Designing Effective GitHub Copilot Agents
Key Points
- GitHub Copilot now supports multiple custom agents defined in agent.md files, letting you create specialized “team members” (e.g., doc, test, security) with distinct roles and rules.
- Agents often fail because their descriptions are too vague; you must give precise, task‑oriented instructions (e.g., “QA engineer writing React tests, never modify source code”).
- Effective agent.md files include early, detailed comments with exact flags, concrete code snippets that show the desired style, clear boundaries on what the agent may not touch (e.g., production configs, secrets), and explicit stack information such as versions and key dependencies.
- Successful agents cover six core areas—comments, testing, project structure, code style, workflow, and boundaries—and can follow a three‑tier model: actions the agent can take, questions it must ask first, and prohibited actions.
- To create your first agent, start with a single, simple task, define a concise name, one‑line description, persona, and boundaries; you can even have Copilot generate a starter file that you review and refine, then expand into a full team of specialized agents.
Sections
- Defining Effective Copilot Agents - The video explains how to craft precise agent.m MD files for GitHub Copilot by using detailed comments, concrete code snippets, clear boundaries, and specific stack/version information to avoid vague failures.
- Designing Minimal Purpose‑Built Agents - The speaker recommends starting with a concise agent name, one‑line description, and defined persona, setting strict boundaries on what it may modify, using Copilot to generate starter code, and illustrates this approach with simple doc, test, and API agents.
Full Transcript
# Designing Effective GitHub Copilot Agents **Source:** [https://www.youtube.com/watch?v=7353uEUBH78](https://www.youtube.com/watch?v=7353uEUBH78) **Duration:** 00:04:55 ## Summary - GitHub Copilot now supports multiple custom agents defined in agent.md files, letting you create specialized “team members” (e.g., doc, test, security) with distinct roles and rules. - Agents often fail because their descriptions are too vague; you must give precise, task‑oriented instructions (e.g., “QA engineer writing React tests, never modify source code”). - Effective agent.md files include early, detailed comments with exact flags, concrete code snippets that show the desired style, clear boundaries on what the agent may not touch (e.g., production configs, secrets), and explicit stack information such as versions and key dependencies. - Successful agents cover six core areas—comments, testing, project structure, code style, workflow, and boundaries—and can follow a three‑tier model: actions the agent can take, questions it must ask first, and prohibited actions. - To create your first agent, start with a single, simple task, define a concise name, one‑line description, persona, and boundaries; you can even have Copilot generate a starter file that you review and refine, then expand into a full team of specialized agents. ## Sections - [00:00:00](https://www.youtube.com/watch?v=7353uEUBH78&t=0s) **Defining Effective Copilot Agents** - The video explains how to craft precise agent.m MD files for GitHub Copilot by using detailed comments, concrete code snippets, clear boundaries, and specific stack/version information to avoid vague failures. - [00:03:25](https://www.youtube.com/watch?v=7353uEUBH78&t=205s) **Designing Minimal Purpose‑Built Agents** - The speaker recommends starting with a concise agent name, one‑line description, and defined persona, setting strict boundaries on what it may modify, using Copilot to generate starter code, and illustrates this approach with simple doc, test, and API agents. ## Full Transcript
If you're using GitHub Copilot, then you
must know about this new feature. GitHub
Copilot now lets you define multiple
custom agents using agent.m MD files. So
instead of one generalpurpose assistant,
you can create a whole team, a doc
agent, a test agent, a security agent,
each with its own job description and
rules. But in order to use these agents
efficiently, you need to understand how
to define them right. And in this video,
I will show you how. So keep watching
and let's dive in. So why most agents
fail? According to the GitHub's own
research, based on the analysis of more
than 2,500
repos, agent MD files fail for one
simple reason. It's just too vague. You
are a helpful coding assistance. This
sounds nice, but it does not tell the
model what it should actually do. You
need to specify it. For example, you are
a QA engineer who writes tests for React
components and never modifies source
code. But it's not just this. GitHub
researchers found a consistent pattern
in agency MDS at work and that's what
you should do. First put comments early
and not just the generic one like yarn
run or yarn install. Give your exact
comments including flags. What happened?
What happened? [sighs and gasps]
and use code examples over explanations.
One real code snippet showing your
coding style is better than paragraphs
of rules. Set clear boundaries. Tell the
agent what it should never touch.
Production configs, secrets, or specific
folders. And be specific about your
stack. include versions and key
dependencies and make sure you cover
these six areas that are shown in the
most successful agent MD files comments
testing project structure code style g
workflow and as I already said
boundaries GitHub also shares an example
of a doc agent file that is basically a
blueprint of the good agent MD file it
defined finds a role technical writer
and states where it should read from and
where it should write to. Specifies the
ST and includes comments such as dogs
build and markdown linty. And I like
this three tier boundaries idea things
that agent should do, things it should
ask first and things it should never do
like modifying source code or committing
secrets. So, how do you actually start
writing your first agent without
overengineering it? And that's what
GitHub suggests. Pick one simple task.
Don't build a general helper agent and
start minimal agent name, a oneline
description, and a clear persona. Then
define boundaries, where it should write
and what it should never do. Or you can
even be smarter and just ask Copilot to
write an agent for you. Copilot can
generate you a starter file that you
then can review and adjust. For example,
using some custom comments from your
repo and voila, it's ready.
>> And once you got the idea, you can start
building your own team of agents. So
which agents are worth building? These
are few examples from my projects. A doc
agent that reads your source files and
generates documentation but never
changes the original code. A test agent
that writes and runs test but never
deletes or skips them to fix it. or you
can add an API agent that will build you
a new endpoints but will ask you
permissions in case if it needs to
change database schema. So these are
simple rules of building good agents and
if you have any examples of the agents
you build or something to add to this
please let me know in the comments below
the video. Thank you so much for
watching my video and don't forget to
hit the like button and subscribe.