Agent Performance Report - Initial Baseline (Week of Dec 23-27, 2025)

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Executive Summary

This is the first run of the Agent Performance Analyzer meta-orchestrator. This report establishes a baseline framework for monitoring the health and effectiveness of all 126 agentic workflows in the repository.

Key Findings:

• Workflows analyzed: 126 total
• Safe output adoption: 120 workflows (95%)
• Meta-orchestrators: 4 (agent-performance-analyzer, campaign-manager, metrics-collector, workflow-health-manager)
• Engine distribution: Copilot 55%, Claude 20%, Codex 6%, Other/Unspecified 19%
• Status: Awaiting metrics data for quantitative analysis

Critical Dependency: The Metrics Collector workflow needs to run and populate performance data before comprehensive quality and effectiveness scoring can begin.

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Ecosystem Structure

Workflow Inventory

Total workflows in .github/workflows/: 126

Meta-Orchestrators (4):

1. agent-performance-analyzer.md - Analyzes agent quality and effectiveness (this workflow)
2. campaign-manager.md - Manages campaigns and coordinates cross-campaign activities
3. metrics-collector.md - Collects daily performance metrics for the ecosystem
4. workflow-health-manager.md - Monitors workflow health and operational status

Safe Output Adoption:

• Workflows with safe outputs: 120 (95%)
• Workflows without safe outputs: 6 (5%)

This high adoption rate indicates strong adherence to structured GitHub API interaction patterns, which is essential for:

• Rate limit management
• Consistent output formatting
• Audit trails and attribution
• Cross-workflow coordination

Engine Distribution

Engine	Count	Percentage	Notes
Copilot	70	55%	Primary engine, good for most tasks
Claude	25	20%	Used for analysis, code quality, security
Codex	8	6%	Specialized code generation tasks
Unspecified	14	11%	May be template/test workflows
Other	9	7%	Mixed or custom configurations

Analysis:

• Good engine diversity enables leveraging different AI strengths
• Copilot dominance aligns with GitHub's native integration
• Claude usage for analytical tasks shows thoughtful engine selection
• 14 workflows without engines specified should be reviewed

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Current Limitations (First Run)

Missing Data Sources

**1. Metrics Collector (redacted)

• Expected location: /tmp/gh-aw/repo-memory-default/memory/meta-orchestrators/metrics/latest.json
• Status: Not yet available (metrics-collector hasn't run)
• Impact: Cannot calculate quantitative performance scores

2. GitHub API Access:

• gh CLI not authenticated in this context
• Cannot query:
  • Recent workflow runs and success rates
  • Issues/PRs/discussions created by agents
  • Engagement metrics (reactions, comments)
  • PR merge rates and close times

3. Historical Baselines:

• No prior performance data to compare against
• Cannot identify trends (improving/declining/stable)
• Cannot detect anomalies or regressions

What We Can't Measure Yet

Without metrics data, the following analyses are pending:

Quality Metrics (0-100 scale):

• ❌ Output clarity scores
• ❌ Output completeness scores
• ❌ Output accuracy scores
• ❌ Output actionability scores

Effectiveness Metrics:

• ❌ Task completion rates
• ❌ PR merge rates
• ❌ Issue resolution rates
• ❌ Time to completion
• ❌ User engagement levels

Resource Efficiency:

• ❌ Average run times
• ❌ Safe output usage rates
• ❌ API quota consumption
• ❌ Failure rates and retry patterns

Behavioral Patterns:

• ❌ Over/under-creation detection
• ❌ Duplication analysis
• ❌ Scope creep identification
• ❌ Collaboration effectiveness

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Positive Patterns Observed

Despite limited data, several positive architectural patterns are evident:

✅ 1. High Safe Output Adoption (95%)

The ecosystem demonstrates excellent adherence to safe output patterns:

• Structured GitHub API interactions
• Consistent attribution and audit trails
• Rate limit awareness
• Coordinated cross-workflow communication

✅ 2. Well-Designed Meta-Orchestration Layer

Four specialized meta-orchestrators provide comprehensive oversight:

• Agent Performance Analyzer (this workflow): Quality and effectiveness monitoring
• Campaign Manager: Campaign coordination and resource optimization
• Metrics Collector: Centralized data collection infrastructure
• Workflow Health Manager: Operational health monitoring

This separation of concerns enables:

• Specialized expertise for different monitoring aspects
• Parallel analysis without conflicts
• Coordinated insights through shared memory
• Scalable monitoring as ecosystem grows

✅ 3. Engine Diversity

Strategic use of different AI engines:

• Copilot (55%): General-purpose tasks, GitHub integration
• Claude (20%): Analysis, security, code quality, complex reasoning
• Codex (6%): Specialized code generation

This diversity enables:

• Leveraging each engine's strengths
• Fallback options if one engine has issues
• A/B testing different approaches
• Cost optimization

✅ 4. Shared Memory Infrastructure

The repo-memory system enables:

• Persistent state across workflow runs
• Cross-orchestrator coordination
• Historical trend analysis
• Conflict resolution (current version wins)

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Framework for Future Analysis

Once metrics data is available, this workflow will perform comprehensive analysis in these areas:

Phase 1: Data Collection (10 minutes)

1. Load Metrics:

   • Read /tmp/gh-aw/repo-memory-default/memory/meta-orchestrators/metrics/latest.json
   • Extract per-workflow statistics (runs, success rates, safe outputs)
   • Load historical metrics for trend analysis

2. Gather Agent Outputs:

   • Query recent issues/PRs/comments with agent attribution
   • Collect metadata: creation dates, status, engagement
   • Build agent output profiles

3. Analyze Workflow Runs:

   • Review recent workflow logs
   • Extract agent decisions and actions
   • Record resource usage

Phase 2: Quality Assessment (10 minutes)

4. Evaluate Output Quality:

   • Sample outputs from each agent
   • Rate on clarity, accuracy, completeness, actionability (1-5 scale)
   • Calculate average quality scores

5. Assess Effectiveness:

   • Calculate task completion rates
   • Measure time-to-completion
   • Track PR merge rates
   • Evaluate user engagement

6. Analyze Resource Efficiency:

   • Calculate average run times
   • Measure safe output usage
   • Estimate API quota consumption

Phase 3: Pattern Detection (5 minutes)

7. Identify Behavioral Patterns:

   • Detect over/under-creation
   • Find duplication or redundancy
   • Identify scope creep
   • Flag inconsistent behavior

8. Analyze Collaboration:

   • Map agent interactions
   • Find productive collaborations
   • Detect conflicts

9. Assess Coverage:

   • Map agent coverage across repository
   • Identify gaps and redundancy

Phase 4: Insights and Recommendations (3 minutes)

10. Generate Insights:

    • Rank agents by quality score
    • Identify top performers and underperformers
    • Detect systemic issues

11. Develop Recommendations:

    • Specific improvements for low-performing agents
    • Ecosystem-wide optimizations
    • New agent opportunities

Phase 5: Reporting (2 minutes)

12. Create Performance Report:

    • Executive summary
    • Agent rankings and scores
    • Key findings and recommendations
    • Action items

13. Create Improvement Issues:

    • Critical agent issues
    • Systemic problems
    • Link to performance report

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Immediate Recommendations

High Priority

1. Ensure Metrics Collector Runs Successfully

• Why: All quantitative analysis depends on metrics data
• Action: Monitor metrics-collector workflow execution
• Expected Outcome: Baseline performance data available for next analysis
• Timeline: Within 24 hours

2. Establish Performance Baselines

• Why: Need benchmarks for quality and effectiveness scoring
• Action: Once metrics available, calculate:
  • Workflow success rate baselines by category
  • Safe output volume norms
  • Expected engagement levels
  • Quality score thresholds
• Timeline: After first metrics collection

3. Document Scoring Methodology

• Why: Ensure fair, consistent, objective agent evaluation
• Action: Create specification for:
  • Quality scoring criteria (clarity, accuracy, completeness, actionability)
  • Effectiveness measurements (completion rates, merge rates)
  • Efficiency benchmarks (run time, resource usage)
  • Behavioral pattern detection rules
• Timeline: Next 7 days

Medium Priority

1. Review Workflows Without Specified Engines (14 workflows)

• Why: Unspecified engines may indicate incomplete configuration
• Action:
  • Identify which workflows have no engine specified
  • Determine if intentional (test/template workflows) or oversight
  • Assign appropriate engines where needed
  • Document exceptions
• Timeline: Next 14 days

2. Standardize Safe Output Configurations

• Why: 6 workflows don't use safe outputs - understand why
• Action:
  • Review the 6 workflows without safe-outputs configuration
  • Determine if they should use safe outputs
  • Document legitimate exceptions
  • Update workflows or document rationale
• Timeline: Next 14 days

3. Set Up Cross-Orchestrator Coordination

• Why: Meta-orchestrators should share insights to avoid duplicate work
• Action:
  • Define shared memory structure in /tmp/gh-aw/repo-memory/default/
  • Create shared-alerts.md for cross-orchestrator communication
  • Establish coordination protocols
  • Document when to escalate vs. handle locally
• Timeline: Next 7 days

Low Priority

1. Create Agent Performance Dashboard

• Why: Visual representation helps identify patterns
• Action: Design dashboard showing:
  • Agent quality scores over time
  • Effectiveness trends
  • Resource usage patterns
  • Top performers and underperformers
• Timeline: After baseline data established

2. Develop Agent Improvement Templates

• Why: Standardize recommendations for common issues
• Action: Create templates for:
  • Prompt refinements
  • Tool configuration improvements
  • Timeout optimizations
  • Permission adjustments
• Timeline: After observing common patterns

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Next Steps

Immediate (Next Run)

1. **Check for Metrics (redacted)

   • Look for /tmp/gh-aw/repo-memory-default/memory/meta-orchestrators/metrics/latest.json
   • If available, begin comprehensive quantitative analysis
   • If not, create issue to troubleshoot metrics-collector

2. Coordinate with Other Meta-Orchestrators:

   • Check for campaign-manager-latest.md in shared memory
   • Check for workflow-health-latest.md in shared memory
   • Share this initial analysis via shared memory

3. Establish Baseline Framework:

   • Document quality scoring methodology
   • Define effectiveness measurements
   • Set performance thresholds

Within 7 Days

1. Complete First Quantitative Analysis:

   • Score all 126 workflows on quality (0-100)
   • Calculate effectiveness rates
   • Identify top 10 performers and bottom 10 underperformers

2. Create Improvement Roadmap:

   • Prioritize agents needing improvement
   • Estimate effort and impact
   • Create issues for critical improvements

3. Document Process:

   • Publish agent performance analysis methodology
   • Create runbooks for common scenarios
   • Share learnings with team

Within 30 Days

1. Trend Analysis:

   • Compare performance week-over-week
   • Identify improving vs. declining agents
   • Measure impact of implemented improvements

2. Ecosystem Optimization:

   • Identify consolidation opportunities
   • Recommend new agents for gaps
   • Suggest deprecation of inactive agents

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Coordination with Other Meta-Orchestrators

Campaign Manager Integration

Information Sharing:

• Campaign Manager identifies campaigns with quality issues → This workflow analyzes which agents in those campaigns need improvement
• This workflow identifies underperforming agents → Campaign Manager considers when planning campaign sequencing

Shared Alerts:

• Agents affecting campaign success
• Quality patterns requiring campaign adjustments

Workflow Health Manager Integration

Information Sharing:

• Workflow Health Manager flags failing workflows → This workflow analyzes if failures are due to agent configuration vs. systemic issues
• This workflow identifies inefficient agents → Workflow Health Manager monitors for timeout or resource issues

Shared Alerts:

• Quality issues requiring workflow fixes
• Performance patterns indicating technical problems

Metrics Collector Dependency

Critical Infrastructure:

• Metrics Collector provides raw performance data
• This workflow transforms data into insights and recommendations
• Metrics Collector must run successfully for this workflow to function

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Technical Notes

Memory Structure

This workflow uses repo memory at /tmp/gh-aw/repo-memory/default/:

Files Created:

• agent-performance-latest.md - Most recent analysis summary
• shared-alerts.md - Coordination notes for other meta-orchestrators

Files Consumed:

• metrics/latest.json - Most recent metrics (from metrics-collector)
• metrics/daily/YYYY-MM-DD.json - Historical metrics for trends
• campaign-manager-latest.md - Campaign insights
• workflow-health-latest.md - Workflow health insights

Safe Output Constraints

This workflow is configured with:

• create-discussion: max 2 - For performance reports
• create-issue: max 5 - For critical improvement recommendations
• add-comment: max 10 - For updates and coordination

Execution Schedule

• Frequency: Daily (via on: daily)
• Timeout: 30 minutes
• Permissions: Read-only (contents, issues, pull-requests, discussions, actions)

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Success Metrics for This Workflow

How we measure our own effectiveness:

1. Quality Improvement Over Time:

   • Average agent quality score trending upward
   • Reduction in agents scoring below 60/100

2. Effectiveness Increase:

   • Higher task completion rates
   • Better PR merge rates
   • Reduced time-to-completion

3. Behavioral Pattern Reduction:

   • Fewer over-creation incidents
   • Less duplication
   • Better scope adherence

4. Coverage Optimization:

   • Fewer gaps in repository coverage
   • Reduced redundancy
   • Better balance across work types

5. Recommendation Implementation:

   • High percentage of recommendations acted upon
   • Measurable improvements after interventions

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Conclusion

This initial run establishes the framework for systematic agent performance monitoring. While quantitative analysis awaits metrics data, the ecosystem shows positive structural patterns:

• ✅ High safe output adoption (95%)
• ✅ Well-designed meta-orchestrator layer
• ✅ Good engine diversity
• ✅ Shared memory infrastructure for coordination

Critical Next Step: Ensure metrics-collector workflow runs successfully to enable comprehensive performance analysis.

Next Report: After metrics data is available (expected within 24-48 hours)

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Report generated by: agent-performance-analyzer
Analysis period: Initial baseline (2025-12-27)
Next scheduled run: Daily

AI generated by Agent Performance Analyzer - Meta-Orchestrator