Agentic AI for Maintainers

Getting the Most from GitHub Copilot Enterprise

Nate Waddington
Head of Mentorship & Documentation, CNCF

Maintainer Summit — KubeCon + CloudNativeCon Europe 2026

March 22, 2026

The CNCF + GitHub Partnership

CNCF has partnered with GitHub to provide Copilot Enterprise to project maintainers.

What's in the bundle:

  • Copilot Coding Agent — autonomous, async task execution
  • Copilot Code Review — automated PR analysis
  • Contextual Chat — codebase-aware Q&A

How to get access:

Apply at servicedesk.cncf.io → Program Management

CC BY 4.0 — Cloud Native Computing Foundation |

Augmentation, Not Automation

Two findings from Stanford's Digital Economy Lab (2025):

  • In occupations where AI automates work, early-career workers experienced a 16% relative employment decline. [1]
  • In occupations where AI augments work, employment growth remained stable — even in the most AI-exposed roles. [1]

What this means for maintainers:

  • The goal is increased agency, not replacement
  • AI generates; humans improve and approve
  • Your expertise, judgment, and project knowledge remain essential
CC BY 4.0 — Cloud Native Computing Foundation |

The Agentic Spectrum

Completions Chat Agent Mode Coding Agent Agentic Workflows
Where Editor Editor sidebar Editor GitHub cloud GitHub Actions
Interaction Keystroke Conversational Real-time loop Async / background Scheduled / event-driven
Autonomy You write, it suggests You ask, it answers It plans and acts, you guide and approve It works alone; you review It runs continuously; you oversee
Output Code snippets Answers + code Multi-file changes Branch + PR PRs, labels, comments, reports

Today we're focusing on the right side: agent mode, the coding agent, and agentic workflows.

CC BY 4.0 — Cloud Native Computing Foundation |

Agent Mode: Your Pair Programmer

Synchronous, in your editor. You give it a goal in natural language; it plans, edits files, runs commands, reads failures, fixes them, and loops — while you direct, edit, and approve.

When to reach for agent mode:

  • Exploring an unfamiliar codebase
  • Prototyping a feature or fixing interactively
  • Debugging — let it read errors and iterate
  • Refactoring with immediate feedback
CC BY 4.0 — Cloud Native Computing Foundation |

Agent Mode: Tips for Better Results

  • Be specific: "Add a health check endpoint to the API server" beats "help with the API"
  • Seed context: Point it at the spec file, paste a schema, reference an issue
  • Extend with MCP: Connect external tools (databases, APIs, other repos) [2]
CC BY 4.0 — Cloud Native Computing Foundation |

Coding Agent: Your Async Teammate

Asynchronous, in the cloud. Assign a GitHub issue to Copilot. It spins up a workspace via GitHub Actions, writes code, runs tests, and opens a PR for your review.

The workflow:

  1. Create or select an issue with clear acceptance criteria
  2. Assign the issue to Copilot
  3. Copilot creates a branch, implements, runs CI
  4. You get a PR — review, comment, request changes, or merge
CC BY 4.0 — Cloud Native Computing Foundation |

Coding Agent: What Works

(and What Doesn't)

Sweet-spot tasks:

  • Adding or extending tests
  • Small refactors (extract helpers, rename services)
  • Documentation updates and fixes
  • Bug fixes in well-tested repos

Less helpful for:

  • Massive rewrites or architectural changes
  • Repos with zero test coverage
CC BY 4.0 — Cloud Native Computing Foundation |

Agentic Workflows: Continuous AI

Automated, in-GitHub Actions. Define maintenance tasks in Markdown. Coding agents execute them on a schedule — daily, on events, or on demand. [10]

What it looks like:

  1. Author a workflow in .github/workflows/my-workflow.md
  2. Describe the goal in plain language (not YAML)
  3. It runs via GitHub Actions using a coding agent
  4. Results appear as comments, labels, issues, or PRs
  5. You review
CC BY 4.0 — Cloud Native Computing Foundation |

Agentic Workflows: Maintainer Use Cases

  • Continuous triage — label, summarize, and route new issues
  • Continuous documentation — keep READMEs aligned with code changes
  • Continuous testing — assess coverage and add high-value tests
  • Continuous quality — investigate CI failures and propose fixes
  • Backlog processing — systematically work through stale issues [11]
CC BY 4.0 — Cloud Native Computing Foundation |

When to Use Which

Agent Mode Coding Agent Agentic Workflows
You want to... Explore, prototype, debug Clear backlog, batch work Automate ongoing maintenance
Your role Steering in real time Reviewing a PR later Overseeing a continuous process
What it's for Tight feedback loops Well-scoped, well-tested tasks Repetitive, recurring work
Think of it as... Pair programming Delegating a ticket Hiring a night shift

The combined workflow:

Prototype in agent mode → file an issue → coding agent implements → agentic workflows maintain → code review catches issues → you merge [4]

CC BY 4.0 — Cloud Native Computing Foundation |

Teaching Copilot Your Project

Two files that change everything:

  1. .github/copilot-instructions.md
  2. AGENTS.md
CC BY 4.0 — Cloud Native Computing Foundation |

Teaching Copilot Your Project

.github/copilot-instructions.md — Your project's house rules

Copilot reads this on every interaction. Put your conventions here:

  • Language, framework, and version constraints
  • Code style and naming patterns
  • Testing requirements and preferred frameworks
  • Architectural decisions and legacy constraints
CC BY 4.0 — Cloud Native Computing Foundation |

Teaching Copilot Your Project

AGENTS.md — What the coding agent can and can't do

Lives at your repo root. Defines the agent's persona, available commands, and boundaries:

  • What to run: npm test, make lint, etc.
  • File boundaries: "never edit files in /legacy"
  • Behavioral constraints: "never commit secrets"

Custom agents — Specialists for larger projects

Define them in .github/agents/*.agent.md to create focused personas: [7]

  • For example: a test agent that writes and maintains test suites
CC BY 4.0 — Cloud Native Computing Foundation |

Cross-Repo Workflows with MCP

MCP (Model Context Protocol) is an open standard for connecting AI agents to external tools and data sources. [2] In agent mode, it gives you access across your organization:

  • Search issues, PRs, and code in other repos
  • Check CI status and review workflow results
  • Create issues and PRs in related repositories
  • Query external systems (APIs, databases, project trackers)
CC BY 4.0 — Cloud Native Computing Foundation |

Security Is Built In

What runs automatically on every coding agent PR:

  • CodeQL security analysis
  • Secret scanning — catches accidentally committed keys and tokens
  • Dependency checks against the GitHub Advisory Database
  • Automated code review for quality issues

The agent attempts to fix issues before submitting the PR. [3]

You still review PRs.

CC BY 4.0 — Cloud Native Computing Foundation |

Security Is Built In

What You Should Know

  • Review all agent-generated PRs — agents accelerate, they don't replace your judgment
  • The LF AI policy [8] is the baseline — projects can add stricter guidance, but not looser
CC BY 4.0 — Cloud Native Computing Foundation |

Real-World: KubeStellar's Agentic Experiment

A CNCF sandbox project put these tools to the test with Andy Anderson and Ashley Wolf — all initial code and every PR were authored by coding agents:

Metric Result
Total PRs 2,217 opened, 1,907 merged in 9 weeks
Issues auto-detected 75% in first 5 weeks (348 auto-qa issues total)
Copilot PRs merged 197 — 51% acceptance rate
GitHub Actions runs 144,126

51% fully automated + 49% augmented = 100% useful, not 51% success / 49% waste. Half the work was merged untouched; the other half gave reviewers a head start instead of a blank page.

CC BY 4.0 — Cloud Native Computing Foundation |

The Pattern That Works

Detection → Auto-Fix → Human Review → Loop

Bug detected
  → Issue created
    → Copilot assigned automatically
      → PR opened
        → CI validates
          → Human reviews and merges

The same pattern works at any scale — start with one workflow and grow.

CC BY 4.0 — Cloud Native Computing Foundation |

Your Next Monday Back

Three things you can do when you're back at your desk:

1. Get access

Apply at servicedesk.cncf.io → Program Management. Provide your GitHub handle.

2. Add copilot-instructions.md to one repo

Write your project's conventions in .github/copilot-instructions.md. Start simple — you can iterate.

3. Try the coding agent on one small issue

Pick a well-scoped issue (a docs fix, a missing test, a lint violation), assign it to Copilot, and review the PR it opens.

CC BY 4.0 — Cloud Native Computing Foundation |

Questions?

Resources

CNCF + GitHub Partnership

CC BY 4.0 — Cloud Native Computing Foundation |

Resources (continued)

GitHub Docs & Blog Posts

CC BY 4.0 — Cloud Native Computing Foundation |

Resources (continued)

The Broader Ecosystem: AAIF

In December 2025, the Linux Foundation created the Agentic AI Foundation (AAIF) — a sibling foundation to the CNCF.

Three foundational projects:

  • MCP (Model Context Protocol) — the open standard for connecting agents to external tools. Contributed by Anthropic.
  • AGENTS.md — the standard for agent instruction files. Contributed by OpenAI.
  • Goose — an open-source, local-first agent framework. Contributed by Block.

MCP and AGENTS.md aren't GitHub-only concepts — they're open standards governed by the Linux Foundation, the same umbrella as the CNCF. [9]

CC BY 4.0 — Cloud Native Computing Foundation |

References

[1] Brynjolfsson, E., Chandar, B., & Chen, R. "Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence." Stanford Digital Economy Lab, November 2025.
https://digitaleconomy.stanford.edu/app/uploads/2025/11/CanariesintheCoalMine_Nov25.pdf

[2] "5 Ways to Transform Your Workflow Using GitHub Copilot and MCP." GitHub Blog.
https://github.blog/ai-and-ml/github-copilot/5-ways-to-transform-your-workflow-using-github-copilot-and-mcp/

[3] "Copilot Coding Agent Now Automatically Validates Code Security and Quality." GitHub Changelog, October 2025.
https://github.blog/changelog/2025-10-28-copilot-coding-agent-now-automatically-validates-code-security-and-quality/

CC BY 4.0 — Cloud Native Computing Foundation |

References (continued)

[4] "The Difference Between Coding Agent and Agent Mode in GitHub Copilot." GitHub Blog.
https://github.blog/developer-skills/github/less-todo-more-done-the-difference-between-coding-agent-and-agent-mode-in-github-copilot/

[5] "Using Custom Instructions to Unlock the Power of Copilot." GitHub Docs.
https://docs.github.com/en/copilot/tutorials/use-custom-instructions

[6] "How to Write a Great agents.md: Lessons from Over 2,500 Repositories." GitHub Blog.
https://github.blog/ai-and-ml/github-copilot/how-to-write-a-great-agents-md-lessons-from-over-2500-repositories/

CC BY 4.0 — Cloud Native Computing Foundation |

References (continued)

[7] "Creating Custom Agents for Copilot Coding Agent." GitHub Docs.
https://docs.github.com/en/copilot/how-tos/use-copilot-agents/coding-agent/create-custom-agents

[8] "Policy Guidance Regarding Use of Generative AI Tools for Open Source Software Development." The Linux Foundation.
https://www.linuxfoundation.org/legal/generative-ai

[9] "Linux Foundation Announces the Formation of the Agentic AI Foundation (AAIF)." The Linux Foundation, December 2025.
https://www.linuxfoundation.org/press/linux-foundation-announces-the-formation-of-the-agentic-ai-foundation

CC BY 4.0 — Cloud Native Computing Foundation |

References (continued)

[10] "Automate Repository Tasks with GitHub Agentic Workflows." GitHub Blog, February 2026.
https://github.blog/ai-and-ml/automate-repository-tasks-with-github-agentic-workflows/

[11] Syme, D. "Repo Assist: A Repository Assistant." February 2026.
https://dsyme.net/2026/02/25/repo-assist-a-repository-assistant/

[12] "GitHub Copilot Enterprise for CNCF Maintainers." CNCF Contributor Blog, December 2025.
https://contribute.cncf.io/blog/2025/12/16/github-copilot-enterprise-for-maintainers

CC BY 4.0 — Cloud Native Computing Foundation |

Colophon

This presentation was built with Marp and is available under CC BY 4.0.

Source: github.com/nate-double-u/talks
Path: KCCN-EU-Maintainer-Summit-2026/agentic-ai/presentation/slides.md
Theme: Maintainer Summit 2026 (based on KubeConEU26-Maintainer-Summit_ppt.pptx)

SLIDE 1 — Act 1: Why This Matters

Speaker notes: 1. Welcome, introduce yourself 2. Frame the session: practical strategies, not a product pitch 3. "By the end of this, you'll have concrete things to take back to your projects"

SLIDE 2

Speaker notes: Don't linger — the audience wants to know HOW to use it, not WHAT it is 1. Quick context: this exists, it's free for maintainers, here's what's in it 2. Mention eligibility: verified against maintainers.cncf.io --- - Blog announcement: [12] contribute.cncf.io - If they haven't applied yet, the link is on the references slide at the end

SLIDE 3

Speaker notes: 1. This is the through-line for the entire talk 2. Every tool I'm about to show you keeps the human in the loop --- - Source: Brynjolfsson, Chandar & Chen, "Canaries in the Coal Mine?" (Nov 2025), pp. 11, 16 https://digitaleconomy.stanford.edu/app/uploads/2025/11/CanariesintheCoalMine_Nov25.pdf - The 16% figure is from p.16 (Fact 4): "most AI-exposed occupations" after firm-time controls - The automate vs augment split is from p.11 (Fact 3, Figure 3 Panels B & C) - If challenged: "The 16% is about exposed occupations broadly; the paper separately shows the decline concentrates in automative applications, not augmentative ones."

SLIDE 4 — Act 2: The Toolkit

Speaker notes: - This is a quick visual overview — don't spend more than 90 seconds here 1. The progression left to right is: increasing autonomy, decreasing real-time oversight 2. Most maintainers are already using completions and chat 3. New column: Agentic Workflows (technical preview, Feb 2026) — the "Continuous AI" layer - Think of it as CI/CD for AI-assisted maintenance: triage, docs sync, test improvement - Authored in Markdown, executed by coding agents in GitHub Actions - Read-only by default with safe outputs — strong guardrails by design --- - Sets up the next three slides

SLIDE 5

Speaker notes: 1. This is the "senior dev pair programming with you" metaphor 2. Key point: you're in the loop the whole time — this is augmentation 3. Agent mode supports multiple models — mention the model picker briefly

SLIDE 6

Speaker notes: --- - MCP = Model Context Protocol, an open standard for connecting AI to external tools - [2] = GitHub blog on MCP, add to references

SLIDE 7

Speaker notes: 1. This is the "diligent teammate clearing the backlog" metaphor 2. Emphasize: it runs YOUR CI pipeline, YOUR linters, YOUR tests — it follows your rules --- - Built-in security: CodeQL, secret scanning, dependency checks run automatically [3] - Each run consumes GitHub Actions minutes AND premium requests — be strategic - [3] = GitHub changelog on security validation

SLIDE 8

Speaker notes: 1. Stress: pick something small and well-tested for the first try

SLIDE 9

Speaker notes: 1. "Continuous AI" — GitHub Next's framing, parallel to CI/CD 2. Guardrails: read-only by default, safe outputs constrain what the agent can do (specific labels only, title-prefixed PRs only, never merges) --- - [10] = GitHub blog on agentic workflows

SLIDE 10

Speaker notes: 1. Don Syme's Repo Assist is a concrete example: cleared over half the technical debt across 4 F# repos in a weekend using this --- 3. [11] = Don Syme's blog post on Repo Assist

SLIDE 11

Speaker notes: 1. This is the "peanut butter and jelly" slide — they're meant to be used together 2. Key takeaway: agent mode for novel work, coding agent for well-defined work, agentic workflows for the recurring stuff nobody wants to do manually --- - Cite: [4] GitHub blog on agent mode vs coding agent

SLIDE 12

Speaker notes: 1. This is where the talk gets actionable — these are files they can add TODAY

SLIDE 13

Speaker notes: 1. copilot-instructions.md: first 4,000 characters matter most for code review 2. SHOW a real example from the companion repo here --- - You can also use path-specific instructions (.github/instructions/*.instructions.md) with applyTo frontmatter for rules that only apply to certain file types/paths - [5] = GitHub docs on custom instructions

SLIDE 14

Speaker notes: 1. AGENTS.md at repo root: think of it like onboarding instructions for a new contributor — general rules that apply to everything 2. Custom agents in .github/agents/ extend AGENTS.md with specialist personas — they inherit the base rules but add their own scope and constraints 3. All agent files live in your repo, versioned alongside your code — easy to review in PRs --- - AGENTS.md is an open standard (AAIF/LF), not GitHub-specific — same file works with other tools

SLIDE 15

Speaker notes: 1. Don't go too deep on MCP setup — point to the docs, show the companion repo example 2. IMPORTANT NUANCE on cross-repo: - MCP gives agent mode "eyes" into other repos (search, read, check status) via the GitHub API - But the actual code edits from agent mode still happen in your local workspace - The coding agent works within ONE repo per task — it doesn't hop between repos 3. For the audience: "Think of MCP as giving Copilot read access across your org, while the coding agent handles the writes one repo at a time" --- - Cross-repo workflow in practice: use MCP in agent mode to investigate across repos, then file separate issues for the coding agent in each repo - Neither tool does true "edit repo A and repo B in one atomic operation" - MCP is configured via .vscode/mcp.json — the GitHub MCP server is first-party

SLIDE 16

Speaker notes: 1. This is where you tie security back to augmentation: the tooling catches a lot, but YOU are the reviewer 2. The security checks run without extra licensing — included in Enterprise --- - [3] = GitHub changelog on security validation - TODO: Review CodeQL docs to be able to explain it in detail if asked: https://docs.github.com/en/code-security/code-scanning/introduction-to-code-scanning/about-code-scanning-with-codeql — Key points: static analysis engine, treats code as queryable data, finds security vulns (not style), free for public repos, supports Go/Python/JS/TS/Java/C++ and more.

SLIDE 17

Speaker notes: 1. IP considerations: Microsoft provides indemnification for unmodified Copilot suggestions, but maintainers should still review for license compatibility 2. LF policy sets licensing/attribution minimums — projects can add stricter rules, not waive those. If challenged: "The minimums around licensing and attribution aren't optional." --- - Invisible character attacks are a known research concern — compromised upstream code could influence suggestions. Good reason to review carefully. - [8] = LF generative AI policy

SLIDE 18

Speaker notes: 1. SET THE SCENE: KubeStellar Console had massive contribution volume — 1,300+ PRs in 5 weeks, 229 issues filed. Small maintainer team couldn't keep up manually. 2. WALK THE NUMBERS: 952 PRs merged total. 75% of issues were auto-detected by CI workflows — not humans finding them. Copilot generated 101 PRs that got merged. 3. LAND THE PUNCHLINE: 52% acceptance rate. Tie back to Slide 3 (augmentation): "That's not a failing grade. It means half the work was fully handled, and for the other half, Copilot gave reviewers a starting point instead of a blank page. That's augmentation." 4. CREDIT: "This work was done with Andy Anderson and Ashley Wolf." --- - If time is tight, speed through the numbers and focus on the 52% takeaway - DCO NOTE (for your info, not on slide): DCO + AI-generated code is still actively debated. The core issue: DCO requires you to certify you have the right to submit the code, but U.S. Copyright Office says purely AI-generated works aren't copyrightable. So who signs? Community is splitting: some require disclosure trailers (Assisted-by:), some require human review/transformation, some projects (QEMU) have restricted pure AI contributions. A March 2026 blog post "DCO and AI is a no-go" (brokenco.de/2026/03/02/copyright-ai.html) lays out the argument directly. Red Hat also has a good writeup: redhat.com/en/blog/ai-assisted-development-and-open-source-navigating-legal-issues

SLIDE 19

Speaker notes: 1. This is the pipeline diagram from the KubeStellar work 2. Walk through each step briefly — the audience should see how it maps to the tools you just explained (coding agent = the auto-fix, code review = CI validates) 3. The key insight: the loop is fully automated, but a human is always the final gate 4. Encourage the audience: "You don't need 37 workflows to start. Pick one pain point, write one workflow, and let the coding agent handle the fixes." --- - "Goodnight agent" example: an agent that runs nightly to update documentation - This is the bridge to Act 3 — "here's how to start"

SLIDE 20 — Act 3: Your Next Move

Speaker notes: 1. Keep this punchy — these are concrete, low-risk actions 2. Step 1 is for anyone who hasn't applied yet 3. Step 2 is the lowest-effort, highest-value thing — even without the coding agent, copilot-instructions.md improves every Copilot interaction in that repo 4. Step 3 is where they see the magic — but stress: pick something small and well-tested for the first try. Don't start with a massive refactor. --- - If they already have access, they can skip to steps 2 and 3

SLIDE 21

Speaker notes: 1. Open for Q&A — aim for ~5 minutes --- - Likely questions to prep for: - "How does this work with CLAs/DCOs?" (see your Slide 12 notes) - "What about CodeQL?" (see your Slide 11 notes) - "Can I use this with [other AI tool]?" — MCP is an open standard, AGENTS.md is too - "What models does it use?" — model picker lets you choose; Auto mode recommended - "Is my code used for training?" — No, Enterprise code is not used for training

SLIDE 22

Speaker notes: 1. This is the "take a photo of this slide" moment 2. All the references are also on the next slides with full citations --- - TODO: Add companion repo section back once repo is created. Include working examples of copilot-instructions.md, AGENTS.md, custom agents, and MCP config — they can fork and adapt

SLIDE 23

SLIDE 24

Speaker notes: 1. This slide positions the talk as "not a GitHub sales pitch" — the standards are open 2. AAIF is to agentic AI what CNCF is to cloud infrastructure --- - Goose is interesting but out of scope for today — it's a local agent framework, model-agnostic (works with any LLM), Apache 2.0 licensed - If someone asks about Goose: "It's an open-source alternative to agent mode that runs on your machine with whatever model you choose. Worth exploring, but today we're focused on what's included in the CNCF's Enterprise bundle." - AAIF had 146 member organizations by Feb 2026 — growing fast - David Nalley (AWS, former CNCF experience) chairs the governing board - [9] = LF press release on AAIF formation

SLIDE 25 (References 1/4)

SLIDE 26 (References 2/4)

SLIDE 27 (References 3/4)

SLIDE 28 (References 4/4)

SLIDE 29 (Colophon)