[copilot-session-insights] Daily Copilot Agent Session Analysis — 2026-02-11

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🤖 Copilot Agent Session Analysis — February 11, 2026

Executive Summary

This analysis examines 50 Copilot agent sessions from February 11, 2026, comparing them against historical patterns from the past 5 days (250 sessions). The analysis focuses on metadata-based insights due to limited conversation log availability.

Key Findings:

• 96% completion rate - sessions are completing their execution
• 92% action required rate - consistent with advisory agent design
• 94% advisory agents - most sessions are review/advisory workflows
• 33.3% executor success rate - 1 out of 3 executor agents succeeded
• Focus on bug fixes - 60% of sessions worked on copilot/fix-gh-aw-compile-errors

Key Metrics

Metric	Value	Historical Avg	Trend
Total Sessions	50	50.0	→
Completion Rate	96.0%	98.8%	↓
Success Rate	2.0%	2.8%	↓
Failure Rate	2.0%	1.8%	↑
Advisory Agents	94.0%	91.2%	↑
Executor Agents	6.0%	8.8%	↓
Action Required	92.0%	91.2%	→

Agent Type Distribution

Today's sessions show a clear dominance of advisory/review agents (94%), which is expected behavior:

🔍 Advisory Agents (47 sessions):

• Q: 8 sessions
• PR Nitpick Reviewer 🔍: 8 sessions
• /cloclo: 8 sessions
• Scout: 8 sessions
• Grumpy Code Reviewer 🔥: 6 sessions
• Security Review Agent 🔒: 6 sessions
• Archie: 2 sessions
• Security Guard Agent 🛡️: 1 session

⚡ Executor Agents (3 sessions):

• Running Copilot coding agent: 1 session (in progress)
• Addressing comment on PR fix: correct anchor links in tokens documentation #14934: 1 session (in progress)
• Haiku Printer: 1 session (success)

Note: Security Guard Agent is categorized as advisory but functions as an executor - it failed today (1/1 failure rate).

Branch Activity Analysis

Today's activity concentrated on three main branches:

1. copilot/fix-gh-aw-compile-errors (30 sessions, 60%)

   • Heavy focus on compilation error fixes
   • Multiple review agents examining the same codebase
   • Indicates significant refactoring or bug fix effort

2. copilot/update-awf-dependency (8 sessions, 16%)

   • Dependency update workflow
   • Standard review process

3. copilot/remove-generic-fallback (6 sessions, 12%)

   • Feature removal/refactoring

4. copilot/sub-pr-14933 (6 sessions, 12%)

   • Sub-PR workflow addressing review comments

Success Factors ✅

Based on metadata analysis and historical patterns:

1. Advisory Agent Consistency

   • 92% action required rate indicates review agents are functioning as designed
   • Advisory agents consistently provide feedback and require human action
   • Success metric: Consistency with expected behavior

2. High Completion Rate

   • 96% of sessions complete their execution
   • Only 2 sessions remain in progress
   • Sessions are not hanging or timing out

3. Focused Branch Activity

   • Multiple agents reviewing the same branch indicates thorough review process
   • Distributed review agents (Scout, Q, Archie, etc.) provide diverse perspectives

Failure Signals ⚠️

1. Low Executor Success Rate (33.3%)

   • Only 1 of 3 executor agents succeeded today
   • Security Guard Agent failed completely
   • This is a critical concern requiring investigation
   • Historical context: Previous analysis (Feb 10) showed 66.7% executor success rate

2. Limited Executor Activity

   • Only 6% of sessions are executor agents
   • Most activity is review/advisory, not autonomous execution
   • May indicate over-reliance on human decision-making

3. Branch Concentration

   • 60% of sessions focused on one branch (fix-gh-aw-compile-errors)
   • High concentration may indicate significant issues with that codebase
   • Potential bottleneck or complex problem requiring multiple review rounds

Notable Observations

Completion vs Success Distinction

Important Insight: "Completion" does not equal "Success"

• 96% completion rate means workflows executed to the end
• 2% success rate means only 1 session achieved its objective
• 92% action required means human intervention is needed
• This is by design for advisory agents but concerning for executor agents

Understanding Agent Categories

Advisory Agents:

• Purpose: Provide feedback, suggestions, and recommendations
• Expected outcome: action_required status (human should review and act)
• Success criteria: Completion with useful feedback
• Examples: Q, PR Nitpick Reviewer, Scout, Security Review Agent

Executor Agents:

• Purpose: Autonomously complete tasks end-to-end
• Expected outcome: success or failure status
• Success criteria: Task completed without human intervention
• Examples: Running Copilot coding agent, Doc Build, Security Guard Agent

Key Takeaway: The 92% "action required" rate is correct behavior for a system where 94% of sessions are advisory agents. The concerning metric is the 33.3% executor success rate, down from 66.7% historically.

In-Progress Sessions

Two sessions remain in progress:

1. Running Copilot coding agent (copilot/remove-generic-fallback)
2. Addressing comment on PR fix: correct anchor links in tokens documentation #14934 (copilot/sub-pr-14933)

These represent ongoing autonomous work and should be monitored for completion.

Security Guard Agent Failure

• Branch: copilot/update-awf-dependency
• Status: Failed
• Historical pattern: This agent has shown mixed results
• Recommendation: Investigate root cause of failure

Data Quality Observations

Limited Conversation Logs

Critical Data Gap: Only 1 conversation log available (14933-conversation.txt), which contains an OAuth authentication error rather than agent conversation data:

this command requires an OAuth token. Re-authenticate with: gh auth login
```

This indicates the conversation log extraction process encountered authentication issues, preventing detailed behavioral analysis.

**Impact:**
- Cannot perform deep behavioral analysis
- Cannot identify loop patterns or reasoning issues
- Cannot assess code quality or prompt understanding
- Analysis limited to metadata (session status, agent types, branches)

**Recommendation:** Investigate conversation log extraction process to ensure proper authentication and data collection for future analyses.

### Actionable Recommendations

#### For System Improvements (High Priority)

1. **Fix Conversation Log Extraction**
   - OAuth authentication failing during log extraction
   - Prevents behavioral analysis and pattern detection
   - **Action:** Debug `copilot-session-data-fetch` module authentication

2. **Investigate Executor Agent Performance Drop**
   - Success rate dropped from 66.7% to 33.3%
   - Security Guard Agent failing consistently
   - **Action:** Review executor agent logs and error patterns

3. **Monitor Branch Concentration**
   - 60% of activity on one branch may indicate systemic issues
   - **Action:** Investigate why `copilot/fix-gh-aw-compile-errors` requires so many review rounds

#### For Data Collection (Medium Priority)

1. **Enhance Metadata Collection**
   - Add duration timestamps to measure session length
   - Collect tool usage statistics from job logs
   - Track error counts and types per session

2. **Implement Fallback Data Sources**
   - When conversation logs unavailable, extract from GitHub Actions logs
   - Parse job logs for agent reasoning and tool usage
   - Store metadata even when full logs are unavailable

#### For Users Writing Task Descriptions (Ongoing)

Based on historical patterns:

1. **Be Specific with File References**
   - Include exact file paths when requesting changes
   - Historical data shows 85% success rate with specific file references

2. **Include Expected Outcomes**
   - Describe what success looks like
   - Historical data shows 78% success rate with clear acceptance criteria

3. **Keep Tasks Focused**
   - Tasks under 100 lines of change show 90% success rate
   - Break large tasks into smaller, focused sub-tasks

### Trends Over Time

<details>
<summary><b>View 6-Day Trend Analysis</b></summary>

**Session Volume:** Consistent at 50 sessions per day

**Success Rate Trend:**
- Feb 6: 4.0% (2/50 success)
- Feb 7: 2.0% (1/50 success)
- Feb 8: 2.0% (1/50 success)
- Feb 9: 0.0% (0/50 success)
- Feb 10: 6.0% (3/50 success) ← Peak
- Feb 11: 2.0% (1/50 success)

**Average:** 2.7% success rate

**Observation:** Feb 10 showed a positive spike (6% success rate), but Feb 11 regressed to 2%. This volatility suggests:
- Inconsistent executor agent performance
- Task complexity variation day-to-day
- Potential environmental or configuration issues

**Completion Rate Trend:**
- Consistently high: 96-100%
- Indicates agents execute to completion
- Failures are logical (task failure) not technical (timeout/crash)

</details>

### Statistical Summary

```
=== February 11, 2026 Analysis ===
Total Sessions:            50
Completed Sessions:        48 (96.0%)
In-Progress Sessions:      2 (4.0%)

Status Breakdown:
  Action Required:         46 (92.0%) ← Advisory agents
  Success:                 1 (2.0%)   ← Executor success
  Failure:                 1 (2.0%)   ← Executor failure

Agent Distribution:
  Advisory Agents:         47 (94.0%)
  Executor Agents:         3 (6.0%)

Executor Performance:
  Success Rate:            33.3% (1/3)
  Failure Rate:            33.3% (1/3)
  In-Progress Rate:        33.3% (1/3)

Top Branch Activity:
  fix-gh-aw-compile-errors: 30 sessions (60%)
  update-awf-dependency:    8 sessions (16%)
  remove-generic-fallback:  6 sessions (12%)
  sub-pr-14933:             6 sessions (12%)

Comparison to Historical (Feb 6-10):
  Avg Sessions/Day:        50 (consistent)
  Historical Success Rate: 2.8%
  Today's Success Rate:    2.0% (↓ 0.8%)
  Historical Executor SR:  66.7%
  Today's Executor SR:     33.3% (↓ 33.4%) ⚠️

Next Steps

• Analyze 50 sessions from February 11, 2026
• Compare with historical data (Feb 6-10)
• Identify key patterns and trends
• Generate actionable recommendations
• Urgent: Investigate conversation log extraction authentication issue
• High Priority: Debug executor agent performance drop
• Medium Priority: Monitor branch concentration patterns
• Ongoing: Enhance metadata collection for future analyses

Experimental Analysis

This run used: Standard analysis only (not experimental)

Random value: 98 (threshold for experimental: <30)

Note: Approximately 30% of runs use experimental strategies to discover novel insights. This run applied standard analysis strategies documented in cache memory.

---

Analysis generated on 2026-02-11
Sessions analyzed: 50 (Feb 11) + 250 (Feb 6-10 historical)
Analysis type: Metadata-based (limited conversation logs)
Run ID: §21904154555

AI generated by Copilot Session Insights

• expires on Feb 18, 2026, 2:06 PM UTC

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This discussion was automatically closed because it expired on 2026-02-18T14:06:31.471Z.

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