[copilot-agent-analysis] Daily Copilot Agent Analysis - 2025-10-17 (Evening)

[github-actions[bot]]

🤖 Copilot Agent PR Analysis - 2025-10-17 (Evening Run)

Summary

Analysis Period: Last 24 hours (ending 2025-10-17 ~21:00 UTC)
Total PRs Analyzed: 0
Success Rate: N/A (no activity)

Activity Status

No PRs created by copilot-swe-agent in the analyzed 24-hour window.

This analysis covers the 24-hour period ending around 21:00 UTC on October 17, 2025. No pull requests were created by GitHub Copilot's SWE agent (copilot-swe-agent) during this time frame.

Historical Context

Previous Activity (from cache)

According to cached metrics, there was significant activity earlier on 2025-10-17:

Metric	Value
PRs Created	9
PRs Merged	8 (89%)
PRs Closed (not merged)	0
PRs Open	1
Avg Human Comments	0.67 per PR
Avg Agent Duration	37 minutes
Success Rate	89%

Note: This data represents a different 24-hour analysis window from earlier on October 17, 2025. The copilot-swe-agent was quite active during that period, creating 9 PRs with an excellent 89% merge rate.

Trend Analysis

Data Availability

Currently, historical data is limited:

• Available: 1 day of metrics (2025-10-17 from earlier run)
• Required for weekly trends: 7 days minimum
• Required for monthly trends: 30 days minimum

Status: ⚠️ Insufficient data for meaningful trend analysis

Recommendations for Future Runs

1. Historical Data Collection: Future runs should incrementally build historical data by querying past dates
2. Consistent Windows: Establish consistent analysis windows (e.g., always 00:00-23:59 UTC)
3. Data Accumulation: With 7+ days of data, week-over-week comparisons will become available

Recent Changes to Copilot Agent Analysis Workflow

Significant Update Detected

Commit: 9af8441 - "Update copilot-agent-analysis to create discussions with weekly/monthly summaries"
Date: 2025-10-17T21:43:48Z
Author: copilot-swe-agent[bot]
PR: #1897

Changes Made

The copilot-agent-analysis workflow (this very workflow!) was recently updated by the copilot-swe-agent to:

• Create discussions in the "Audits" category
• Add weekly and monthly summary capabilities
• Implement historical data rebuild functionality
• Enhance trend analysis features

Impact: This update improves the quality and depth of future analysis reports. The workflow can now track performance over time and provide actionable insights based on historical trends.

Search Methodology

Bot Identification Challenges

The copilot-swe-agent can appear under multiple identities:

• Username: Copilot (ID: 198982749)
• App reference: app/copilot-swe-agent
• Bot reference: copilot-swe-agent[bot]
• Via: github-actions[bot] with specific patterns

Search Queries Attempted

1. ✅ author:copilot-swe-agent - No results
2. ✅ author:app/copilot - Found multiple PRs (but from older dates)
3. ✅ author:github-actions[bot] created:>=2025-10-17 - Found non-copilot PRs
4. ✅ "Co-authored-by: copilot-swe-agent" - No results in target window

Confirmed Finding

No PRs from copilot-swe-agent in the current 24-hour analysis window.

Notes

About This Analysis

• First comprehensive run: This appears to be one of the first runs with the enhanced analysis capabilities
• Timing variance: The previous cache data and this analysis represent different 24-hour windows on the same calendar date
• Data building: Historical data will accumulate with each daily run, enabling richer trend analysis

Copilot Agent Performance (from available data)

Based on the single historical data point:

• High success rate: 89% of PRs were successfully merged
• Fast execution: Average 37-minute completion time
• Low human intervention: Average 0.67 human comments per PR suggests autonomous operation
• Consistent quality: 8 out of 9 PRs merged without issues

Recommendations

Immediate Actions

1. Continue daily monitoring: Let this workflow run daily to build historical data
2. Consistent scheduling: Ensure the workflow runs at the same time each day
3. Data validation: After 7 days, validate trend calculations

Long-term Improvements

1. Establish baseline metrics: Once 30 days of data is collected, establish performance baselines
2. Alert thresholds: Define what constitutes unusual activity (e.g., success rate drops below 70%)
3. Correlation analysis: Track correlation between instruction changes and performance shifts

Next Run

Expected: 2025-10-18 at 10:00 UTC (per schedule)
Goal: Begin building continuous daily metrics for trend analysis
Progress: 1/7 days for weekly trends, 1/30 days for monthly trends

---

This analysis was generated automatically by the Copilot Agent Analysis workflow.
Report generated at: 2025-10-17 ~21:00 UTC
Workflow run: #18605582702

AI generated by Copilot Agent PR Analysis

[github-actions[bot]]

This discussion was automatically closed because it was created by an agentic workflow more than 1 month ago.