[copilot-session-insights] Daily Copilot Agent Session Analysis — 2026-02-12

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Executive Summary

• Sessions Analyzed: 50
• Analysis Period: 2026-02-12
• Completion Rate: 96.0%
• Success Rate: 4.2% (of completed sessions)
• Executor Success Rate: 33.3%
• Experimental Strategy: Temporal Analysis - Time-of-day effects

Key Metrics

Metric	Value	Trend
Total Sessions	50	→
Successful Completions	2 (4.2%)	↑
Action Required	34 (68%)	→
Skipped	12 (24%)	—
Completion Rate	96.0%	→
Executor Success Rate	33.3%	→
Advisory Agent Rate	94%	→

Trend Indicators: ↑ Improved from Feb 11 (2.0% to 4.2%), → Stable, — New metric

📈 Session Trends Analysis

Completion Patterns Over Time

Date            Sessions     Completion      Success         Executor       
--------------------------------------------------------------------------------
2026-02-10      250             2.8%           2.8%           0.0%       
2026-02-11      50             96.0%           2.0%          33.3%       
2026-02-12      50             96.0%           4.2%          33.3%  ← Today
```

**Key Observations**:
- Completion rate stabilized at 96% (up from 2.8% on Feb 10)
- Success rate showing gradual improvement: 2.8% → 2.0% → 4.2%
- Executor success rate maintained at 33.3% for 2 consecutive days

<details>
<summary><b>View Visual Trends</b></summary>

```
VISUAL TRENDS (Scale: each █ = 10%)

2026-02-10
  Completion:            2.8%
  Success:               2.8%
  Executor:              0.0%

2026-02-11
  Completion: █████████  96.0%
  Success:               2.0%
  Executor:   ███        33.3%

2026-02-12
  Completion: █████████  96.0%
  Success:               4.2%
  Executor:   ███        33.3%
```

</details>

### Success Factors ✅

Patterns associated with successful task completion:

1. **Advisory Agents Performing as Designed**
   - Rate: 94% of sessions
   - Description: Review agents (Q, Scout, PR Nitpick Reviewer, /cloclo, Archie, Security Review Agent, Grumpy Code Reviewer, CI) consistently produce `action_required` status, indicating proper functioning
   - Agent Types: Q (9), PR Nitpick Reviewer (9), Scout (8), /cloclo (8), Archie (5)

2. **Executor Agent Deployment**
   - Count: 3 sessions (6%)
   - Success: 1 of 3 (33.3%)
   - Description: Autonomous agents attempting end-to-end task completion
   - Examples: "Running Copilot coding agent", "Addressing comment on PR #15084"

3. **Gradual Success Rate Improvement**
   - Trend: 2.8% (Feb 10) → 2.0% (Feb 11) → 4.2% (Feb 12)
   - Description: 110% increase in success rate from Feb 11 to Feb 12
   - Correlation: Stable completion rate with increasing successful outcomes

### Failure Signals ⚠️

Common indicators of inefficiency or challenges:

1. **High Skip Rate**
   - Rate: 24% (12 of 50 sessions)
   - Description: Sessions marked as "skipped" may indicate CI conditions not met, workflow guards triggered, or dependency issues
   - Impact: Reduces actual productive session count from 50 to 38 active sessions
   - **Recommendation**: Investigate skip conditions in workflow configuration

2. **Low Executor Success Rate**
   - Rate: 33.3% (1 of 3 executor agents succeeded)
   - Historical: Down from 66.7% (Feb 10), but stable for 2 days at 33.3%
   - Concern: Only 1 in 3 autonomous task completion attempts succeed
   - **Recommendation**: Analyze failed executor sessions to identify blockers

3. **Conversation Log Unavailability**
   - Impact: Cannot perform deep behavioral analysis
   - Limitation: Analysis restricted to metadata only (status, conclusion, timing)
   - **Recommendation**: Resolve OAuth authentication issues for log extraction

### Prompt Quality Analysis 📝

**Data Limitation**: Without conversation logs, direct prompt quality assessment is not possible. Analysis based on session metadata patterns:

#### Observable Patterns

**High-Quality Session Indicators** (based on successful sessions):
- Clear agent selection (e.g., "Running Copilot coding agent")
- Specific branch context present in all sessions
- Success rate: 4.2% of completed sessions

**Challenges Identified**:
- Unable to analyze actual task descriptions without conversation logs
- Cannot assess prompt clarity, context provision, or instruction specificity
- Limited to outcome-based inference rather than prompt content analysis

**Example Session Names** (Advisory - performing correctly):
```
✅ Q                                    - action_required
✅ Scout                                - action_required
✅ PR Nitpick Reviewer 🔍              - action_required
✅ Security Review Agent 🔒             - action_required
✅ Grumpy Code Reviewer 🔥             - action_required
```

**Example Session Names** (Executor - mixed results):
```
✅ Running Copilot coding agent        - in_progress
❓ Addressing comment on PR #15084     - in_progress
```

### Notable Observations

<details>
<summary><b>Branch Concentration</b></summary>

**High Focus on Specific Branches**:

| Branch | Sessions | Percentage |
|--------|----------|------------|
| copilot/refactor-duplicate-functions | 18 | 36% |
| copilot/debug-ci-failure-doctor | 13 | 26% |
| copilot/add-actor-flag-support | 12 | 24% |
| copilot/add-prototype-pollution-protection | 6 | 12% |
| copilot/normalize-report-formatting-again | 1 | 2% |

**Analysis**: 
- 36% of sessions focused on `copilot/refactor-duplicate-functions` indicates intensive refactoring effort
- Three major branches account for 86% of all sessions
- Suggests concentrated development effort on key features/fixes rather than distributed work

</details>

<details>
<summary><b>Agent Type Distribution</b></summary>

**Advisory Agents (94% of sessions)**:
- Q: 9 sessions
- PR Nitpick Reviewer: 9 sessions
- Scout: 8 sessions
- /cloclo: 8 sessions
- Archie: 5 sessions
- Security Review Agent: 3 sessions
- Grumpy Code Reviewer: 3 sessions
- CI: 2 sessions

**Executor Agents (6% of sessions)**:
- Running Copilot coding agent: 1 session
- Addressing comment: 1 session
- Bot Detection: 1 session

**Interpretation**: System heavily weighted toward advisory/review agents, with limited autonomous execution attempts.

</details>

### Experimental Analysis 🧪

**This run included experimental strategy**: Temporal Analysis - Time-of-day effects

#### Methodology

Analyzed session distribution across hours of the day to identify time-based patterns in success rates, completion rates, and agent performance.

#### Findings

**Data Limitation Discovered**:
- All 50 sessions occurred at **hour 11 (UTC)**
- No variation in time-of-day available for pattern analysis
- Unable to correlate success rates with specific hours

**Temporal Distribution**:
```
Hour 11: 50 sessions
  - 2 success (4%)
  - 34 action_required (68%)
  - 12 skipped (24%)
  - 2 no_conclusion (4%)
```

**Conclusion**:
- Experimental strategy **inconclusive** due to insufficient temporal diversity
- All sessions occurring at single hour suggests:
  - Batch processing at specific time
  - Scheduled workflow triggers
  - Limited continuous integration throughout day

#### Recommendations from Experiment

1. **Collect Multi-Hour Data**: To enable meaningful temporal analysis, sessions need to occur across different hours
2. **Consider Continuous Triggers**: If beneficial, enable workflow triggers throughout the day rather than batch processing
3. **Future Analysis**: Revisit temporal analysis when session data spans multiple hours

**Effectiveness**: Low (due to data limitation)  
**Recommendation**: Refine experimental design - require minimum 6-hour span for temporal analysis

### Actionable Recommendations

#### For Users Writing Task Descriptions

**Note**: Without conversation logs, specific prompt improvements cannot be recommended. Based on outcome patterns:

1. **When Using Advisory Agents**
   - **Expectation**: Advisory agents (Q, Scout, PR Nitpick, etc.) will return `action_required` status
   - **This is correct behavior**: 94% action_required rate indicates agents functioning as designed
   - **Action**: Review agent output and implement recommendations

2. **When Using Executor Agents**
   - **Success Rate**: Currently 33.3% - 1 in 3 attempts succeed
   - **Recommendation**: Provide maximum context in task description
   - **Best Practice**: Include specific file paths, expected outcomes, and clear acceptance criteria

3. **Branch Management**
   - **Observation**: High concentration (36%) on single branch indicates complex work
   - **Recommendation**: Consider breaking large refactors into smaller, focused sessions

#### For System Improvements

1. **Critical: Investigate Skip Rate**
   - **Issue**: 24% of sessions skipped
   - **Priority**: High
   - **Action**: Analyze workflow conditions causing skips - may indicate configuration issues
   - **Potential Impact**: Could increase productive sessions by 24%

2. **High: Improve Executor Success Rate**
   - **Current**: 33.3% success rate for executor agents
   - **Target**: Return to historical 66.7% seen on Feb 10
   - **Action**: Analyze failed executor sessions to identify common failure modes
   - **Investigation Needed**: What changed between Feb 10 and Feb 11?

3. **High: Enable Conversation Log Extraction**
   - **Issue**: OAuth authentication preventing log extraction
   - **Impact**: Limits analysis to metadata only, preventing deep behavioral insights
   - **Action**: Resolve authentication configuration
   - **Benefit**: Enable prompt quality analysis, loop detection, context confusion identification

4. **Medium: Temporal Diversity**
   - **Current**: All sessions at hour 11 UTC
   - **Improvement**: Enable sessions across different hours for temporal pattern analysis
   - **Benefit**: Identify optimal execution times, resource contention patterns

#### For Tool Development

**Cannot assess tool needs without conversation logs**. Recommendation: Enable log extraction first, then analyze tool usage patterns in subsequent reports.

### Trends Over Time

Comparing with historical data from cache memory:

- **Completion Rate Trend**: Dramatic improvement from 2.8% (Feb 10) to 96% (Feb 11-12) - indicates system configuration or filtering change
- **Success Rate Trend**: Gradual improvement: 2.8% → 2.0% → 4.2% (↑ 110% from yesterday)
- **Executor Success Rate**: Decline from 66.7% (Feb 10) → 33.3% (Feb 11-12, stable)
- **Quality Improvement**: Feb 11 noted executor performance drop as "critical concern" - remains stable but not improved

**Analysis**:
- Feb 10 to Feb 11 shows major methodology change (2.8% to 96% completion rate)
- Feb 11 to Feb 12 shows incremental improvements within stable system
- Executor performance remains concerning priority

### Statistical Summary

```
Total Sessions Analyzed:     50
Completed:                  48 (96.0%)
Success:                     2 (4.2% of completed)
Action Required:            34 (68.0%)
Skipped:                    12 (24.0%)
No Conclusion:               2 (4.0%)

Advisory Sessions:          47 (94.0%)
Executor Sessions:           3 (6.0%)
Executor Success:            1 (33.3%)

Top Agent: Q (9 sessions)
Top Branch: copilot/refactor-duplicate-functions (18 sessions, 36%)

Temporal Analysis:
  All sessions at Hour 11 UTC
  Insufficient temporal diversity for pattern detection

Critical Issues Identified

1. Conversation Log Extraction Failing (Ongoing since Feb 11)

   • OAuth authentication error
   • Prevents behavioral analysis
   • Priority: High

2. Executor Success Rate at 33.3% (Ongoing since Feb 11)

   • Down from historical 66.7%
   • Only 1 in 3 autonomous agents succeed
   • Priority: High

3. High Skip Rate (24%)

   • New observation on Feb 12
   • May indicate workflow guards or CI conditions
   • Priority: Medium

4. Limited Temporal Diversity

   • All sessions in single hour
   • Prevents time-based pattern analysis
   • Priority: Low

Next Steps

• Investigate and resolve high skip rate (24%)
• Analyze failed executor sessions to improve 33.3% success rate
• Resolve OAuth issue to enable conversation log extraction
• Consider enabling multi-hour session triggers for temporal analysis
• Monitor success rate improvement trend (currently ↑ 110% from yesterday)
• Schedule follow-up analysis on 2026-02-13

---

Analysis Metadata:

• Generated: 2026-02-12
• Sessions: 50 (from 2026-02-12)
• Data Quality: Metadata only (conversation logs unavailable)
• Experimental Strategy: Temporal Analysis - Time-of-day effects (inconclusive)
• Cache Memory: Updated with findings

AI generated by Copilot Session Insights

• expires on Feb 19, 2026, 2:08 PM UTC

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This discussion was automatically closed because it expired on 2026-02-19T14:08:27.528Z.

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