Agent Performance Report - Baseline (Week of 2026-01-02)

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Executive Summary

This is the first baseline report for the Agent Performance Analyzer meta-orchestrator. As the shared memory infrastructure was just initialized, this report establishes baseline metrics and framework for future trend analysis.

Summary Statistics:

• Agents analyzed: 176 workflow markdown files
• Active workflows: 165+ compiled workflows in GitHub Actions
• Recent activity: 6 active Copilot agent PRs (all WIP/draft)
• Metrics availability: ⚠️ Awaiting first Metrics Collector run
• Top concern: All recent PRs stuck in draft status

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Performance Rankings

🔄 Baseline Establishment Phase

Note: Comprehensive performance rankings require metrics data from the Metrics Collector workflow. This baseline report documents the ecosystem structure and establishes the framework for future analysis.

Recent Agent Activity (Last 7 Days)

Copilot Coding Agent PRs:

1. Migrate from gopkg.in/yaml.v3 to canonical go.yaml.in/yaml/v3 path #8567 - YAML dependency migration verification (Draft, 0 comments)
2. Add comprehensive test coverage for CLI completion functions #8566 - CLI completion function test coverage (Draft, 0 comments)
3. Add workflow descriptions to shell completions #8565 - Workflow name completion descriptions (Draft, 0 comments)
4. Add --completions flag to init command with automatic shell detection #8564 - Init command completions flag (Draft, 0 comments)
5. Fix awmg MCP config rewrite by including gateway section in initial config #8563 - MCP config rewrite fix (Draft, 0 comments)
6. Enable MCP gateway (awmg) in smoke-copilot-no-firewall workflow with built-in mode, fix compile timestamp handling, and enhance health check logging #8535 - MCP gateway enablement (Draft, 29 comments) ⭐

Quality Observations:

• ✅ All PRs well-documented with implementation plans
• ✅ Clear acceptance criteria and references
• ✅ PR Enable MCP gateway (awmg) in smoke-copilot-no-firewall workflow with built-in mode, fix compile timestamp handling, and enhance health check logging #8535 shows active iteration and refinement
• ⚠️ All 6 PRs remain in draft status - completion barrier?
• ⚠️ No merged agent PRs in recent period
• ⚠️ Zero movement from draft to "Ready for Review"

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Agent Ecosystem Overview

Repository Structure

• Total workflow files: 176 .md files in .github/workflows/
• Active workflows: 165 compiled .lock.yml workflows
• Workflow categories:
  • Daily/scheduled workflows: ~30+
  • Event-driven workflows: ~50+
  • Smoke test workflows: 5+
  • Meta-orchestrator workflows: 3
  • Campaign workflows: Multiple
  • Utility workflows: Code quality, docs, analysis

Agent Distribution

Based on available workflow files:

• Copilot engine: Primary engine (observed in PRs)
• Claude engine: Available but limited visible activity
• Codex engine: Available but limited visible activity
• Custom engines: Supported infrastructure

Coverage Analysis

Well-Covered Areas (observed workflow types):

• ✅ Campaign orchestration (campaign-manager, campaign-generator)
• ✅ Code health monitoring (ci-doctor, smoke-detector)
• ✅ Documentation maintenance (daily-doc-updater, glossary-maintainer)
• ✅ Repository quality (repo-quality-improver, static-analysis-report)
• ✅ Performance tracking (daily-code-metrics, lockfile-stats)

Potential Gaps (to verify with metrics):

• ❓ Security vulnerability tracking depth
• ❓ Performance optimization coverage
• ❓ User experience improvement agents

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Quality Analysis - Initial Baseline

Positive Patterns ✅

1. Excellent PR Documentation

• All Copilot agent PRs include detailed implementation plans
• Clear acceptance criteria defined upfront
• Original prompts preserved for transparency
• Consistent commit message quality

2. Iterative Improvement Example

• PR Enable MCP gateway (awmg) in smoke-copilot-no-firewall workflow with built-in mode, fix compile timestamp handling, and enhance health check logging #8535 demonstrates productive iteration (29 comments)
• Multiple validation checkpoints
• Detailed progress tracking in PR description
• Shows agent learning and refinement

3. Consistent Standards

• All PRs follow [WIP] prefix convention
• Clear issue number references (Fixes #XXXX)
• Descriptive titles following patterns

Concerning Patterns ⚠️

1. PR Completion Barriers

• All 6 PRs in draft status: None have progressed to "Ready for Review"
• Zero recent merges: No agent outputs reaching production
• Stagnation signal: Suggests workflow completion issues
• Investigation needed: What's blocking PR finalization?

2. Limited Agent Diversity

• All recent visible activity from Copilot coding agent
• No observable Claude or Codex agent PRs
• Engine diversity unclear without metrics

3. Missing Performance Data

• Cannot assess task completion rates
• Cannot measure output quality scores
• Cannot track resource efficiency
• Cannot analyze collaboration patterns

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Missing Data & Recommendations

Critical Missing Infrastructure

1. Metrics Collection System
Expected location: /tmp/gh-aw/repo-memory/default/metrics/

• latest.json - Most recent daily metrics snapshot
• daily/*.json - Historical metrics for trend analysis

Metrics Needed:

• Per-workflow safe output counts (issues, PRs, comments)
• Workflow run statistics (total, success rate, duration)
• Engagement metrics (reactions, comments, replies)
• Quality indicators (PR merge rates, issue close times)

2. Historical Baseline
Cannot establish trends without:

• Week-over-week performance changes
• Month-over-month quality improvements
• Success rate trends
• Resource usage patterns

High Priority Recommendations

1. Investigate PR Completion Barriers 🚨

• Issue: 6 PRs stuck in draft, 0% progression to review
• Action: Analyze what prevents draft → ready transition
• Questions:
  • Are agents waiting for human review?
  • Do tests fail preventing progression?
  • Is there a process gap in agent workflows?
  • Are agents lacking "completion signals"?

2. Trigger Metrics Collector Workflow

• Action: Ensure Metrics Collector runs and populates shared memory
• Purpose: Establish quantitative baseline
• Expected Output: metrics/latest.json with full ecosystem data

3. Establish Quality Baselines
Once metrics available, set targets:

• PR merge rate: >50% (industry standard)
• Issue resolution rate: >60%
• Workflow success rate: >80%
• Average time-to-merge: <7 days

Medium Priority Recommendations

1. Create Agent Category Taxonomy
Fair comparison requires categories:

• Campaign orchestrators: Different expectations than workers
• Meta-orchestrators: Different metrics than utilities
• Daily/scheduled: Different patterns than event-driven

2. Develop Coverage Map
Track agent distribution:

• What repository areas have active agents?
• What areas lack coverage?
• What areas have redundant coverage?
• What agent types serve which areas?

3. Establish Performance Scoring System
Design 0-100 scale scoring for agents:

• Clarity (20%): Output structure and readability
• Accuracy (25%): Problem-solving effectiveness
• Completeness (20%): All required elements present
• Actionability (20%): Human-actionable outputs
• Efficiency (15%): Resource usage reasonableness

Low Priority Recommendations

1. Create Agent Performance Dashboard

• Visual representation of agent health
• Trend charts for quality metrics
• Ecosystem coverage visualization

2. Implement Automated Agent Health Checks

• Daily quality score calculations
• Automatic flagging of underperformers
• Alerting for degraded performance

3. Develop Agent Benchmarking System

• Compare agents within categories
• Establish "best in class" examples
• Create improvement playbooks

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Trends

Baseline Establishment - Cannot assess trends without historical data.

Future reports will track:

• Overall agent quality score (0-100)
• Average effectiveness score (0-100)
• Output volume (week-over-week %)
• PR merge rate (%)
• Resource efficiency (average runtime)

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Actions Taken This Run

1. ✅ Analyzed repository structure (176 workflows, 165 active)
2. ✅ Reviewed recent agent activity (6 Copilot PRs identified)
3. ✅ Documented positive patterns and concerns
4. ✅ Identified critical missing metrics infrastructure
5. ✅ Created comprehensive baseline report framework
6. ✅ Established recommendations for next steps
7. ✅ Attempted to initialize shared memory (permission issues noted)

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Next Steps

Immediate Actions (Next 24-48 Hours)

1. Resolve shared memory permissions for /tmp/gh-aw/repo-memory/default/
2. Trigger Metrics Collector workflow to populate baseline data
3. Monitor PR Enable MCP gateway (awmg) in smoke-copilot-no-firewall workflow with built-in mode, fix compile timestamp handling, and enhance health check logging #8535 as the most active agent output
4. Investigate PR completion barriers - why 100% draft rate?

Next Run (After Metrics Available)

1. Load and analyze metrics from metrics/latest.json
2. Calculate quality scores (0-100) for each active agent
3. Rank agents by performance within categories
4. Create specific improvement issues for underperformers
5. Document collaboration patterns between agents
6. Establish week-over-week trend baselines

Ongoing Coordination

• Campaign Manager: Monitor campaign-associated workflow performance
• Workflow Health Manager: Coordinate on workflows with health issues
• Shared Alerts: Populate coordination notes once patterns emerge

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Framework for Future Reports

This baseline establishes the structure for comprehensive analysis:

Agent Quality Scoring (0-100)

Clarity Score (20% weight):

• Output structure and organization
• Markdown formatting consistency
• Code block and formatting quality

Accuracy Score (25% weight):

• Problem-solving effectiveness
• Root cause analysis quality
• Solution appropriateness

Completeness Score (20% weight):

• All required elements present
• Acceptance criteria met
• Documentation included

Actionability Score (20% weight):

• Clear next steps provided
• Human-actionable recommendations
• Appropriate level of detail

Efficiency Score (15% weight):

• Resource usage reasonableness
• Execution time appropriateness
• API quota consumption

Behavioral Pattern Detection

• Over-creation: Creating too many outputs
• Under-creation: Not producing expected outputs
• Repetition: Creating duplicate work
• Scope creep: Exceeding defined boundaries
• Collaboration: Building on other agent work
• Conflicts: Undoing other agent work

Ecosystem Health Indicators

• Agent coverage distribution
• Engine diversity utilization
• Output volume trends
• Collaboration effectiveness
• Gap identification
• Redundancy detection

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Conclusion

This baseline report establishes the Agent Performance Analyzer framework. The immediate priority is resolving metrics infrastructure to enable quantitative analysis. The concerning pattern of 100% draft PRs (6/6) requires investigation.

Status: ✅ Framework established, ⚠️ Awaiting metrics infrastructure
Next Report: After Metrics Collector populates baseline data
Priority: Investigate PR completion barriers (6 drafts, 0 merged)

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Analysis Period: Repository baseline establishment (2026-01-02)
Next Report: After metrics data available
Meta-Orchestrator: Agent Performance Analyzer
Run: https://github.com/githubnext/gh-aw/actions/runs/20651212677

AI generated by Agent Performance Analyzer - Meta-Orchestrator