[copilot-session-insights] Daily Copilot Agent Session Analysis — 2026-01-21

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Executive Summary

• Sessions Analyzed: 50
• Analysis Period: 2026-01-21
• Completion Rate: 10.0% (↓ from 16% yesterday)
• Average Duration: 3.95 minutes
• Experimental Strategy: None (standard analysis)

Key Alert: First explicit failures detected in 5 days (8 sessions, 16% failure rate). Completion rate declining from recent 16-18% range.

Key Metrics

Metric	Value	7-Day Trend
Total Sessions	50	→
Successful Completions	5 (10%)	↓
Failed	8 (16%)	↑
Action Required	15 (30%)	↓
Skipped	21 (42%)	↑
Average Duration	3.95 min	→
Median Duration	2.63 min	↑

Trends Over Time

Completion Rate (Last 7 Days):

• Jan 15: 8.5% → Jan 16: 0% → Jan 17: 0% → Jan 18: 47.6% → Jan 19: 18% → Jan 20: 16% → Jan 21: 10%

Duration Trends:

• Average duration stable in 2-10 minute range (healthy)
• Median duration increased to 2.63 min (up from near-zero)
• Suggests shift toward actual task execution vs orchestration

Success Factors ✅

1. Optimal Duration Range

• Success rate: 10%
• Description: Sessions within 2-15 minute window complete effectively
• Example: Average duration 4.0 minutes falls in optimal range

Historical Context

Historical data shows:

• Sessions under 2 minutes: Often orchestration/validation only
• Sessions 2-15 minutes: Optimal for task completion
• Sessions over 15 minutes: Higher complexity, variable success

Jan 19-20 data showed 100% success for coding tasks (13.7 min avg) and testing tasks (4.8 min avg).

Failure Signals ⚠️

1. Explicit Failures (NEW)

• Failure rate: 16% (8 sessions)
• Description: First day with explicit workflow failures in 5 days
• Impact: This is the highest failure rate since analysis began

2. Action Required Status

• Rate: 30%
• Description: 15 sessions requiring manual intervention
• Note: Lower than historical 70% (Jan 15-20), indicating workflow pattern shift

3. High Skip Rate

• Rate: 42%
• Description: 21 sessions filtered before execution
• Impact: Highest skip rate since baseline, reduces analyzable data

Prompt Quality Analysis 📝

High-Quality Prompt Characteristics

• Explicit task descriptions with action verbs: Found in 14% of sessions
• Reference to specific PRs or issues: 7 sessions (14%)
• Clear scope: "Running Copilot coding agent" pattern

Example High-Quality Prompt:

Running Copilot coding agent

Sessions with this pattern: 2 (4% of total)

Low-Quality Prompt Characteristics

• Generic agent names: Q, Scout, Archie, /cloclo (58% of sessions)
• Single word or very short names: 80% of low-quality prompts
• Lack of context or task description: Orchestration agents only

Example Low-Quality Prompt:

Q

Sessions with generic names: 29 (58% of total)

Important Note: 80% of "low-quality prompts" are actually orchestration agent names (Q, Scout, Archie, /cloclo), not user-facing task descriptions. These are system-level workflow names and should be excluded from quality metrics.

Notable Observations

Agent Distribution

• Orchestration agents: 58% (Q: 7, Scout: 7, Archie: 8, /cloclo: 7)
• PR-related tasks: 14% (7 sessions addressing specific comments)
• Coding tasks: 4% (2 sessions "Running Copilot coding agent")
• CI tasks: 8% (4 sessions)
• Other: 16%

Session Patterns

• Duration clustering: Most sessions fall in 1-5 minute range
• Skip rate spike: 42% is highest since Jan 15 baseline (was 12-30%)
• Failure emergence: First failures since Jan 17 (3 failures that day)

Actionable Recommendations

For Users Writing Task Descriptions

1. Use Explicit Task Descriptions

• Guidance: Include the action and target in your task description
• Before: Fix bug
• After: Fix authentication timeout bug in login flow

2. Provide Context and Scope

• Guidance: Reference specific files, PRs, or issues
• Before: Update tests
• After: Update authentication tests in tests/auth.test.ts

3. Set Clear Success Criteria

• Guidance: Define what success looks like
• Before: Improve performance
• After: Reduce API response time to under 200ms

For System Improvements

1. Workflow Orchestration

• Issue: High action_required rate suggests orchestration overhead
• Impact: Medium
• Recommendation: Consider separate metrics for orchestration vs task-execution sessions

2. Skip Logic

• Issue: 42% skip rate indicates aggressive filtering
• Impact: Low
• Recommendation: Review skip criteria - may be filtering too many sessions

3. Failure Investigation (High Priority)

• Issue: 8 explicit failures detected (first in 5 days)
• Impact: High
• Recommendation: Investigate root cause of failures - pattern break from recent success

Statistical Summary

Total Sessions Analyzed:     50
Successful Completions:      5 (10%)
Failed Sessions:            8 (16%)
Action Required:           15 (30%)
Skipped Sessions:          21 (42%)

Average Session Duration:   3.95 min
Median Session Duration:    2.63 min
Duration Range:            ~0-15 min

High-Quality Prompts:       7 (14%)
Medium-Quality Prompts:     3 (6%)
Low-Quality Prompts:       40 (80%)
  (Note: 58% are orchestration agent names)

Top Agent Types:
  Orchestration (Q/Scout/Archie/cloclo): 29 (58%)
  PR-related tasks:                       7 (14%)
  CI automation:                          4 (8%)
  Coding tasks:                           2 (4%)
  Other:                                  8 (16%)

Comparison with Historical Data

Week-over-Week Trends:

• Completion rate declining: 18% (Jan 19) → 16% (Jan 20) → 10% (Jan 21)
• Failure rate emerging: 0% (Jan 15-20) → 16% (Jan 21)
• Skip rate increasing: 12% (Jan 19) → 30% (Jan 20) → 42% (Jan 21)
• Duration stabilizing: Wild variance (0.15-10.25 min) now steady at 3-4 min

Positive Indicators:

• Duration in healthy 2-15 minute range
• Lower action_required rate (30% vs historical 70%)
• Median duration up to 2.63 min (from near-zero) suggests more real work

Concerning Indicators:

• First explicit failures in 5 days (16% rate)
• Completion rate drop from 16% to 10%
• Skip rate at highest level (42%)

Next Steps

• URGENT: Investigate root cause of 8 failed sessions - pattern break
• Review skip logic criteria - 42% may be too aggressive
• Separate orchestration agents from task-execution in future metrics
• Monitor completion rate trend over next 2-3 days
• Document successful PR-addressing patterns for replication
• Consider implementing task classification (orchestration vs execution vs PR)

Analysis Methodology

Standard Analysis Applied:

• Completion analysis (success/failure/abandoned)
• Duration pattern analysis (2-15 min optimal window)
• Prompt structure analysis (explicit vs generic)
• Agent type classification (orchestration vs task)

No Experimental Strategy This Run:

• Random selection determined standard analysis only
• Experimental strategies (semantic clustering, temporal analysis, etc.) applied ~30% of time
• Next potential experimental run: within 2-3 days

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Analysis generated automatically on 2026-01-21
Analysis Period: 50 sessions from 2026-01-21
Workflow Run: §21199005426

AI generated by Copilot Session Insights

• expires on Jan 28, 2026, 7:22 AM UTC

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This discussion was automatically closed because it expired on 2026-01-28T07:22:08.810Z.