Meet the Workflows: Advanced Analytics & ML

Jan 13, 2026

Don Syme

Peli de Halleux

Mara Kiefer

Ooh! Time to plunge into the data wonderland at Peli’s Agent Factory! Where numbers dance and patterns sing!

In our previous post, we explored organization and cross-repo workflows that operate at enterprise scale - analyzing dozens of repositories together to find patterns and outliers that single-repo analysis would miss. We learned that perspective matters: what looks normal in isolation might signal drift at scale.

Beyond tracking basic metrics (run time, cost, success rate), we wanted deeper insights into how our agents actually behave and how developers interact with them. What patterns emerge from thousands of agent prompts? What makes some PR conversations more effective than others? How do usage patterns reveal improvement opportunities? This is where we brought out the big guns: machine learning, natural language processing, sentiment analysis, and clustering algorithms. Advanced analytics workflows don’t just count things - they understand them, finding patterns and insights that direct observation would never reveal.

Advanced Analytics & ML Workflows

These agents use sophisticated analysis techniques to extract insights:

Copilot Session Insights - Analyzes Copilot agent usage patterns and metrics
Copilot PR NLP Analysis - Natural language processing on PR conversations
Prompt Clustering Analysis - Clusters and categorizes agent prompts using ML
Copilot Agent Analysis - Deep analysis of agent behavior patterns

The Prompt Clustering Analysis uses machine learning to categorize thousands of agent prompts, revealing patterns we never noticed (“oh, 40% of our prompts are about error handling”).

The Copilot PR NLP Analysis does sentiment analysis and linguistic analysis on PR conversations - it found that PRs with questions in the title get faster review.

The Session Insights workflow analyzes how developers interact with Copilot agents, identifying common patterns and failure modes. What we learned: meta-analysis is powerful - using AI to analyze AI systems reveals insights that direct observation misses.

These workflows helped us understand not just what our agents do, but how they behave and how users interact with them.

Using These Workflows

You can add these workflows to your own repository and remix it as follows:

Copilot Session Insights:

gh aw add https://github.com/github/gh-aw/blob/v0.37.7/.github/workflows/copilot-agent-analysis.md

Copilot PR NLP Analysis:

gh aw add https://github.com/github/gh-aw/blob/v0.37.7/.github/workflows/copilot-pr-nlp-analysis

Prompt Clustering Analysis:

gh aw add https://github.com/github/gh-aw/blob/v0.37.7/.github/workflows/prompt-clustering-analysis.md

Copilot Agent Analysis:

gh aw add https://github.com/github/gh-aw/blob/v0.37.7/.github/workflows/copilot-agent-analysis.md

Then edit and remix the workflow specifications to meet your needs,recompile using gh aw compile, and push to your repository. See our Quick Start for further installation and setup instructions.

Learn More

GitHub Agentic Workflows - The technology behind the workflows
Quick Start - How to write and compile workflows

Next Up: Project Coordination Workflows

We’ve reached the final stop: coordinating multiple agents toward shared, complex goals across extended timelines.

Continue reading: Project Coordination Workflows →

This is part 18 of a 19-part series exploring the workflows in Peli’s Agent Factory.