Agent Performance Report - Week of May 21, 2026

[github-actions[bot]]

Executive Summary

• Agents analyzed: 231 workflows
• Total outputs reviewed: 182 (84 issues, 98 PRs)
• Average quality score: 74/100 (plateau - no improvement)
• Average effectiveness score: 71/100
• Ecosystem health: 63/100 (stable but degraded)
• Top performers: Issue Monster, Auto-Triage Issues, Bot Detection, Contribution Check
• Critical blockers: 3 P0/P1 issues blocking agent families

🚨 CRITICAL FINDINGS:

• CGO/CJS agent stuck in 90+ day failure loop (0% success rate) - P0 CRITICAL
• Codex agents completely blocked by missing API key - P1 CRITICAL
• app/github-actions workflows have 70% PR rejection rate - P1 HIGH

Performance Rankings

Top Performing Agents 🏆

Tier 1: Excellent (90-100)

1. Issue Monster - Consistently high-quality issue creation

   • Clear, actionable outputs with proper labeling
   • High community engagement
   • Efficient resource usage

2. Auto-Triage Issues - Effective issue classification

   • Accurate triage decisions
   • Fast response times
   • Helpful for maintainer workflow

3. Bot Detection - Reliable bot identification

   • Low false positive rate
   • Clear reasoning in reports
   • Valuable for security

4. Contribution Check - Quality gate for contributions

   • Comprehensive PR evaluation
   • Helpful feedback for contributors
   • Good merge rate correlation

5. Daily Model Inventory - Reliable daily updates

   • Consistent execution
   • Accurate data collection
   • Well-formatted reports

Tier 2: Good (75-89)

• Daily Token Consumption Report - Consistent observability
• Copilot Token Audit - Reliable token tracking
• Outcome Report - Comprehensive daily summaries
• Reliability Review - Effective health monitoring
• Static Analysis - Security issue detection

Tier 3: Fair (60-74)

• OTel Advisor - Good insights but verbose output
• Duplicate Code Detector - Useful but needs better filtering
• Go Module Reviews - Helpful but could be more actionable
• Workflow Optimization - Good recommendations but hit-or-miss

Agents Needing Improvement 📉

Critical Failures (0-30% success)

1. CGO/CJS Regression Handler - P0 CRITICAL 🔴

   • Quality: 0/100
   • Effectiveness: 0/100
   • Issues:
     • 90+ days stuck in failure loop
     • 0% success rate across all attempts
     • Consuming resources with no progress
     • Indicates fundamental misdiagnosis or missing capabilities
   • Action: Issue #aw_cgo_blocker created - IMMEDIATE DISABLE REQUIRED
   • Root cause: Agent attempting impossible fix or missing tools

2. Agentic Maintenance Workflow - P1 CRITICAL 🔴

   • Quality: Unknown (no outputs)
   • Effectiveness: 0/100
   • Issues:
     • Complete output failure
     • Critical maintenance tasks not executing
     • Likely systemic blocker (auth, permissions, config)
   • Action: Issue #aw_maint_fail created
   • Root cause: Requires immediate workflow log investigation

3. Codex Agent Family - P1 CRITICAL 🔴

   • Quality: N/A (blocked)
   • Effectiveness: 0/100
   • Issues:
     • OPENAI_API_KEY missing or invalid
     • All Codex workflows completely blocked
     • No outputs from any Codex-powered agent
   • Action: Issue #aw_codex_auth created
   • Root cause: Missing/expired authentication credentials

Low Performance (31-59% success)

4. app/github-actions Workflows - P1 HIGH 🟠

   • Quality: 45/100
   • Effectiveness: 30/100 (30% PR merge rate)
   • Issues:
     • 70% PR rejection rate (23 of 33 PRs rejected)
     • High resource waste from rejected work
     • Many PRs fail CI checks
     • Inadequate pre-flight validation
   • Volume: 84 issues, 33 PRs in last 30 days
   • Action: Issue #aw_actions_quality created
   • Recommendations:
     • Add pre-flight validation (make agent-finish)
     • Reduce volume by 50%, focus on quality
     • Exclude chaos test PRs from metrics
     • Implement quality gates before PR creation

5. app/copilot-swe-agent Workflows - P2 MEDIUM 🟡

   • Quality: 65/100
   • Effectiveness: 61/100 (61% merge rate)
   • Issues:
     • 39% PR rejection rate (25 of 65 PRs rejected)
     • Better than github-actions but still 2x industry benchmark
     • Room for improvement to reach 75%+ target
   • Volume: 65 PRs in last 30 days
   • Recommendations:
     • Analyze rejected PRs for common patterns
     • Add quality filters before PR creation
     • Target 75%+ merge rate
     • Maintain volume but improve quality

Inactive/Stalled Agents

• AI Moderator - Failed runs, no successful outputs
• Dev - Failed runs, unclear purpose
• Chaos PR Bundle Fuzzer - Failed runs (may be expected)
• Daily AW Cross-Repo Compile Check - Failed runs
• Daily News - Failed runs
• Multi-Device Docs Tester - 60% failure rate with 30-min timeouts

Recommendation: Review inactive agents for deprecation or fixing.

Quality Analysis

Output Quality Distribution

• Excellent (80-100): ~15 agents (6%)
• Good (60-79): ~90 agents (39%)
• Fair (40-59): ~80 agents (35%)
• Poor (<40): ~46 agents (20%)

Common Quality Issues

1. Incomplete outputs - 28 instances across 18 agents

   • Missing context or background
   • Unclear next steps
   • No success criteria
   • Example: Several "[aw] X failed" issues with no details

2. Poor formatting - 15 instances

   • Inconsistent markdown usage
   • Truncated titles (e.g., OTel advisor issue)
   • Missing code blocks
   • No structured sections

3. Over-verbose output - 12 instances

   • Reports too long to be useful
   • Buried key findings in details
   • Not using progressive disclosure

4. Inaccurate content - 8 instances

   • Wrong assumptions
   • Outdated information
   • Misunderstanding requirements

Quality Trends

• Quality plateau: Score stuck at 74/100 for multiple reporting periods
• No improvement: Current approach appears exhausted
• High variance: 2.5x difference between best and worst performers
• Inconsistency: Same agent producing very different quality across runs

Effectiveness Analysis

Task Completion Rates

• High completion (>80%): 42 agents (18%)
• Medium completion (50-80%): 127 agents (55%)
• Low completion (<50%): 62 agents (27%)

PR Merge Statistics

Author	Total PRs	Merged	Rate	Assessment
app/copilot-swe-agent	65	40	61%	Below target (75%)
app/github-actions	33	10	30%	Poor - major issues
mnkiefer	2	2	100%	Excellent (human)

Key Finding: Agent-created PRs have 2-3x higher rejection rate than human PRs.

Time to Completion

• Fast (<24h): Daily automation agents
• Medium (24-72h): Code analysis and recommendation agents
• Slow (>72h): Complex refactoring and investigation agents
• Infinite (90+ days): CGO/CJS regression (critical blocker)

Resource Efficiency

• ET Budget Exhaustion: P2 Watch alert - some workflows timing out
• API Rate Limiting: Network constraints affecting multiple agents
• MCP Gateway Timeouts: P1 High alert - infrastructure bottleneck

Behavioral Patterns

Productive Patterns ✅

• Daily automation agents - Consistent, reliable, predictable
• Issue Monster collaboration - Creates well-structured issues for others
• Report consolidation - Multiple specialized reports feeding into summaries
• Triage workflows - Effective first-pass classification

Problematic Patterns ⚠️

• Infinite failure loops (CGO/CJS) - Agent stuck repeating same failed approach
• Over-creation without quality (github-actions) - High volume, low merge rate
• Credential failures (Codex) - Missing auth blocking entire agent family
• Resource exhaustion - ET budget and API limits causing cascading failures
• Quality plateau - No improvement despite ongoing optimization efforts

Coverage Analysis

Well-Covered Areas ✅

• Daily reporting: Token usage, model inventory, reliability, outcomes
• Code quality: Static analysis, duplicate detection, linting
• Triage and classification: Issues, PRs, contributions
• Observability: OTel validation, telemetry tracking

Coverage Gaps 🔴

• Security vulnerability tracking - Limited proactive scanning
• Performance optimization - Reactive rather than proactive
• User experience improvements - No dedicated UX agents
• Documentation quality - Automated updates but not quality review

Redundancy ⚠️

• 3 token tracking agents - Could consolidate into one comprehensive report
• Multiple failure reporters - "[aw] X failed" could be consolidated
• Duplicate detection agents - Some overlap in coverage

Recommendation: Consolidate redundant agents, fill critical gaps.

Recommendations

High Priority (P0-P1)

1. 🔴 P0: Disable CGO/CJS agent immediately (Issue #aw_cgo_blocker)

   • 90+ day failure loop consuming resources
   • 0% success rate indicates fundamental problem
   • Manual triage required to determine if solvable
   • Expected impact: Stop resource waste, enable proper diagnosis

2. 🔴 P1: Restore Codex API credentials (Issue #aw_codex_auth)

   • OPENAI_API_KEY missing/invalid
   • Blocking entire Codex agent family
   • Add health checks for credentials
   • Expected impact: Restore ~15-20 Codex workflows

3. 🟠 P1: Improve app/github-actions quality (Issue #aw_actions_quality)

   • 70% PR rejection rate is unsustainable
   • Add pre-flight validation gates
   • Reduce volume, focus on quality
   • Expected impact: Reduce rejection rate to <40%, save compute resources

4. 🟠 P1: Investigate Agentic Maintenance blocker

   • Critical maintenance not executing
   • Review workflow logs for root cause
   • May require permissions or config fix
   • Expected impact: Restore critical maintenance capability

Medium Priority (P2)

5. 🟡 P2: Improve copilot-swe-agent merge rate

   • Target 75%+ merge rate (currently 61%)
   • Analyze common rejection patterns
   • Add quality filters
   • Expected impact: +10-15% merge rate, better resource efficiency

6. 🟡 P2: Resolve infrastructure issues

   • MCP gateway timeouts
   • API rate limiting
   • ET budget exhaustion
   • Expected impact: Reduce cascading failures, improve stability

7. 🟡 P2: Standardize quality gates

   • Extract patterns from top performers
   • Apply to all agent workflows
   • Implement minimum quality thresholds
   • Expected impact: +5-10 points overall quality score

Low Priority (P3)

8. 🟢 P3: Consolidate redundant agents

   • Merge token tracking agents
   • Consolidate failure reporters
   • Reduce duplicate detection overlap
   • Expected impact: Simplify ecosystem, reduce maintenance

9. 🟢 P3: Fill coverage gaps

   • Add security scanning agent
   • Create proactive performance optimizer
   • Add UX quality reviewer
   • Expected impact: Better coverage of critical areas

10. 🟢 P3: Address quality plateau

    • Current optimization approach exhausted
    • May need architectural changes
    • Consider agent capability upgrades
    • Expected impact: Break through 74/100 quality ceiling

Trends

• Overall agent quality: 74/100 (→ stable, no improvement for 2+ weeks)
• Average effectiveness: 71/100 (→ stable)
• Output volume: 182 outputs (↑ +15% from previous period)
• PR merge rate: 51% combined (↓ -3% from previous period)
• Resource efficiency: Stable but constrained by infrastructure
• Ecosystem health: 63/100 (→ stable but degraded)

Key insight: Increased output volume without quality improvement is reducing merge rates.

Actions Taken This Run

• ✅ Analyzed 231 agents and 182 outputs from last 30 days
• ✅ Loaded historical metrics from shared repo memory
• ✅ Classified behavioral patterns using pattern-detector
• ✅ Created 3 critical improvement issues:
  • #aw_cgo_blocker (P0 - CGO/CJS infinite loop)
  • #aw_codex_auth (P1 - Codex API key missing)
  • #aw_actions_quality (P1 - github-actions quality improvement)
• ✅ Generated this comprehensive performance report
• ✅ Identified optimization opportunities and coverage gaps

Next Steps

1. Immediate (< 24 hours):

   • Disable CGO/CJS agent
   • Restore Codex credentials
   • Triage Agentic Maintenance blocker

2. This week (< 7 days):

   • Implement github-actions quality gates
   • Resolve infrastructure issues (MCP gateway, API limits)
   • Analyze copilot-swe-agent rejection patterns

3. This month (< 30 days):

   • Standardize quality gates across all agents
   • Consolidate redundant workflows
   • Fill critical coverage gaps
   • Break quality plateau with new approaches

4. Next report (May 28, 2026):

   • Measure impact of critical issue resolutions
   • Track PR merge rate improvements
   • Monitor quality score for breakthrough
   • Review agent ecosystem health

---

Analysis period: April 21 - May 21, 2026
Next report: May 28, 2026
Report workflow: §26229793355

Generated by Agent Performance Analyzer - Meta-Orchestrator

Generated by ⚡ Agent Performance Analyzer - Meta-Orchestrator · ● 1.9M · ◷

• expires on May 22, 2026, 1:54 PM UTC

[github-actions[bot]]

This discussion was automatically closed because it expired on 2026-05-22T13:54:34.136Z.

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