Continue to fly high with these GitHub Copilot resources 🚀

[Akash1134]

GitHub Copilot — Agents, CLI, and real-world workflows 🚀

Thanks for joining our webinar session. We've got your next steps and more resources below:

We got into the stuff that changes how you actually work day-to-day: building custom agents, using Copilot from the terminal, connecting it to external tools with MCP, and seeing what agentic workflows look like in practice.

Below is a recap of what we covered, along with resources you can bookmark and come back to as you start experimenting on your own.

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💡 What we covered

Topic	The short version	Something to try
Custom agents	You can teach Copilot your project's conventions with plain-language instruction files. It picks up on patterns, coding standards, even build quirks.	Drop a copilot-instructions.md in your repo's .github folder and see how the suggestions change.
Agentic workflows	Agent mode doesn't just suggest code — it runs commands, catches its own errors, and iterates until the task is done.	Switch to Agent in VS Code Copilot Chat and give it a small refactor. Watch what happens.
Copilot CLI	You can talk to Copilot from your terminal. Make code changes, create PRs, summarize commits — no IDE needed.	Run copilot in a project folder and try: "What changed in this repo this week?"
MCP (Model Context Protocol)	MCP lets you connect Copilot to external tools and data sources so it has more context to work with.	Set up the GitHub MCP server in your IDE — it takes a few minutes.
Instructions that stack	Repo-level, path-specific, and org-level instructions all combine. The more you add over time, the smarter Copilot gets about your codebase.	Start with 5-10 lines of repo-wide instructions. You can always add more later.
The WRAP approach	Write good issues, Refine your instructions, keep tasks Atomic, and Pair with the agent. It's a simple way to get consistent results.	Pick a small backlog item, write a clear issue, and assign it to Copilot coding agent.

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📚 Dig deeper

Here are the resources we referenced during the session, plus a few extras worth bookmarking.

Agents & agentic workflows

Resource	What it covers
GitHub Copilot: The Agent Awakens	How agent mode works, what Copilot Edits does, and where autonomous coding agents are headed
WRAP Up Your Backlog with Copilot Coding Agent	A practical framework — Write issues, Refine instructions, Atomic tasks, Pair with the agent
Creating Custom Agents	Walk-through for setting up specialized agents for tasks your team does often
5 Ways to Use Copilot Coding Agent	Concrete examples — tech debt, dependency updates, test coverage, and more

Custom instructions & context

Resource	What it covers
Adding Repository Custom Instructions	How to set up copilot-instructions.md, path-specific instructions, and AGENTS.md
Customization Cheat Sheet	One-page reference for every way you can shape Copilot's responses
About Agentic Memory	How Copilot remembers things about your repo across sessions

CLI workflows

Resource	What it covers
About GitHub Copilot CLI	Overview of interactive and programmatic modes, what you can do, and how permissions work
CLI for Beginners: Interactive vs. Non-Interactive Mode	Hands-on walkthrough with real examples
Installing Copilot CLI	Setup instructions for macOS, Linux, and Windows

MCP & extensibility

Resource	What it covers
About Model Context Protocol (MCP)	What MCP is, how the GitHub MCP server works, and where to find more servers
Enhancing Agent Mode with MCP	Tutorial — connect external tools to agent mode step by step
Extending Copilot Chat with MCP Servers	How to configure MCP servers in your IDE
GitHub MCP Registry	Browse community and partner MCP servers you can add

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❓ Common questions

What if I haven't done Copilot 101 yet?

No worries — the Copilot getting started post covers the fundamentals (prompt crafting, model selection, getting started). It's a good place to start if any of the topics feel unfamiliar.

What's the difference between agent mode and the coding agent?

Agent mode lives in your IDE — you watch it work in real time as it edits files, runs commands, and fixes its own mistakes. The coding agent works in the background on GitHub — you assign it an issue and it opens a PR when it's done. Different tools for different situations.

Do custom instructions really make a difference?

They do. Even a short file that describes your stack, naming conventions, and test patterns means Copilot stops guessing about those things. Over time, you keep adding to it and the suggestions get more specific to how your team actually works.

How do I get Copilot CLI?

It's a standalone binary now — separate from the old gh copilot extension (which has been retired). The install guide covers macOS, Linux, and Windows.

Can I bring my own models?

Yep. Copilot CLI works with OpenAI-compatible endpoints, Azure OpenAI, Anthropic, and local models like Ollama. You just set a couple of environment variables (COPILOT_PROVIDER_BASE_URL and COPILOT_MODEL).

What's MCP in plain terms?

It's a way to give Copilot access to things outside your code — databases, APIs, internal tools. The GitHub MCP server comes built in, and there's a registry of community-built servers you can add.

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🛠️ Where to go from here

You don't need to do all of this — just pick one or two things that caught your attention during the session.

Start small

• Add a .github/copilot-instructions.md to one of your repos. Even 5-10 lines about your stack and conventions is enough to see a difference.
• Try agent mode on something real — a small refactor, a bug fix, a test you've been meaning to write.
• Install Copilot CLI and run copilot in a project folder. Ask it something simple to get a feel for it.

When you're ready for more

• Pick a backlog issue and assign it to Copilot coding agent. Dependency updates and test coverage gaps are good first candidates.
• Set up the GitHub MCP server in your IDE — it only takes a few minutes.
• Add path-specific instructions (e.g. frontend.instructions.md, backend.instructions.md) so Copilot knows the difference between parts of your codebase.

Keep going

• Share what's working for you in Copilot Conversations — other folks benefit from seeing real examples.
• When Copilot gets something wrong, add a note to your instructions file. They get better over time.

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If you're newer to Copilot, the Copilot Getting Started post is a good starting point before diving into the topics above.

Questions? Ideas? Drop a comment below — we're around. 👇

[rubyaj24]

For teams already using Copilot agent workflows in production — what changed the most after introducing custom instructions and MCP integration? Did it actually improve code quality/context awareness, or mostly just speed up repetitive tasks?

[Rafhael-Oliveira-IA]

Take a look here
https://github.com/Rafhael-Oliveira-IA/brasa-ai-core-engine

[Meshal1212222]

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