[copilot-session-insights] Daily Copilot Agent Session Analysis — 2026-02-05

[github-actions[bot]]

Executive Summary

• Sessions Analyzed: 50
• Analysis Period: February 5, 2026 (single day snapshot)
• Overall Success Rate: 6.0%
• Action Required Rate: 78.0%
• Failure Rate: 16.0%
• Experimental Strategy: Semantic Clustering

Key Finding

78% of agent sessions require human intervention, indicating significant opportunities for automation improvement. Testing workflows show particularly concerning patterns with 100% failure rate.

---

Key Metrics

Metric	Value	Status
Total Sessions	50	—
Successful Completions	3 (6%)	🔴
Action Required	39 (78%)	⚠️
Failed/Cancelled	8 (16%)	⚠️
Data Coverage	Single day	⚠️ Limited

---

Success Factors ✅

Patterns associated with successful task completion:

1. PR-Specific Tasks

• Success rate: 100%
• Description: Tasks directly addressing PR comments or specific PR management activities
• Example: "Addressing comment on PR Delete .github/aw/*.md files now downloaded from GitHub #13860"
• Why it works: Clear, bounded scope with specific deliverables

2. General Copilot Invocations

• Success rate: 100%
• Description: General coding agent tasks without specialized routing
• Example: "Running Copilot coding agent"
• Why it works: Flexible agent behavior without constraints

Common Success Characteristics:

• ✓ Specific, actionable task descriptions
• ✓ Clear success criteria (e.g., address a specific comment)
• ✓ Direct invocation without specialized agent routing
• ✓ Limited scope and clear boundaries

---

Failure Signals ⚠️

Common indicators of inefficiency or failure:

1. Test Workflow Failures (Critical)

• Failure rate: 100% (8/8 sessions)
• Task: .github/workflows/test-workflow.yml
• Branch: copilot/configure-docs-site-videos
• Issue: Systematic failure across all test workflow invocations
• Impact: High - blocks quality assurance

2. Named Agent Sessions Require Action

• Action required rate: 100% (21/21 sessions)
• Agents affected: Archie (7), /cloclo (7), Q (7)
• Branch: Primarily copilot/configure-docs-site-videos (44 of 50 sessions)
• Issue: Named agents consistently unable to complete autonomously

3. Code Review Tasks Incomplete

• Action required rate: 100% (7/7 sessions)
• Agent: "PR Nitpick Reviewer 🔍"
• Issue: Review tasks never complete without human intervention

4. Documentation and Exploration Tasks

• Action required rate: 100% (11/11 sessions combined)
• Agents: Scout (7), Doc Build - Deploy (4)
• Issue: Research and documentation tasks need human validation

---

Experimental Analysis: Semantic Clustering

Strategy Used: Grouped sessions by task type based on agent name patterns to identify performance characteristics.

Task Clusters Identified:

Cluster	Sessions	Success	Action Req	Failure
Testing	8	0%	0%	100% 🔴
Named Agents	21	0%	100%	0% ⚠️
Code Review	7	0%	100%	0% ⚠️
Exploration	7	0%	100%	0% ⚠️
Documentation	4	0%	100%	0% ⚠️
PR Management	2	100% ✅	0%	0%
General Copilot	1	100% ✅	0%	0%

Key Findings:

1. Clear Task-Type Performance Patterns: Different agent types show remarkably consistent behavior within their category
2. Testing Workflows Are Broken: All 8 test workflow invocations failed - requires immediate investigation
3. Named Agents Need Guardrails: While not failing, named agents consistently require human action
4. Scoped Tasks Succeed: PR management tasks with clear scope complete successfully

Strategy Effectiveness: HIGH

Recommendation: Continue using semantic clustering in future analyses - it clearly identifies task-specific issues and helps prioritize improvements.

---

Prompt Quality Analysis 📝

High-Quality Prompt Characteristics

Based on successful sessions (3 total):

• Specific PR references: "Addressing comment on PR Delete .github/aw/*.md files now downloaded from GitHub #13860" (success)
• Clear action verbs: "Running Copilot coding agent" (success)
• Bounded scope: Tasks with specific, achievable goals

Example High-Quality Prompt:

Addressing comment on PR #13860
```
**Why it works**: References specific PR, implies clear deliverable, bounded scope

### Low-Quality Prompt Characteristics

Based on sessions requiring action or failing:
- **Generic agent names**: "Scout", "Archie", "Q" without context
- **Workflow file references**: `.github/workflows/test-workflow.yml` (all failed)
- **Broad exploration tasks**: Tasks without clear completion criteria

**Example Low-Quality Prompt**:
```
.github/workflows/test-workflow.yml
```
**Why it fails**: No context about what to test, no clear success criteria, likely triggered automatically

---

## Notable Observations

### Branch Concentration
- **44 of 50 sessions** (88%) occurred on branch `copilot/configure-docs-site-videos`
- **6 sessions** on branch `copilot/delete-markdown-files`
- Suggests focused development work on video configuration feature

### Agent Name Distribution
Diverse agent ecosystem with specialized roles:
- `.github/workflows/test-workflow.yml`: 8 (automated testing)
- Archie, /cloclo, Q, Scout: 7 each (specialized named agents)
- PR Nitpick Reviewer: 7 (code quality)

### Temporal Pattern
All 50 sessions occurred on **2026-02-05** - represents active development day with intensive Copilot usage.

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## Actionable Recommendations

### For Users Writing Task Descriptions

1. **Be Specific with PR References**
   - ✅ DO: "Address the code style comment on PR #123"
   - ❌ DON'T: "Fix the PR" or rely on generic agent names
   - **Impact**: 100% success rate for specific PR tasks vs 0% for generic

2. **Define Clear Success Criteria**
   - ✅ DO: "Add docstring to calculateTotal function in utils.py"
   - ❌ DON'T: "Scout" or "Improve code"
   - **Impact**: Clear deliverables enable autonomous completion

3. **Avoid Triggering Specialized Agents for Open-Ended Tasks**
   - ✅ DO: Use general Copilot invocation for exploratory work
   - ❌ DON'T: Rely on Scout/Archie for tasks without clear completion
   - **Impact**: Named agents consistently require intervention

### For System Improvements

1. **CRITICAL: Fix Test Workflow Failures** (Priority: HIGH)
   - **Issue**: 100% failure rate across 8 invocations
   - **Recommendation**: Investigate `.github/workflows/test-workflow.yml` configuration
   - **Potential causes**: 
     - Missing dependencies on feature branch
     - Incompatible test configuration
     - Environment setup issues
   - **Action**: Review workflow logs at runs like [21702592382](https://github.com/github/gh-aw/actions/runs/21702592382)

2. **Improve Named Agent Autonomy** (Priority: MEDIUM)
   - **Issue**: 21 named agent sessions (100%) require human action
   - **Recommendation**: 
     - Add autonomous completion criteria for Archie, /cloclo, Q, Scout
     - Implement confidence scoring for autonomous decisions
     - Define clear handoff points to humans
   - **Impact**: Could reduce action-required rate from 78% to ~36%

3. **Enhance Code Review Automation** (Priority: MEDIUM)
   - **Issue**: PR Nitpick Reviewer never completes autonomously
   - **Recommendation**:
     - Define auto-approvable review categories
     - Implement suggestion auto-application for minor issues
     - Add confidence thresholds for autonomous completion
   - **Impact**: Could enable 7 more autonomous completions

### For Tool Development

1. **Test Execution Debugging Tool**
   - **Need**: Identified from 8 test failures
   - **Use case**: Diagnose why test workflows fail on Copilot branches
   - **Priority**: High - blocks quality assurance

2. **Session Completion Confidence Scoring**
   - **Need**: Help agents determine when human action is required
   - **Use case**: Named agents could self-assess completion confidence
   - **Priority**: Medium - reduces action-required rate

3. **Branch-Specific Configuration Validator**
   - **Need**: 88% of sessions on one branch - may need branch-specific tooling
   - **Use case**: Validate environment setup before agent runs
   - **Priority**: Medium - prevents environment-related failures

---

## Data Limitations

### Single Day Snapshot
- **Limitation**: Analysis covers only February 5, 2026
- **Impact**: Cannot identify trends, improvements over time, or seasonal patterns
- **Mitigation**: Future analyses should aggregate 30+ days of data

### Metadata Only
- **Limitation**: Analysis based on workflow run metadata, not full conversation transcripts
- **Impact**: Cannot analyze:
  - Loop detection and retry patterns
  - Tool usage effectiveness
  - Agent reasoning quality
  - Context confusion instances
- **Mitigation**: Future analyses should include conversation logs when available

### No Duration Data
- **Limitation**: Session duration not available in metadata
- **Impact**: Cannot measure efficiency improvements or identify timeout issues
- **Mitigation**: Parse workflow logs for timing data or access enhanced metadata

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## Next Steps

- [ ] **URGENT**: Investigate test workflow failures on `copilot/configure-docs-site-videos` branch
- [ ] Review named agent (Archie, /cloclo, Q, Scout) completion criteria and add autonomous modes
- [ ] Analyze correlation between branch name and success rate with more data
- [ ] Implement session transcript collection for deeper analysis
- [ ] Schedule daily analysis to build historical trends (30-day rolling window)
- [ ] Create alerting for clusters with >50% failure rate

---

## Statistical Summary

```
Total Sessions Analyzed:     50
Successful Completions:      3 (6.0%)
Action Required:            39 (78.0%)
Failed Sessions:             8 (16.0%)

By Task Cluster:
  Testing:                   8 sessions (100% failure)
  Named Agents:             21 sessions (100% action required)
  Code Review:               7 sessions (100% action required)
  Exploration (Scout):       7 sessions (100% action required)
  Documentation:             4 sessions (100% action required)
  PR Management:             2 sessions (100% success) ✅
  General Copilot:           1 session  (100% success) ✅

Branch Distribution:
  copilot/configure-docs-site-videos:  44 sessions (88%)
  copilot/delete-markdown-files:        6 sessions (12%)

Agent Name Variety:          9 unique agent names/workflows

Data Coverage:              Single day (2026-02-05)
Experimental Strategy:      Semantic Clustering (High Effectiveness)

---

Analysis generated on 2026-02-05 | Experimental Run
Strategy: Semantic Clustering | Effectiveness: High
§21710455367

AI generated by Copilot Session Insights

• expires on Feb 12, 2026, 1:50 PM UTC