset Generator ($0.24-0.38)

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🔗 References

Analyzed Workflows: 37 total workflows in repository
Log Location: /tmp/gh-aw/aw-mcp/logs
Analysis Tool: mcp__agentic_workflows (status, logs, audit commands)

Detailed Run Information:

• Failed runs audited: 18647362956, 18647360501, 18638718225
• Successful runs reviewed: 18641152766, 18638372752, 18638452768

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Next Steps

This analysis should be repeated weekly to:

1. Track improvement metrics
2. Identify new patterns
3. Validate optimization impact
4. Adjust strategies based on data

Recommended: Schedule automated weekly analysis workflow to run every Sunday at 09:00 UTC.

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Generated by Weekly Workflow Analysis Agent
Run ID: 18647455141
Analysis completed: 2025-10-20T09:08:21Z

AI generated by Weekly Workflow Analysis

[github-actions]

Agentic Issue Classifier triggered by this issue.

[pelikhan]

/q update workflow analysis to create discussions into the Audits category

[github-actions]

Agentic Q triggered by this issue comment.

[dsyme]

✅ Fix Applied

I've created a pull request to fix the audit-workflows.md configuration to properly create discussions in the Audits category.

What Was Fixed

The workflow was using:

category: "audits"

But it needed the proper GitHub Discussion category ID:

category: "DIC_kwDOPc1QR84Cwdet"  # Audits category

Why This Matters

• Category Field: The schema uses category (not category-id as shown in some documentation)
• ID Format: GitHub's GraphQL API requires the specific category ID string format
• Matching: The system matches first against IDs, then names, then slugs - using the ID ensures exact matching

Validation

✅ Workflow compiles successfully with 0 errors, 0 warnings
✅ Pull request created with detailed explanation
✅ Only source .md file modified (.lock.yml will be auto-generated)

The audit workflow will now correctly create discussions in the Audits category when it runs.

AI generated by Q for #2012