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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.

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.

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

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.

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

Let’s continue our journey through Peli’s Agent Factory!

In our previous post, we explored infrastructure workflows - the meta-monitoring layer that validates MCP servers, checks tool configurations, and ensures the platform itself stays healthy. These workflows watch the watchers, providing visibility into the invisible plumbing.

Most workflows we’ve seen so far run once and complete: analyze this PR, triage that issue, test this deployment. They’re ephemeral - they execute, produce results, and disappear. But what about projects that are too big to tackle in a single run? What about initiatives that require research, setup, and incremental implementation? Traditional CI/CD is built for stateless execution, but we discovered something powerful: workflows that maintain state across days, working a little bit each day like a persistent team member who never takes breaks. Welcome to our most ambitious experiment - multi-phase improvers that prove AI agents can handle complex, long-running projects.

These are some of our most ambitious agents - they tackle big projects over multiple days:

• Daily Backlog Burner - Systematically works through issues and PRs, one day at a time
• Daily Perf Improver - Three-phase performance optimization (research, setup, implement)
• Daily QA - Continuous quality assurance that never sleeps
• Daily Accessibility Review - WCAG compliance checking with Playwright
• PR Fix - On-demand slash command to fix failing CI checks (super handy!)

This is where we got experimental with agent persistence and multi-day workflows. Traditional CI runs are ephemeral, but these workflows maintain state across days using repo-memory. The Daily Perf Improver runs in three phases - research (find bottlenecks), setup (create profiling infrastructure), implement (optimize). It’s like having a performance engineer who works a little bit each day. The Daily Backlog Burner systematically tackles our issue backlog - one issue per day, methodically working through technical debt. We learned that incremental progress beats heroic sprints - these agents never get tired, never get distracted, and never need coffee breaks. The PR Fix workflow is our emergency responder - when CI fails, invoke /pr-fix and it investigates and attempts repairs.

These workflows prove that AI agents can handle complex, long-running projects when given the right architecture.

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

Daily Backlog Burner:

gh aw add-wizard githubnext/agentics/workflows/daily-backlog-burner.md

Daily Perf Improver:

gh aw add-wizard githubnext/agentics/workflows/daily-perf-improver.md

Daily QA:

gh aw add-wizard githubnext/agentics/workflows/daily-qa.md

Daily Accessibility Review:

gh aw add-wizard githubnext/agentics/workflows/daily-accessibility-review.md

PR Fix:

gh aw add-wizard githubnext/agentics/workflows/pr-fix.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.

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

Single-repository workflows are powerful, but what happens when you scale to an entire organization with dozens of repositories?

Continue reading: Organization & Cross-Repo Workflows →

This is part 16 of a 19-part series exploring the workflows in Peli’s Agent Factory.

Delighted to have you back on our journey through Peli’s Agent Factory! Now, prepare yourself for something quite peculiar - the room where we watch the watchers!

In our previous post, we explored testing and validation workflows that continuously verify our systems function correctly - running smoke tests, checking documentation across devices, and catching regressions before users notice them. We learned that trust must be verified.

But here’s a question that kept us up at night: what if the infrastructure itself fails? What if MCP servers are misconfigured, tools become unavailable, or agents can’t access the capabilities they need? Testing the application is one thing; monitoring the platform that runs AI agents is another beast entirely. Tool and infrastructure workflows provide meta-monitoring - they watch the watchers, validate configurations, and ensure the invisible plumbing stays functional. Welcome to the layer where we monitor agents monitoring agents monitoring code. Yes, it gets very meta.

These agents monitor and analyze the agentic infrastructure itself:

• MCP Inspector - Validates Model Context Protocol configurations - ensures agents can access tools
• GitHub MCP Tools Report - Analyzes available MCP tools - 5 merged PRs out of 6 proposed (83% merge rate)
• Agent Performance Analyzer - Meta-orchestrator for agent quality - 29 issues created, 14 leading to PRs (8 merged)

Infrastructure for AI agents is different from traditional infrastructure - you need to validate that tools are available, properly configured, and actually working. The MCP Inspector continuously validates Model Context Protocol server configurations because a misconfigured MCP server means an agent can’t access the tools it needs.

GitHub MCP Tools Report Generator has contributed 5 merged PRs out of 6 proposed (83% merge rate), analyzing MCP tool availability and keeping tool configurations up to date. For example, PR #13169 updates MCP server tool configurations.

Agent Performance Analyzer has created 29 issues identifying performance problems across the agent ecosystem, and 14 of those issues led to PRs (8 merged) by downstream agents - for example, it detected that draft PRs accounted for 9.6% of open PRs, created issue #12168, which led to #12174 implementing automated draft cleanup.

We learned that layered observability is crucial: you need monitoring at the infrastructure level (are servers up?), the tool level (can agents access what they need?), and the agent level (are they performing well?).

These workflows provide visibility into the invisible.

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

MCP Inspector:

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

GitHub MCP Tools Report:

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

Agent Performance Analyzer:

gh aw add-wizard https://github.com/github/gh-aw/blob/v0.45.5/.github/workflows/agent-performance-analyzer.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.

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

Most workflows we’ve seen are stateless - they run, complete, and disappear. But what if agents could maintain memory across days?

Continue reading: Multi-Phase Improver Workflows →

This is part 15 of a 19-part series exploring the workflows in Peli’s Agent Factory.

Right this way! Let’s continue our grand tour of Peli’s Agent Factory! Into the verification chamber where nothing escapes scrutiny!

In our previous post, we explored ChatOps workflows - agents that respond to slash commands and GitHub reactions, providing on-demand assistance with full context.

But making code better is only half the battle. We also need to ensure it keeps working. As we refactor, optimize, and evolve our codebase, how do we know we haven’t broken something? How do we catch regressions before users do? That’s where testing and validation workflows come in - the skeptical guardians that continuously verify our systems still function as expected. We learned that AI infrastructure needs constant health checks, because what worked yesterday might silently fail today. These workflows embody trust but verify.

These agents keep everything running smoothly through continuous testing:

• Daily Testify Uber Super Expert - Analyzes test files daily and suggests testify-based improvements - 19 issues created, 13 led to merged PRs (100% causal chain merge rate)
• Daily Test Improver - Identifies coverage gaps and implements new tests incrementally
• Daily Compiler Quality Check - Analyzes compiler code to ensure it meets quality standards

• Daily Multi-Device Docs Tester - Tests documentation across devices with Playwright - 2 merged PRs out of 2 proposed (100% merge rate)
• CLI Consistency Checker - Inspects the CLI for inconsistencies, typos, and documentation gaps - 80 merged PRs out of 102 proposed (78% merge rate)

• CI Coach - Analyzes CI pipelines and suggests optimizations - 9 merged PRs out of 9 proposed (100% merge rate)
• Workflow Health Manager - Meta-orchestrator monitoring health of all agentic workflows - 40 issues created, 5 direct PRs + 14 causal chain PRs merged

The Daily Testify Expert has created 19 issues analyzing test quality, and 13 of those issues led to merged PRs by downstream agents - a perfect 100% causal chain merge rate. For example, issue #13701 led to #13722 modernizing console render tests with testify assertions. The Daily Test Improver works alongside it to identify coverage gaps and implement new tests.

The Multi-Device Docs Tester uses Playwright to test our documentation on different screen sizes - it has created 2 PRs (both merged), including adding —network host to Playwright Docker containers. It found mobile rendering issues we never would have caught manually. The CLI Consistency Checker has contributed 80 merged PRs out of 102 proposed (78% merge rate), maintaining consistency in CLI interface and documentation. Recent examples include removing undocumented CLI commands and fixing upgrade command documentation.

CI Optimization Coach has contributed 9 merged PRs out of 9 proposed (100% merge rate), optimizing CI pipelines for speed and efficiency with perfect execution. Examples include removing unnecessary test dependencies and fixing duplicate test execution.

The Workflow Health Manager has created 40 issues monitoring the health of all other workflows, with 25 of those issues leading to 34 PRs (14 merged) by downstream agents - plus 5 direct PRs merged. For example, issue #14105 about a missing runtime file led to #14127 fixing the workflow configuration.

These workflows embody the principle: trust but verify. Just because it worked yesterday doesn’t mean it works today.

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

Daily Testify Uber Super Expert:

gh aw add-wizard https://github.com/github/gh-aw/blob/v0.45.5/.github/workflows/daily-testify-uber-super-expert.md

Daily Test Improver:

gh aw add-wizard githubnext/agentics/daily-test-improver

Daily Compiler Quality Check:

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

Daily Multi-Device Docs Tester:

gh aw add-wizard https://github.com/github/gh-aw/blob/v0.45.5/.github/workflows/daily-multi-device-docs-tester.md

CLI Consistency Checker:

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

CI Coach:

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

Workflow Health Manager:

gh aw add-wizard https://github.com/github/gh-aw/blob/v0.45.5/.github/workflows/workflow-health-manager.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.

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

But what about the infrastructure itself? Who watches the watchers? Time to go meta.

Continue reading: Tool & Infrastructure Workflows →

This is part 14 of a 19-part series exploring the workflows in Peli’s Agent Factory.

Onwards, onwards! Let’s keep exploring the wonders of Peli’s Agent Factory! To the command center where instant magic happens!

In our previous post, we explored creative and culture workflows - agents that bring joy, build team culture, and create moments of delight. We discovered that AI agents don’t have to be all business; they can have personality while making work more enjoyable.

But sometimes you need help right now, at the exact moment you’re stuck on a problem. You don’t want to wait for a scheduled run - you want to summon an expert agent with a command. That’s where interactive workflows and ChatOps come in. These agents respond to slash commands and GitHub reactions, providing on-demand assistance with full context of the current situation.

We learned that the right agent at the right moment with the right information is a valuable addition to an agent portfolio.

These agents respond to commands, providing on-demand assistance whenever you need it:

• Q - Workflow optimizer that investigates performance and creates PRs - 69 merged PRs out of 88 proposed (78% merge rate)
• Grumpy Reviewer - Performs critical code reviews with personality - creates issues for downstream agents
• Workflow Generator - Creates new workflows from issue requests - scaffolds workflow files

Interactive workflows changed how we think about agent invocation. Instead of everything running on a schedule, these respond to slash commands and reactions - /q summons the workflow optimizer, a reaction triggers analysis. Q (yes, named after the James Bond quartermaster) became our go-to troubleshooter - it has contributed 69 merged PRs out of 88 proposed (78% merge rate), responding to commands and investigating workflow issues on demand. Recent examples include fixing the daily-fact workflow action-tag and configuring PR triage reports with 1-day expiration.

The Grumpy Reviewer performs opinionated code reviews, creating issues that flag security risks and code quality concerns (e.g., #13990 about risky event triggers) for downstream agents to fix. It gave us surprisingly valuable feedback with a side of sass (“This function is so nested it has its own ZIP code”).

Workflow Generator creates new agentic workflows from issue requests, scaffolding the markdown workflow files that other agents then refine (e.g., #13379 requesting AWF mode changes).

We learned that context is king - these agents work because they’re invoked at the right moment with the right context, not because they run on a schedule.

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

Q:

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

Grumpy Reviewer:

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

Workflow Generator:

gh aw add-wizard https://github.com/github/gh-aw/blob/v0.45.5/.github/workflows/workflow-generator.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.

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

While ChatOps agents respond to commands, we also need workflows that continuously verify our systems still function as expected.

Continue reading: Testing & Validation Workflows →

This is part 13 of a 19-part series exploring the workflows in Peli’s Agent Factory.