Mara Kiefer

Welcome back to our journey through Peli’s Agent Factory!

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.

Tool & Infrastructure Workflows

These agents monitor and analyze the agentic infrastructure itself:

MCP Inspector - Validates Model Context Protocol configurations
GitHub MCP Tools Report - Analyzes available MCP tools
Agent Performance Analyzer - Meta-orchestrator for agent quality

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 checks Model Context Protocol server configurations because a misconfigured MCP server means an agent can’t access the tools it needs. The Agent Performance Analyzer is a meta-orchestrator that monitors all our other agents - looking for performance degradation, cost spikes, and quality issues. 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.

Learn More

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

Next Up: Multi-Phase Improver 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 12 of a 16-part series exploring the workflows in Peli’s Agent Factory.

Meet the Workflows: Operations & Release

Jan 13, 2026

Welcome to another stop in Peli’s Agent Factory!

In our previous post, we explored metrics and analytics workflows - the agents that monitor other agents, turning raw activity data into actionable insights. We built our central nervous system for the agent ecosystem, learning that you can’t optimize what you don’t measure.

Now comes the moment of truth: actually shipping software to users. All the quality checks, metrics tracking, and iterative improvements culminate in one critical process - the release. Operations and release workflows handle the orchestration of building, testing, generating release notes, and publishing. These workflows can’t afford to be experimental; they need to be rock-solid reliable, well-tested, and yes, even a bit boring. Let’s explore how automation makes shipping predictable and stress-free.

Operations & Release Workflows

The agents that help us actually ship software:

Release - Orchestrates builds, tests, and release note generation
Daily Workflow Updater - Keeps actions and dependencies current (because dependency updates never stop)

Shipping software is stressful enough without worrying about whether you formatted your release notes correctly. The Release workflow handles the entire orchestration - building, testing, generating coherent release notes from commits, and publishing. What’s interesting here is the reliability requirement: these workflows can’t afford to be creative or experimental. They need to be deterministic, well-tested, and boring (in a good way).

The Daily Workflow Updater taught us that maintenance is a perfect use case for agents - it’s repetitive, necessary, and nobody enjoys doing it manually. These workflows handle the toil so we can focus on the interesting problems.

Learn More

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

Next Up: Security & Compliance Workflows

After all this focus on shipping, we need to talk about the guardrails: how do we ensure these powerful agents operate safely?

Continue reading: Security & Compliance Workflows →

This is part 7 of a 16-part series exploring the workflows in Peli’s Agent Factory.

Meet the Workflows: Issue & PR Management

Jan 13, 2026

Let’s talk collaboration at Peli’s Agent Factory!

In our previous post, we explored documentation and content workflows - agents that maintain glossaries, technical docs, slide decks, and blog content. We learned that AI-generated docs need human review, but they’re dramatically better than having no docs at all.

Now let’s talk about the daily rituals of software development: managing issues and pull requests. GitHub provides excellent primitives for collaboration, but there’s a lot of ceremony involved - linking related issues, merging main into PR branches, assigning work, closing completed sub-issues, optimizing templates. These are small papercuts individually, but they add up to significant friction. Issue and PR management workflows don’t replace GitHub’s features; they enhance them, removing tedious ceremony and making collaboration feel effortless. Let’s see how automation makes GitHub more pleasant to use.

Issue & PR Management Workflows

These agents enhance issue and pull request workflows:

Issue Arborist - Links related issues as sub-issues
Issue Monster - Assigns issues to Copilot agents one at a time
Mergefest - Automatically merges main branch into PR branches
Sub Issue Closer - Closes completed sub-issues automatically
Issue Template Optimizer - Improves issue templates based on usage

Issue management is tedious ceremony that developers tolerate rather than enjoy. The Issue Arborist automatically links related issues, building a dependency tree we’d never maintain manually. The Issue Monster became our task dispatcher for AI agents - it assigns one issue at a time to Copilot agents, preventing the chaos of parallel work on the same codebase. Mergefest eliminates the “please merge main” dance that happens on long-lived PRs. We learned that tiny frustrations add up - each of these workflows removes a small papercut, and collectively they make GitHub feel much more pleasant to use. The Issue Template Optimizer analyzes which fields in our templates actually get filled out and suggests improvements (“nobody uses the ‘Expected behavior’ field, remove it”).

Learn More

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

Next Up: Quality & Hygiene Workflows

While issue workflows help manage collaboration ceremony, we also need agents that maintain codebase health - spotting problems before they escalate.

Continue reading: Quality & Hygiene Workflows →

This is part 4 of a 16-part series exploring the workflows in Peli’s Agent Factory.

Meet the Workflows: Documentation & Content

Jan 13, 2026

Welcome to the documentation corner of Peli’s Agent Factory!

In our previous post, we explored code quality and refactoring workflows - agents that continuously push our codebase toward better design, finding patterns and improvements that humans often miss. These workflows never take a day off, quietly working to make our code cleaner and more maintainable.

Now let’s address one of software development’s eternal challenges: keeping documentation accurate and up-to-date. Code evolves rapidly; docs… not so much. Terminology drifts, API examples become outdated, slide decks grow stale, and blog posts reference deprecated features. The question isn’t “can AI agents write good documentation?” but rather “can they maintain it as code changes?” Documentation and content workflows challenge conventional wisdom about AI-generated technical content. Spoiler: the answer involves human review, but it’s way better than the alternative (no docs at all).

Documentation & Content Workflows

These agents maintain high-quality documentation and content:

Glossary Maintainer - Keeps glossary synchronized with codebase
Technical Doc Writer - Generates and updates technical documentation
Slide Deck Maintainer - Maintains presentation slide decks
Multi-device Docs Tester - Tests documentation site across mobile, tablet, and desktop devices

Documentation is where we challenged conventional wisdom. Can AI agents write good documentation?

The Technical Doc Writer generates API docs from code, but more importantly, it maintains them - updating docs when code changes. The Glossary Maintainer caught terminology drift (“we’re using three different terms for the same concept”).

The Slide Deck Maintainer keeps our presentation materials current without manual updates.

The Multi-device Docs Tester uses Playwright to verify our documentation site works across phones, tablets, and desktops - testing responsive layouts, accessibility, and interactive elements. It catches visual regressions and layout issues that only appear on specific screen sizes.

The Blog Auditor ensures our blog posts stay accurate as the codebase evolves - it flags outdated code examples and broken links.

AI-generated docs need human review, but they’re dramatically better than no docs (which is often the alternative). Validation can be automated to a large extent, freeing writers to focus on content shaping, topic, clarity, tone, and accuracy.

Learn More

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

Next Up: Issue & PR Management Workflows

Beyond writing code and docs, we need to manage the flow of issues and pull requests. How do we keep collaboration smooth and efficient?

Continue reading: Issue & PR Management Workflows →

This is part 3 of a 16-part series exploring the workflows in Peli’s Agent Factory.

Meet the Workflows: Code Quality & Refactoring

Jan 13, 2026

Welcome to the next chapter in Peli’s Agent Factory!

In our previous post, we explored how triage and summarization workflows help us stay on top of incoming activity - automatically labeling issues, creating digestible summaries, and narrating the day’s events. These workflows taught us that tone matters and even simple automation dramatically reduces cognitive load.

Now let’s turn to the agents that continuously improve code quality. Code quality and refactoring workflows work quietly in the background, never taking a day off - they analyze console output styling, spot semantic duplication, identify structural improvements, and find patterns humans miss because they can hold entire codebases in context. These workflows embody the principle that good enough can always become better, and that incremental improvements compound over time. Let’s meet the perfectionist agents.

Code Quality & Refactoring Workflows

These agents make our codebase cleaner and our developer experience better:

Terminal Stylist - Analyzes and improves console output styling (because aesthetics matter!)
Semantic Function Refactor - Spots refactoring opportunities we might have missed
Repository Quality Improver - Takes a holistic view of code quality and suggests improvements

Code quality is where AI agents really shine - they never get bored doing the repetitive analysis that makes codebases better. The Terminal Stylist literally reads our console output code and suggests improvements to make our CLI prettier (and yes, it understands Lipgloss and modern terminal styling). The Semantic Function Refactor finds duplicated logic that’s not quite identical enough for traditional duplicate detection. We learned that these agents see patterns humans miss because they can hold the entire codebase in context. The Repository Quality Improver takes a holistic view - it doesn’t just find bugs, it identifies structural improvements and documentation gaps.

These workflows continuously push our codebase toward better design.

Learn More

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

Next Up: Documentation & Content Workflows

Beyond code quality, we need to keep documentation accurate and up-to-date as code evolves. How do we maintain docs that stay current?

Continue reading: Documentation & Content Workflows →

This is part 2 of a 16-part series exploring the workflows in Peli’s Agent Factory.

Meet the Workflows: Triage & Summarization

Jan 13, 2026

Hi there! 👋 Welcome back to Peli’s Agent Factory.

We’re the GitHub Next team, and we’ve been on quite a journey. Over the past months, we’ve built and operated a collection of automated agentic workflows. These aren’t just demos or proof-of-concepts - these are real agents doing actual work in our githubnext/gh-aw repository and its companion githubnext/agentics collection.

Think of this as your guided tour through our agent factory. We’re showcasing the workflows that caught our attention, taught us something new, or just made our lives easier. Every workflow links to its source Markdown file, so you can peek under the hood and see exactly how it works.

Starting Simple: Automated Issue Triage

To start the tour, let’s begin with one of the simple workflows that handles incoming activity - issue triage. This represents the “hello world” of automated agentic workflows: practical, immediately useful, relatively simple, and impactful.

Our Issue Triage Agent automatically labels and categorizes new issues the moment they’re opened. Let’s take a look at the full workflow:

---
timeout-minutes: 5

on:
  schedule: "0 14 * * 1-5"
  workflow_dispatch:

permissions:
  issues: read

tools:
  github:
    toolsets: [issues, labels]

safe-outputs:
  add-labels:
    allowed: [bug, feature, enhancement, documentation, question, help-wanted, good-first-issue]
  add-comment: {}
---

# Issue Triage Agent

List open issues in ${{ github.repository }} that have no labels. For each unlabeled issue, analyze the title and body, then add one of the allowed labels: `bug`, `feature`, `enhancement`, `documentation`, `question`, `help-wanted`, or `good-first-issue`.

Skip issues that:
- Already have any of these labels
- Have been assigned to any user (especially non-bot users)

After adding the label to an issue, mention the issue author in a comment explaining why the label was added.

Note how concise and readable this is - it’s almost like reading a to-do list for the agent. The workflow runs every weekday at 14:00 UTC, checks for unlabeled issues, and applies appropriate labels based on content analysis. It even leaves a friendly comment explaining the label choice.

In the frontmatter, we define permissions, tools, and safe outputs. This ensures the agent only has access to what it needs and can’t perform any unsafe actions. The natural language instructions in the body guide the agent’s behavior in a clear, human-readable way.

Automated Activity Summarization

To continue the tour, let’s look briefly at two automated summarization workflows that help us stay on top of repository activity. These agents digest large amounts of information and present it in a concise, readable format.

First, the Weekly Issue Summary creates digestible summaries complete with charts and trends (because who has time to read everything?)

Next, the Daily Repo Chronicle marrates the day’s activity like a storyteller - seriously, it’s kind of delightful.

Learnings

What surprised us most about this category?

First, the reduction of cognitive load. Having these agents handle triage and summarization freed up mental bandwidth for more important work. We no longer had to constantly monitor incoming issues or sift through activity logs - the agents did it for us, delivering only the essentials. This drastically reduced context switching and decision fatigue.

Second, the tone matters. When the Daily Repo Chronicle started writing summaries in a narrative, almost journalistic style, people actually wanted to read them. AI agents don’t have to be robotic - they can have personality while still being informative.

Third, customization is key. Triage differs in every repository. Team needs for activity summaries and actions that arise from them differ in every repository. Tailoring these workflows to our specific context made them far more effective. Generic agents are okay, but customized ones are game-changers.

Finally, these workflows became part of our routine. The Daily Repo Chronicle was a morning regular, giving us a quick overview of what happened overnight while we sipped. For teams that move fast using agents, these are key.

Next Up: Code Quality & Refactoring Workflows

Now that we’ve explored how triage and summarization workflows help us stay on top of incoming activity, let’s turn to the agents that continuously improve code quality.

Continue reading: Code Quality & Refactoring Workflows →

Learn More

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

This is part 1 of a 16-part series exploring the workflows in Peli’s Agent Factory.

Welcome to Peli's Agent Factory

Jan 12, 2026