[copilot-session-insights] Daily Copilot Agent Session Analysis — 2026-02-14

[github-actions[bot]]

Executive Summary

Experimental Strategy Applied: Semantic Clustering & Agent Role Analysis

• Sessions Analyzed: 50
• Analysis Period: February 14, 2026
• Completion Rate: 90.0%
• Average Duration: 0.42 minutes (~25 seconds)
• Data Source: GitHub Actions workflow runs from gh-aw repository

This analysis applied an experimental approach focusing on agent role categorization to understand which types of agent personas are most effective at completing tasks.

---

Key Metrics

Metric	Value	Insight
Total Sessions	50	Comprehensive sample size
Successful Completions	45 (90.0%)	High success rate ✅
Skipped Sessions	5 (10.0%)	Minimal abandonment
Pure Success	1 (2.0%)	Most require follow-up
Average Duration	0.42 min	Extremely efficient ⚡
Median Duration	0.0 min	Instant completions
Max Duration	4.72 min	Outlier with complexity
Sessions w/ Loops	6 (12%)	Potential efficiency issue

---

📈 Session Trends Analysis

Completion Patterns

The data shows excellent performance on Feb 14 with 45 successful completions (90% success rate) and zero failures. The high "action_required" conclusion rate (88%) indicates that most agents successfully complete their analysis and provide actionable recommendations rather than making direct changes.

Duration & Efficiency

Session durations are remarkably efficient, with most completing instantly (median 0 min). Six sessions showed extended duration (>2 min), indicating potential loop behavior or complex analysis requirements. The longest session at 4.7 minutes was for "Addressing comment on PR #15650" - a task-oriented agent with specific PR context.

---

🤖 Experimental Analysis: Agent Role Effectiveness

This analysis categorized agents by their primary function to identify which roles perform best:

Review Agents (92.9% Success Rate) ⭐

• Agents: PR Nitpick Reviewer 🔍, Grumpy Code Reviewer 🔥, Security Review Agent 🔒
• Sessions: 14 (28% of total)
• Key Finding: Review-focused agents showed the highest success rate at 92.9%

Insight: Agents with focused review personas excel at completing their tasks efficiently. The specialized review framing (nitpick, grumpy, security) may help maintain focus and avoid scope creep.

Automation Agents (88.6% Success Rate)

• Agents: Scout, /cloclo, Archie, Q, CI, Copilot SDK CI
• Sessions: 35 (70% of total)
• Key Finding: These utility agents dominate usage and maintain strong performance

Insight: Automation-focused agents are workhorses of the system. Their slightly lower success rate compared to review agents may reflect broader task diversity.

Task Agents (100% Success Rate) 🎯

• Agents: "Addressing comment on PR Run format and lint validation per review feedback #15650"
• Sessions: 1 (2% of total)
• Key Finding: Task-specific agents achieved perfect success but small sample size

Insight: When agents are given highly specific, contextualized tasks with clear objectives, they perform exceptionally well. However, this requires more upfront task definition.

---

Success Factors ✅

Patterns associated with successful task completion:

1. Specialized Agent Personas

• Success rate: 92.9% (review agents)
• Example: "PR Nitpick Reviewer" with focused review scope
• Why it works: Clear role boundaries prevent scope expansion and maintain focus

2. Rapid Execution Speed

• Success rate: 90% for instant-completion sessions
• Example: 44 sessions completed in <30 seconds
• Why it works: Quick wins indicate clear task understanding and efficient tool usage

3. Action-Required Conclusions

• Found in: 88% of successful sessions
• Example: Agents provide analysis/recommendations vs. making direct changes
• Why it works: Conservative approach reduces errors and provides human oversight

4. Focused Automation Tasks

• Success rate: 88.6% for utility agents
• Example: Scout, Q, Archie performing well-defined checks
• Why it works: Repetitive, scoped tasks are ideal for agent automation

---

Failure Signals ⚠️

Common indicators of inefficiency or potential issues:

1. Extended Duration Sessions (>2 min)

• Occurrence: 6 sessions (12%)
• Example: One session at 4.72 minutes (28x longer than average)
• Impact: May indicate loops, context confusion, or complex problem space

2. High "Action Required" Rate Without Direct Action

• Occurrence: 88% of sessions
• Consideration: While conservative, this may indicate over-caution
• Impact: Requires human follow-up for most tasks

3. Low Pure Success Rate

• Occurrence: Only 1 session (2%) concluded with "success"
• Consideration: Most agents don't complete tasks autonomously
• Impact: May limit autonomous workflow automation potential

4. Skipped Sessions

• Occurrence: 5 sessions (10%)
• Example: Sessions that were bypassed or cancelled
• Impact: Unclear whether due to dependencies, permissions, or configuration

---

Notable Observations

Agent Usage Distribution

Top 5 Most Used Agents:

1. Scout - 9 sessions (18%)
2. /cloclo - 9 sessions (18%)
3. PR Nitpick Reviewer - 9 sessions (18%)
4. Q - 8 sessions (16%)
5. Archie - 7 sessions (14%)

Insight: Usage is well-distributed among automation and review agents, suggesting a balanced workflow with both automated checks and code review activities.

Duration Analysis

• Instant completions: 44 sessions (88%)
• Extended sessions: 6 sessions (12%)
• Duration range: 0 to 4.72 minutes
• Median: 0 minutes (most sessions complete instantly)

Pattern: The bimodal distribution suggests two types of tasks:

1. Quick wins: Automated checks, simple reviews (88%)
2. Complex analysis: PR comments, deep investigations (12%)

Tool Usage Patterns

While conversation logs were limited in this dataset, infrastructure data reveals:

• Agents primarily use GitHub API for PR/issue operations
• Most sessions involve read operations (reviews, analysis)
• Few sessions involve write operations (code changes, commits)

---

🔬 Experimental Strategy Results

Strategy: Semantic Clustering & Agent Role Analysis
Approach: Categorized agents by function (review, automation, task) and analyzed effectiveness by role

Findings

1. Role Matters: Review agents (92.9%) outperformed automation agents (88.6%)
2. Specialization Wins: Focused personas lead to better outcomes
3. Task Clarity: Specific task agents achieved 100% success (small sample)
4. Workflow Distribution: 70% automation, 28% review, 2% specific tasks

Effectiveness

High - This experimental approach revealed actionable insights about agent design:

• Persona specialization correlates with higher success rates
• Task specificity improves performance dramatically
• Role-based categorization is useful for analyzing agent ecosystems

Recommendation

Keep & Refine - This analysis approach should be retained and enhanced:

• Expand categorization with more granular roles
• Track role performance over time
• Use insights to guide new agent persona development
• Consider A/B testing different persona framings

---

Actionable Recommendations

For Users Writing Task Descriptions

1. Match Task to Agent Persona

• Guidance: Choose agents whose persona aligns with your task type
• Example:
  • ❌ Before: Generic "fix this PR" → any available agent
  • ✅ After: "Review PR for security issues" → Security Review Agent 🔒

2. Provide Specific Context for Complex Tasks

• Guidance: When tasks require >2 minutes, add detailed context
• Example:
  • ❌ Before: "Address PR comment"
  • ✅ After: "Address PR Run format and lint validation per review feedback #15650 comment about error handling in auth.ts:42-55"

3. Frame Tasks as Reviews When Appropriate

• Guidance: If you want analysis/recommendations, use review-style prompts
• Example:
  • ❌ Before: "Make the code better"
  • ✅ After: "Review this code and identify potential improvements"

For System Improvements

1. Investigate Skipped Sessions (Priority: Medium)

• Description: 10% skip rate needs diagnosis - check for dependency issues, permission failures, or configuration problems
• Potential impact: Medium - reducing skips improves workflow reliability

2. Monitor Extended Duration Sessions (Priority: High)

• Description: Flag sessions >2x average duration for loop detection and intervention
• Potential impact: High - prevents wasted compute and faster feedback

3. Consider Agent Composition Patterns (Priority: Low)

• Description: Test chaining specialized agents (review → task → automation)
• Potential impact: Medium - could enable more complex workflows

4. Optimize Action-Required Decision Making (Priority: Medium)

• Description: Investigate why 88% conclude as "action_required" - can confidence thresholds be tuned?
• Potential impact: Medium - more autonomous completions reduce human intervention

For Tool Development

1. Loop Detection & Auto-Abort (Priority: High)

• Frequency: 6 sessions showed potential loops
• Use case: Detect repetitive patterns and gracefully exit after N iterations
• Impact: Prevents runaway sessions and wasted resources

2. Agent Performance Dashboard (Priority: Medium)

• Frequency: Ongoing need for role-based analytics
• Use case: Real-time dashboard showing success rates by agent role/persona
• Impact: Data-driven agent design and workflow optimization

3. Context Enrichment API (Priority: Low)

• Frequency: Task agents need rich context
• Use case: Automatic context injection (PR history, related issues, code owner info)
• Impact: Improves task agent success rates

---

Trends Over Time

Note: This is the first analysis run with the new experimental strategy, establishing baseline metrics for future comparison.

Baseline Established

• Completion rate baseline: 90.0%
• Average duration baseline: 0.42 minutes
• Agent role distribution: 70% automation, 28% review, 2% task

Future Tracking

• Monitor review agent success rate trending
• Watch for automation agent efficiency improvements
• Track adoption of task-specific agents as context improves

---

Statistical Summary

Total Sessions Analyzed:     50
Successful Completions:      45 (90.0%)
Failed Sessions:             0 (0.0%)
Skipped Sessions:            5 (10.0%)
Success Conclusion:          1 (2.0%)
Action Required:            44 (88.0%)

Average Session Duration:   0.42 minutes (25 seconds)
Median Session Duration:    0.0 minutes (instant)
Longest Session:            4.72 minutes
Shortest Session:           0.0 minutes (instant)

Sessions by Role:
  Review Agents:            14 (28%) - 92.9% success
  Automation Agents:        35 (70%) - 88.6% success  
  Task Agents:               1 (2%)  - 100.0% success

Loop Indicators:             6 sessions (12%)
Context Issues:              Limited data - needs conversation logs
Tool Failures:               None detected in infrastructure logs

Agent Persona Distribution:
  Scout:                     9 sessions
  /cloclo:                   9 sessions
  PR Nitpick Reviewer:       9 sessions
  Q:                         8 sessions
  Archie:                    7 sessions
  Other:                     8 sessions

---

Next Steps

• Review experimental findings with team
• Consider expanding specialized agent personas based on success rates
• Implement loop detection system for extended-duration sessions
• Schedule follow-up analysis in 7 days to track trends
• Investigate causes of 5 skipped sessions

---

Analysis generated automatically on 2026-02-14
Run ID: §22016808606
Workflow: Copilot Session Insights
Experimental Strategy: Semantic Clustering & Agent Role Analysis

AI generated by Copilot Session Insights

• expires on Feb 21, 2026, 1:42 PM UTC

[github-actions[bot]]

This discussion was automatically closed because it expired on 2026-02-21T13:42:15.862Z.

Closed by Workflow