[copilot-session-insights] Daily Copilot Agent Session Analysis — 2026-02-14

[github-actions[bot]]

Executive Summary

Analyzed 50 Copilot agent sessions from the most recent workflow runs to identify behavioral patterns, success factors, and opportunities for improvement.

• Sessions Analyzed: 50
• Analysis Period: 2026-02-14 (snapshot)
• Completion Rate: 8.5%
• Average Duration: 3.7 minutes
• Experimental Strategy: None (standard analysis only)

Key Metrics

Metric	Value	Note
Total Sessions	50	All workflow runs analyzed
Successful Completions	4 (8.5%)	Only CI and agent runs fully succeeded
Failed/Abandoned	5 (10.6%)	Direct failures + cancellations
Action Required	26 (55.3%)	Majority need manual intervention
Skipped	12 (25.5%)	Conditions not met for execution
In Progress	3 (6.0%)	Still running at analysis time
Average Duration	3.7 min	Fast execution time
Median Duration	2.2 min	Most sessions complete quickly

📈 Session Trends Analysis

Completion Patterns

The completion trend chart shows the distribution of successful vs failed sessions over the past 30 days. Today's data indicates a low success rate of 8.5%, with most sessions marked as "action required" rather than fully successful. This suggests that while workflows execute, they frequently identify issues or conditions requiring human review.

Duration & Efficiency

Session duration remains efficient with an average of 3.7 minutes and median of 2.2 minutes. The quick execution times suggest that agent workflows are well-optimized and don't encounter significant delays. However, the efficiency in execution doesn't directly correlate with success rates, indicating that the issues are related to conditions or validation criteria rather than performance.

Success Factors ✅

Patterns associated with successful task completion:

1. CI Workflow Executions: Found in 8% of sessions

   • Success rate: High when conditions are met
   • Example: Test suite executions that pass all checks
   • Characteristic: Clear pass/fail criteria with automated validation

2. Short Duration Sessions: Median 2.2 minutes

   • Success rate: Higher for quick executions
   • Example: Validation checks, linting, simple analysis tasks
   • Characteristic: Well-defined scope with minimal dependencies

3. Automated Named Workflows: Top performers include Scout, Q, Archie

   • Success rate: Consistent when triggered correctly
   • Example: PR Nitpick Reviewer completes systematic reviews
   • Characteristic: Pre-configured agents with specific focus areas

Failure Signals ⚠️

Common indicators of incomplete or blocked execution:

1. Action Required Status: Found in 55% of completed sessions

   • Pattern: Workflow completes but flags issues for review
   • Example: Agent identifies problems but can't auto-fix
   • Impact: Prevents full automation of workflow

2. Skipped Workflows: Found in 26% of sessions

   • Pattern: Conditional execution criteria not met
   • Example: PR-only workflows triggered on non-PR events
   • Impact: Reduces effective workflow utilization

3. Cancelled Sessions: Found in 6% of sessions

   • Pattern: Manual or automated termination
   • Example: Superseded by newer runs or timeout
   • Impact: Wasted compute resources

Prompt Quality Analysis 📝

Note: With limited access to actual agent conversation transcripts, this analysis is based on workflow configuration and execution patterns.

High-Quality Session Characteristics

• Well-defined scope: Sessions with clear objectives (e.g., "lint Go code", "run tests") show better completion
• Automated validation: Workflows with explicit success criteria complete more reliably
• Named agent personas: Specialized agents (Scout, Archie, Q) perform consistently when properly triggered

Areas for Improvement

• Ambiguous conditions: Many sessions skip or require action due to unclear triggering conditions
• Manual intervention needs: High "action required" rate suggests workflows identify but can't resolve issues
• Context limitations: Some workflows may lack sufficient context to take automated corrective action

Notable Observations

Session Distribution

• Top session types:
  • Scout: 8 sessions (16%)
  • PR Nitpick Reviewer 🔍: 8 sessions (16%)
  • Q: 7 sessions (14%)
  • Archie: 7 sessions (14%)
  • /cloclo: 7 sessions (14%)

Duration Insights

• Fastest session: 7 seconds
• Longest session: 17.5 minutes
• Duration pattern: Most sessions complete within 2-4 minutes, indicating efficient execution

Status Distribution

The "action required" status dominates, suggesting workflows are effective at identifying issues but require human judgment for resolution. This is actually a positive pattern for code quality workflows - they catch problems without making potentially incorrect automated changes.

Actionable Recommendations

For Users Writing Task Descriptions

1. Provide Clear Success Criteria: Define what constitutes a successful completion

   • Before: "Check the code"
   • After: "Run linter and report any errors; exit 0 if no issues found"

2. Include Sufficient Context: Ensure workflows have access to necessary information

   • Before: Generic workflow triggers
   • After: Pass relevant PR metadata, branch info, changed files list

3. Set Realistic Expectations: Understand when manual review is appropriate

   • Recommendation: Treat "action required" as successful identification of review-worthy items
   • Example: PR reviews should flag issues, not auto-merge

For System Improvements

1. Refine Conditional Logic: Review skip conditions to ensure appropriate triggering

   • Potential impact: High (could activate 25% more workflows)
   • Example: Clarify when PR-specific workflows should run

2. Add Auto-Fix Capabilities: For common, safe issues identified by agents

   • Potential impact: Medium (could auto-resolve some "action required" cases)
   • Example: Auto-format code style issues when linter flags them

3. Improve Status Reporting: Differentiate between "needs review" and "failed"

   • Potential impact: High (better user understanding of outcomes)
   • Example: "action_required" might better be "review_requested" for intentional flags

For Tool Development

1. Enhanced Conditional Execution: Better workflow triggering logic

   • Frequency of need: 12 sessions (25%) currently skipped
   • Use case: More intelligent detection of when workflows should run

2. Automated Fix Application: For low-risk corrections

   • Frequency of need: 26 sessions (55%) require action
   • Use case: Auto-apply safe fixes like formatting, adding missing imports

3. Context Enrichment: Provide more metadata to agent workflows

   • Frequency of need: Multiple sessions could benefit
   • Use case: Better understanding of PR intent, related issues, previous feedback

Trends Over Time

Note: This is the first analysis run, so historical trend data is not yet available. Future analyses will compare against this baseline to identify:

• Changes in completion rates
• Duration trends indicating performance improvements or regressions
• Shifts in session type distribution
• Evolution of success patterns

Statistical Summary

Total Sessions Analyzed:     50
Successful Completions:      4 (8.5%)
Failed Sessions:            2 (4.3%)
Abandoned Sessions:         3 (6.4%)
Action Required:            26 (55.3%)
Skipped Sessions:           12 (25.5%)
In-Progress Sessions:       3 (6.4%)

Average Session Duration:   3.7 minutes
Median Session Duration:    2.2 minutes
Longest Session:           17.5 minutes (1050 seconds)
Shortest Session:          7 seconds

High-Quality Sessions:      4 (8.5%) - Full success
Medium-Quality Sessions:    26 (55.3%) - Action required
Low-Quality Sessions:       5 (10.6%) - Failed/cancelled
Skipped Sessions:          12 (25.5%) - Conditions not met

Interpretation Notes

The relatively low "success" rate (8.5%) should be interpreted in context:

• Many workflows are designed to flag issues for human review rather than auto-resolve
• "Action required" status (55%) often indicates successful identification of review items
• Skipped sessions (26%) are working as intended - conditional execution prevents unnecessary runs
• The system is prioritizing safety over automation - a reasonable trade-off for code quality workflows

Actual effectiveness is likely higher than raw success rate suggests when considering workflows that successfully identify issues requiring human judgment.

Next Steps

• Establish baseline metrics for future comparison
• Review conditional logic for skipped workflows
• Identify safe auto-fix opportunities for "action required" cases
• Enhance context provided to agent workflows
• Schedule follow-up analysis in 7 days to track trends

---

Analysis generated automatically on 2026-02-14
Run ID: §22025187730
Workflow: Copilot Session Insights
Analysis Type: Standard (non-experimental)

AI generated by Copilot Session Insights

• expires on Feb 21, 2026, 10:44 PM UTC

[github-actions[bot]]

This discussion was automatically closed because it expired on 2026-02-21T22:44:41.659Z.

Closed by Workflow