[copilot-session-insights] Daily Copilot Agent Session Analysis — 2026-02-03

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Executive Summary

• Sessions Analyzed: 50
• Analysis Period: 2026-02-03 (single day snapshot)
• Sessions with Analyzable Logs: 22 (44%)
• Success Rate: 16.0% (8/50 sessions)
• Average Duration: 6.04 minutes
• Experimental Strategy: N/A (Standard analysis only)

Key Metrics

Metric	Value	Notes
Total Sessions	50	All workflow runs analyzed
Successful Completions	8 (16%)	✅ Completed successfully
Failed Sessions	9 (18%)	❌ Encountered errors
Action Required	22 (44%)	⚠️ Needs manual intervention
Skipped Sessions	10 (20%)	⏭️ Bypassed execution
In Progress	1 (2%)	🔄 Still running
Average Duration	6.04 min	For sessions with logs
Loop Detection Rate	1 (2%)	Only 1 session had loops
Context Confusion Rate	6 (12%)	Needed clarification

Critical Findings 🔍

1. Log Availability Gap

56% of sessions (28/50) had no analyzable log content. This severely limits our ability to understand agent behavior, identify issues, and improve performance.

Impact: Cannot analyze majority of sessions for patterns, errors, or optimization opportunities.

2. Security Guard Agent Failures

All 3 failed sessions were Security Guard Agent runs with identical characteristics:

• Duration: ~1 minute (too short for meaningful work)
• Prompt Quality: Medium
• Errors: Exactly 2 per session
• Pattern suggests systematic configuration or integration issue

3. Action Required Dominance

44% of sessions end with "action_required", the most common outcome. This suggests:

• Incomplete automation workflows
• Dependencies on human decision-making
• Potential for workflow optimization

Success Factors ✅

Patterns consistently associated with successful task completion:

1. High-Quality Prompts Are Essential

• Success rate: 100% (8/8 successful sessions had high-quality prompts)
• Characteristics:
  • Specific file references and extensions
  • Clear objectives using "should", "must", "need to"
  • Sufficient context (>5000 characters)
• Example successful session: Running Copilot coding agent (9.4 min, bug fix, high quality prompt)

2. Optimal Duration Range

• Success rate: 100% of successful sessions were 4-12 minutes
• Average for success: 7.8 minutes
• Median: 6.55 minutes
• Insight: Enough time for thorough analysis, but not stuck in loops

3. No Loop Detection

• Success rate: 100% (0/8 successful sessions had loops)
• Insight: Clear progression from problem to solution without repetitive patterns
• Only 1 session total had loops: Session 21628966350 (still in progress)

4. Bug Fix Tasks Perform Well

• Success rate: All 8 successful sessions were bug fix tasks
• Task type distribution (of sessions with logs):
  • Bug fixes: 12 (55%)
  • Unknown: 10 (45%)
• Insight: Current agent configuration excels at bug investigation and fixes

Failure Signals ⚠️

Common indicators of inefficiency or failure:

1. Security Guard Agent Pattern

• Failure rate: 100% (3/3 Security Guard sessions failed)
• Characteristics:
  • Very short duration (~1 minute)
  • Medium-quality prompts
  • Exactly 2 errors per session
  • No loops or confusion detected
• Example: Sessions 21628810309, 21628162163, 21617741569

2. Very Short Duration (<2 minutes)

• Associated with: All failed sessions
• Indicates: Setup failures, configuration errors, or missing prerequisites
• Recommendation: Investigate environment setup and dependencies

3. Missing Log Content

• Rate: 56% of sessions
• Types affected: Q, Scout, Archie, PR Nitpick Reviewer, /cloclo, workflow tests
• Impact: Cannot diagnose issues or learn from these sessions

4. Medium-Quality Prompts

• Associated with: 100% of failed sessions (3/3)
• Characteristics: Lacking context, vague objectives, or insufficient detail
• Contrast: 0% of successful sessions had medium-quality prompts

Prompt Quality Analysis 📝

High-Quality Prompt Characteristics

Found in 100% of successful sessions (9 total sessions, 8 successful):

1. File Extension References: Mentions specific code file types (.js, .py, .ts, etc.)
2. Clear Requirements: Uses directive language ("should", "must", "need to", "expected")
3. Sufficient Context: Provides >5000 characters of background information

Success Rate by Prompt Quality:

• High Quality: 89% success rate (8/9)
• Medium Quality: 0% success rate (0/3)
• Low Quality: 0% success rate (0/10)

Low-Quality Prompt Characteristics

Found in sessions without successful completion:

1. Vague Task Description: "Fix the thing", "Make it work"
2. Missing Context: No file paths, no error messages, no expected behavior
3. Minimal Detail: <2000 characters of information

Example Patterns to Avoid:

• Generic instructions without specifics
• No mention of affected files or components
• Missing error messages or failure symptoms

Tool Usage Patterns 🛠️

Most Used Tools

Across the 22 sessions with analyzable logs:

1. github_ tools (various): PR reads, branch lists, releases, code scanning
2. safeoutputs_ tools: add_comment, assign_to_agent, noop
3. workflows: Workflow execution and management

Tool Effectiveness

• Sessions with 0 tool calls: 5/8 successful (62.5% success rate)
• Sessions with 1-5 tool calls: 2/8 successful (25% success rate)
• Sessions with 20+ tool calls: 1/8 successful (Security Guard Agent)

Insight: Tool usage alone doesn't predict success. Context and prompt quality matter more.

Missing or Unavailable Tools

Based on confusion markers and clarification requests:

• No specific tools were explicitly requested but unavailable
• 12% of sessions showed signs of context confusion, possibly due to insufficient tool guidance

Notable Observations

Loop Detection

• Sessions with loops: 1 (2%)
• Session: 21628966350 (Running Copilot coding agent)
  • 6 occurrences of same tool
  • Still in progress at time of analysis
  • 112 errors detected in log
  • Context confusion present

Context Confusion

• Sessions with confusion: 6 (12%)
• Pattern: Even some successful sessions (5/8) had confusion markers
• Insight: Agents can recover from confusion if prompt quality is high

Task Type Distribution

Of the 22 sessions with analyzable logs:

• Bug Fix: 12 (55%) - Best performing category
• Unknown: 10 (45%) - Could not classify from logs

Recommendation: Better categorization or tagging of task types would improve analysis.

Actionable Recommendations

For Users Writing Task Descriptions

1. Always Provide High-Quality Prompts

✅ DO: Include specific file paths, clear objectives, and rich context
❌ DON'T: Use vague descriptions like "fix the issue" or "make it work"

Before (Low Quality):

Fix the test failures
```

**After (High Quality)**:
```
The test file tests/integration/auth.test.js is failing with error "TypeError: Cannot read property 'token' of undefined". The expected behavior is that user.token should be populated after successful login. Please investigate the login flow in src/auth/login.js and ensure the token is properly set in the user object before returning it. The test expects the token to be a non-empty string.
```

#### 2. **Provide Context >5000 Characters**
- Include error messages in full
- Show relevant code snippets
- Describe expected vs. actual behavior
- Reference related files and functions

#### 3. **Use Directive Language**
- "should", "must", "need to", "expected to"
- Clear acceptance criteria
- Specific outcomes

### For System Improvements

#### 1. **HIGH PRIORITY: Fix Security Guard Agent**
- **Issue**: 100% failure rate, consistent 2-error pattern, ~1 minute duration
- **Impact**: 3 sessions failed, pattern suggests config/integration issue
- **Action**: Debug Security Guard Agent initialization, environment setup, and error handling

#### 2. **HIGH PRIORITY: Improve Log Collection**
- **Issue**: 56% of sessions have no analyzable logs
- **Impact**: Cannot diagnose issues or improve agent behavior for majority of runs
- **Action**: 
  - Ensure all agent types properly emit and save logs
  - Verify log collection for Q, Scout, Archie, PR Nitpick Reviewer agents
  - Investigate why workflow test runs produce no logs

#### 3. **MEDIUM PRIORITY: Reduce Action Required Rate**
- **Issue**: 44% of sessions end with "action_required"
- **Impact**: High manual intervention rate reduces automation value
- **Action**:
  - Analyze what decisions require human input
  - Provide agents with clearer decision-making guidance
  - Automate common approval patterns

#### 4. **MEDIUM PRIORITY: Enhance Context Handling**
- **Issue**: 12% of sessions show context confusion
- **Impact**: Even successful sessions sometimes struggle with context
- **Action**:
  - Improve context injection in prompts
  - Provide agents with better file/repo navigation tools
  - Enhance clarification request handling

### For Tool Development

#### 1. **Session Log Standardization**
- **Need**: Consistent log format across all agent types
- **Frequency**: 28/50 sessions missing logs
- **Use case**: Enable comprehensive analysis and debugging
- **Priority**: HIGH

#### 2. **Task Type Classification**
- **Need**: Automatic task categorization beyond simple keyword matching
- **Frequency**: 10/22 sessions had unknown task type
- **Use case**: Better success rate tracking by task category
- **Priority**: MEDIUM

#### 3. **Prompt Quality Scoring**
- **Need**: Real-time prompt quality feedback before agent execution
- **Frequency**: Would help with 100% of sessions
- **Use case**: Guide users to write better task descriptions
- **Priority**: MEDIUM

## Trends Over Time

**Note**: This is the first analysis run with this methodology. No historical data available for comparison.

Future analyses will track:
- Success rate trends
- Average duration trends
- Prompt quality improvements
- Agent-specific performance

## Statistical Summary

```
Total Sessions Analyzed:     50
  Successful Completions:    8  (16%)
  Failed Sessions:           9  (18%)
  Action Required:          22  (44%)
  Skipped Sessions:         10  (20%)
  In Progress:               1   (2%)

Sessions with Logs:         22  (44%)
Sessions without Logs:      28  (56%)

Average Session Duration:   6.04 minutes
Median Session Duration:    6.55 minutes
Longest Session:           12.47 minutes
Shortest Session:           0.93 minutes

Loop Detection:             1 session (2%)
Context Issues:             6 sessions (12%)

Task Types (with logs):
  Bug Fixes:               12 (55%)
  Unknown:                 10 (45%)

Prompt Quality (with logs):
  High-Quality:             9 (41%)
  Medium-Quality:           3 (14%)
  Low-Quality:             10 (45%)

Success Rate by Quality:
  High:    89% (8/9)
  Medium:   0% (0/3)
  Low:      0% (0/10)

Next Steps

• Complete initial analysis methodology
• Establish baseline metrics
• Identify critical issues (Security Guard Agent, log collection)
• Investigate Security Guard Agent failure root cause
• Improve log collection for all agent types
• Implement prompt quality pre-check
• Run follow-up analysis in 7 days to measure improvements
• Build historical trend tracking

---

Analysis Date: 2026-02-03
Analysis Type: Standard (non-experimental)
Sessions Analyzed: 50 (22 with logs, 28 without)
Run ID: §21629165139

AI generated by Copilot Session Insights

• expires on Feb 10, 2026, 1:39 PM UTC

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This discussion was automatically closed because it expired on 2026-02-10T13:39:57.818Z.

Closed by Workflow