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My dear friends, we’ve arrived at the grand finale - the most spectacular room of all in Peli’s Agent Factory!

We’ve journeyed through 18 categories of workflows - from triage bots to code quality improvers, from security guards to creative poets, culminating in advanced analytics that use machine learning to understand agent behavior patterns. Each workflow handles its individual task admirably.

But here’s the ultimate challenge: how do you coordinate multiple agents working toward a shared goal? How do you break down a large initiative like “migrate all workflows to a new engine” into trackable sub-tasks that different agents can tackle? How do you monitor progress, alert on delays, and ensure the whole is greater than the sum of its parts? This final post explores planning, task-decomposition and project coordination workflows - the orchestration layer that proves AI agents can handle not just individual tasks, but entire structured projects requiring careful coordination and progress tracking.

Planning & Project Coordination Workflows

These agents coordinate multi-agent plans and projects:

Plan Command - Breaks down issues into actionable sub-tasks via /plan command - 514 merged PRs out of 761 proposed (67% merge rate)
Discussion Task Miner - Extracts actionable tasks from discussion threads - 60 merged PRs out of 105 proposed (57% merge rate)

Plan Command has contributed 514 merged PRs out of 761 proposed (67% merge rate), providing on-demand task decomposition that breaks complex issues into actionable sub-tasks. This is the highest-volume workflow by attribution in the entire factory. Developers can comment /plan on any issue to get an AI-generated breakdown into actionable sub-issues that agents can work on. A verified example causal chain: Discussion #7631 → Issue #8058 → PR #8110.

Discussion Task Miner has contributed 60 merged PRs out of 105 proposed (57% merge rate), continuously scanning discussions to extract actionable tasks that might otherwise be lost. The workflow demonstrates perfect causal chain attribution: when it creates an issue from a discussion, and Copilot Coding Assistant later fixes that issue, the resulting PR is correctly attributed to Discussion Task Miner. A verified example: Discussion #13934 → Issue #14084 → PR #14129. Recent merged examples include fixing firewall SSL-bump field extraction and adding security rationale to permissions documentation.

We learned that individual agents are great at focused tasks, but orchestrating multiple agents toward a shared goal requires careful architecture. Project coordination isn’t just about breaking down work - it’s about discovering work (Task Miner), planning work (Plan Command), and tracking work (Workflow Health Manager).

These workflows implement patterns like epic issues, progress tracking, and deadline management. They prove that AI agents can handle not just individual tasks, but entire projects when given proper coordination infrastructure.

Using These Workflows

You can add these workflows to your own repository and remix them. Get going with our Quick Start, then run one of the following:

Plan Command:

gh aw add-wizard https://github.com/github/gh-aw/blob/v0.45.5/.github/workflows/plan.md

Discussion Task Miner:

gh aw add-wizard https://github.com/github/gh-aw/blob/v0.45.5/.github/workflows/discussion-task-miner.md

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

You can also create your own workflows.

Learn More

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

What We’ve Learned

Throughout this 19-part journey, we’ve explored workflows spanning from simple triage bots to sophisticated multi-phase improvers, from security guards to creative poets, from individual task automation to organization-wide orchestration.

The key insight? AI agents are most powerful when they’re specialized, well-coordinated, and designed for their specific context. No single agent does everything - instead, we have an ecosystem where each agent excels at its particular job, and they work together through careful orchestration.

We’ve learned that observability is essential, that incremental progress beats heroic efforts, that security needs careful boundaries, and that even “fun” workflows can drive meaningful engagement. We’ve discovered that AI agents can maintain documentation, manage campaigns, analyze their own behavior, and continuously improve codebases - when given the right architecture and guardrails.

As you build your own agentic workflows, remember: start small, measure everything, iterate based on real usage, and don’t be afraid to experiment. The workflows we’ve shown you evolved through experimentation and real-world use. Yours will too.

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

Meet the Workflows: Advanced Analytics & ML

Jan 13, 2026

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 coding agent usage patterns and metrics - 32 analysis discussions
Copilot PR NLP Analysis - Natural language processing on PR conversations
Prompt Clustering Analysis - Clusters and categorizes agent prompts using ML - 27 analysis discussions
Copilot Agent Analysis - Deep analysis of agent behavior patterns - 48 daily analysis discussions

Prompt Clustering Analysis has created 27 analysis discussions using ML to categorize thousands of agent prompts - for example, #6918 clustering agent prompts to identify patterns and optimization opportunities. It revealed patterns we never noticed (“oh, 40% of our prompts are about error handling”).

Copilot PR NLP Analysis applies natural language processing to PR conversations, performing sentiment analysis and identifying linguistic patterns across agent interactions. It found that PRs with questions in the title get faster review.

Copilot Session Insights has created 32 analysis discussions examining Copilot coding agent usage patterns and metrics across the workflow ecosystem. It identifies common patterns and failure modes.

Copilot Coding Agent Analysis has created 48 daily analysis discussions providing deep analysis of agent behavior patterns - for example, #6913 with the daily Copilot coding agent analysis.

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-wizard https://github.com/github/gh-aw/blob/v0.45.5/.github/workflows/copilot-agent-analysis.md

Copilot PR NLP Analysis:

gh aw add-wizard https://github.com/github/gh-aw/blob/v0.45.5/.github/workflows/copilot-pr-nlp-analysis

Prompt Clustering Analysis:

gh aw add-wizard https://github.com/github/gh-aw/blob/v0.45.5/.github/workflows/prompt-clustering-analysis.md

Copilot Agent Analysis:

gh aw add-wizard https://github.com/github/gh-aw/blob/v0.45.5/.github/workflows/copilot-agent-analysis.md

You can also create your own workflows.

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.

Meet the Workflows: Organization & Cross-Repo

Jan 13, 2026

Let’s zoom out at Peli’s Agent Factory!

In our previous post, we explored multi-phase improver workflows - our most ambitious agents that tackle big projects over multiple days, maintaining state and making incremental progress. These workflows proved that AI agents can handle complex, long-running initiatives when given the right architecture.

But all that sophisticated functionality has focused on a single repository. What happens when you zoom out to organization scale? What insights emerge when you analyze dozens or hundreds of repositories together? What looks perfectly normal in one repo might be a red flag across an organization. Organization and cross-repo workflows operate at enterprise scale, requiring careful permission management, thoughtful rate limiting, and different analytical lenses. Let’s explore workflows that see the forest, not just the trees.

Organization & Cross-Repo Workflows

These agents work at organization scale, across multiple repositories:

Org Health Report - Organization-wide repository health metrics - 4 organization health discussions created
Stale Repo Identifier - Identifies inactive repositories - 2 issues flagging truly stale repos
Ubuntu Image Analyzer - Documents GitHub Actions runner environments - 4 merged PRs out of 8 proposed (50% merge rate)

Scaling agents across an entire organization changes the game. Org Health Report has created 4 organization health discussions analyzing dozens of repositories at scale - for example, #6777 with the December 2025 organization health report. It identifies patterns and outliers (“these three repos have no tests, these five haven’t been updated in months”).

Stale Repo Identifier has created 2 issues flagging truly stale repositories for organizational hygiene - for example, #5384 identifying Skills-Based-Volunteering-Public as truly stale. It helps find abandoned projects that should be archived or transferred.

We learned that cross-repo insights are different - what looks fine in one repository might be an outlier across the organization. These workflows require careful permission management (reading across repos needs organization-level tokens) and thoughtful rate limiting (you can hit API limits fast when analyzing 50+ repos).

Ubuntu Image Analyzer has contributed 4 merged PRs out of 8 proposed (50% merge rate), documenting GitHub Actions runner environments to keep the team informed about available tools and versions. It’s wonderfully meta - it documents the very environment that runs our agents.

Using These Workflows

You can add these workflows to your own repository and remix them. Get going with our Quick Start, then run one of the following:

Org Health Report:

gh aw add-wizard https://github.com/github/gh-aw/blob/v0.45.5/.github/workflows/org-health-report.md

Stale Repo Identifier:

gh aw add-wizard https://github.com/github/gh-aw/blob/v0.45.5/.github/workflows/stale-repo-identifier.md

Ubuntu Image Analyzer:

gh aw add-wizard https://github.com/github/gh-aw/blob/v0.45.5/.github/workflows/ubuntu-image-analyzer.md

You can also create your own workflows.

Learn More

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

Next Up: Advanced Analytics & ML Workflows

Cross-repo insights reveal patterns, but we wanted to go even deeper - using machine learning to understand agent behavior.

Continue reading: Advanced Analytics & ML Workflows →

This is part 17 of a 19-part series exploring the workflows in Peli’s Agent Factory.

Meet the Workflows: Multi-Phase Improvers

Jan 13, 2026