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INTRODUCTION & MOTIVATION (“THE WHY”)
I'm reading and seeing many guides for agentic flows using windsurf_ai, cursor_ai, cline, and roo_code. But for some reason, Copilot agent mode in VS Code remains quite silent. I’ll guess it’s because it’s still in beta and requires installing VS Code Insiders. I’m here to share a bit of my experience with it and, more importantly, why I chose to experiment with it.
Let’s start first with The Why—why not just use the others? They are fine choices and might even be better; I'm not here to compare today. My organization already owns a GitHub enterprise account and has GitHub Copilot available for every developer, which makes it an easy choice. Going through the hassles of a new vendor, compliance, and onboarding is a headache, and I want to start now.
The Why Why
I’m also a big fan of AI and want to see how it can help my organization’s engineering team—improving DX, producing better code, better docs, better test coverage, better pipeline management, better time to resolution… basically better everything. I do, however, need to convince my team: people will say it might be buggy, or that they tried GitHub Chat or Edits mode and didn’t like it. I get that, and I admit my initial results weren’t great either—like any skill or tool, you have to practice. But I think it’s worth it. There’s a cliché that says: “AI won’t take your job; a person using AI better than you will take your job,” and that resonates with me real well.
INITIAL EXPERIENCE & EXPERIMENTATION
I first heard of GitHub Copilot Agent mode a few weeks ago when GitHub announced it, so I immediately downloaded VS Code Insiders for a test drive.
First Experiment
Before tackling real issues, I wanted to see how Copilot Agent mode compared to the hyped experiences in other tools. At this point, I was as novice as one can get—just me and an input box. I asked the Agent to create a simple front-end/back-end project with some tech stack requirements. It planned out a project template, asked if I wanted to implement it, then created files, directories, ran terminal commands (at which point it paused to confirm permission), and fixed any issues it ran into on the fly. Within tens of seconds, I had a fully functional boilerplate project.
I was amazed, not because it invented some grand new app, but because it did everything in such a short time. Then I pushed it further to craft a small part of my organization’s app—saving data in Neo4j, building a FastAPI server, and a React front-end to display relationships in a graph-like manner. It automatically pulled and installed Neo, built and ran Docker, tested cypher queries, created endpoints, spun up a React app—the works. I was basically vibe coding at this point, and it felt awesome.
DEEPER DIVE INTO AGENT MODE CAPABILITIES
What really intrigued me about “Agent mode” is the ability to plan, create multiple files or directories, run (and confirm) terminal commands, and then iterate when hitting issues. It’s more than just code completion or chat-like help; it’s a continuous collaboration partner that tries to solve your entire scenario rather than insert code in a single file.
At the time of writing, switching among different Copilot “modes” (ask, edit, agent) is done through the new consolidated interface. This flexibility mirrors what I see in other AI tools: a chat-like approach, an inline edit approach, and an agentic approach for higher-level tasks—just within GitHub Copilot.
REAL-WORLD USAGE & EXAMPLES
After my initial vibe coding with toys, I wanted to prove it could solve an actual problem in my dev org. So I took a few Jira tasks from our support queue, opened the relevant projects in VS Code, gave the agent the Jira ticket, and asked it to analyze the issue and provide an RCA (root cause analysis). It proceeded to analyze code paths, ask itself follow-up questions, propose possible solutions, and, in many cases, it did a solid job. Even when off the mark, it was impressively close.
Now I was convinced, so I looked for a real feature to implement with it. Sure enough, I was seeing success, and wanted others to share in that experience.
CHANGE MANAGEMENT & TEAM ADOPTION
I decided it was too good to keep to myself. I started spreading the news and showing people how to enable Agent mode. The feedback was and still is amazing. Some folks said it saved them a lot of time, or taught them new ways to do tasks. Others struggled with longer sessions or unclear prompts. Overall, though, it was a big morale and productivity boost.
Changing how people work is never simple. There will be skeptics, or those who used it once and got suboptimal results. As with any tool, you have to invest a bit of time practicing. But if you can demonstrate it solving real Jira tickets or implementing features quickly, people usually come around.
ADVANCED USAGE (PROMPT ENGINEERING, CUSTOM INSTRUCTIONS, CONTEXT MANAGEMENT)
Prompt Engineering
Prompt engineering is crucial. If the Agent doesn’t understand exactly what you need, it’ll plan based on incomplete or incorrect assumptions. That’s why I suggest explaining your goal clearly, possibly with constraints, and asking it to plan or verify things before you just say “implement.”
Explaining your aim clearly and with the proper constraints and asking to plan before execution will bring you faster to the result then going YOLO and asking the agent to immediately go implement
Custom Instructions
Think of custom instructions like onboarding a junior dev who doesn’t know your code style or internal guidelines. With GitHub Copilot, you can store instructions in your user/workspace settings, or even in a file like .github/copilot-instructions.md that copilot defaults to try and read. This way, the Agent won’t wander off into solutions that break your codebase’s conventions or adopt new libraries randomly. You can define style rules, naming conventions, comments, etc., so the AI knows how your org typically does things.
"github.copilot.chat.codeGeneration.instructions": [
{ "text": "Always add a comment: 'Generated by Copilot'." },
{ "text": "In TypeScript always use underscore for private field names." },
{ "file": "code-style.md" }
]
Managing the Context Window
Token limitations become a problem in long sessions. The Agent can lose track of the main plan or which files have changed. A strategy is to maintain a “source of truth” in a .md file, saving your high-level design decisions, requirements, or PRD/DR as you go. Then you can prompt the Agent to reference that file. It’s essentially the memory-bank approach that tools like cline adopt systematically. Even if you don’t use a separate tool, you can replicate that logic with Copilot by consistently updating a single “master plan” file any time you refine tasks.
I highly recommend reading Cline's article about their memory bank and reading the prompt!!!
Using MCPs
Model Context Protocol aka MCP is a new standard by Antropic, the people behind Claude, that allows tools to be discovered and used by any Agent that supports the protocol, and by the time of this article I think most Agents support it already.
By having a standard way of adding any tool you can basically extend the Agent to access and use remote and up to date data sources and tools which are also after the model cut off.
VS Code insiders already has great support for installing and using MCPs which IMHO is superior to the other Agent tools out there.
CONCLUSION & FUTURE OUTLOOK
Right now, GitHub Copilot Agent mode is still in beta, but from my experience it’s already a game-changer. In just a few weeks, I went from curiosity to solving real world challanges, generating PRs, and teaching my team to adopt agentic flows.
I’m excited to see how GitHub Copilot Agent mode evolves once it’s officially out of beta. I suspect more advanced features, improved context handling, and possibly an even simpler user experience for new devs. For now, I suggest you give it a shot if you’re already paying for GitHub Copilot—no additional vendor or compliance overhead, just jump in and start experimenting. Happy coding, and may the Agent be with you!
Keep me posted in the comments about your experience with Github Copilot Agent mode.
I'm reading and seeing many guides for agentic flows using windsurf_ai, cursor_ai, cline, and roo_code. But for some reason, Copilot agent mode in VS Code remains quite silent. I’ll guess it’s because it’s still in beta and requires installing VS Code Insiders. I’m here to share a bit of my experience with it and, more importantly, why I chose to experiment with it.
Let’s start first with The Why—why not just use the others? They are fine choices and might even be better; I'm not here to compare today. My organization already owns a GitHub enterprise account and has GitHub Copilot available for every developer, which makes it an easy choice. Going through the hassles of a new vendor, compliance, and onboarding is a headache, and I want to start now.
The Why Why
I’m also a big fan of AI and want to see how it can help my organization’s engineering team—improving DX, producing better code, better docs, better test coverage, better pipeline management, better time to resolution… basically better everything. I do, however, need to convince my team: people will say it might be buggy, or that they tried GitHub Chat or Edits mode and didn’t like it. I get that, and I admit my initial results weren’t great either—like any skill or tool, you have to practice. But I think it’s worth it. There’s a cliché that says: “AI won’t take your job; a person using AI better than you will take your job,” and that resonates with me real well.
INITIAL EXPERIENCE & EXPERIMENTATION
I first heard of GitHub Copilot Agent mode a few weeks ago when GitHub announced it, so I immediately downloaded VS Code Insiders for a test drive.
Sure enough, the UI changed from time to time or permissions broke in a way that required me to act, but that’s acceptable for beta. Initially, the Copilot extension had two tabs (chat mode, edits mode), and agent mode was integrated under “edits.” You had a dropdown to switch between Edit mode and Agent mode next to the send button. The latest UI merges chat and edits in one tab, letting you toggle among “ask,” “edit,” and “agent.” This fresh UX is convenient and unlocks advanced workflows, where you plan in "ask" mode and execute in either "edit" or "agent" without loosing the scope.
First Experiment
Before tackling real issues, I wanted to see how Copilot Agent mode compared to the hyped experiences in other tools. At this point, I was as novice as one can get—just me and an input box. I asked the Agent to create a simple front-end/back-end project with some tech stack requirements. It planned out a project template, asked if I wanted to implement it, then created files, directories, ran terminal commands (at which point it paused to confirm permission), and fixed any issues it ran into on the fly. Within tens of seconds, I had a fully functional boilerplate project.
I was amazed, not because it invented some grand new app, but because it did everything in such a short time. Then I pushed it further to craft a small part of my organization’s app—saving data in Neo4j, building a FastAPI server, and a React front-end to display relationships in a graph-like manner. It automatically pulled and installed Neo, built and ran Docker, tested cypher queries, created endpoints, spun up a React app—the works. I was basically vibe coding at this point, and it felt awesome.
DEEPER DIVE INTO AGENT MODE CAPABILITIES
What really intrigued me about “Agent mode” is the ability to plan, create multiple files or directories, run (and confirm) terminal commands, and then iterate when hitting issues. It’s more than just code completion or chat-like help; it’s a continuous collaboration partner that tries to solve your entire scenario rather than insert code in a single file.
At the time of writing, switching among different Copilot “modes” (ask, edit, agent) is done through the new consolidated interface. This flexibility mirrors what I see in other AI tools: a chat-like approach, an inline edit approach, and an agentic approach for higher-level tasks—just within GitHub Copilot.
REAL-WORLD USAGE & EXAMPLES
After my initial vibe coding with toys, I wanted to prove it could solve an actual problem in my dev org. So I took a few Jira tasks from our support queue, opened the relevant projects in VS Code, gave the agent the Jira ticket, and asked it to analyze the issue and provide an RCA (root cause analysis). It proceeded to analyze code paths, ask itself follow-up questions, propose possible solutions, and, in many cases, it did a solid job. Even when off the mark, it was impressively close.
Now I was convinced, so I looked for a real feature to implement with it. Sure enough, I was seeing success, and wanted others to share in that experience.
CHANGE MANAGEMENT & TEAM ADOPTION
I decided it was too good to keep to myself. I started spreading the news and showing people how to enable Agent mode. The feedback was and still is amazing. Some folks said it saved them a lot of time, or taught them new ways to do tasks. Others struggled with longer sessions or unclear prompts. Overall, though, it was a big morale and productivity boost.
Changing how people work is never simple. There will be skeptics, or those who used it once and got suboptimal results. As with any tool, you have to invest a bit of time practicing. But if you can demonstrate it solving real Jira tickets or implementing features quickly, people usually come around.
ADVANCED USAGE (PROMPT ENGINEERING, CUSTOM INSTRUCTIONS, CONTEXT MANAGEMENT)
Prompt Engineering
Prompt engineering is crucial. If the Agent doesn’t understand exactly what you need, it’ll plan based on incomplete or incorrect assumptions. That’s why I suggest explaining your goal clearly, possibly with constraints, and asking it to plan or verify things before you just say “implement.”
Explaining your aim clearly and with the proper constraints and asking to plan before execution will bring you faster to the result then going YOLO and asking the agent to immediately go implement
Custom Instructions
Think of custom instructions like onboarding a junior dev who doesn’t know your code style or internal guidelines. With GitHub Copilot, you can store instructions in your user/workspace settings, or even in a file like .github/copilot-instructions.md that copilot defaults to try and read. This way, the Agent won’t wander off into solutions that break your codebase’s conventions or adopt new libraries randomly. You can define style rules, naming conventions, comments, etc., so the AI knows how your org typically does things.
"github.copilot.chat.codeGeneration.instructions": [
{ "text": "Always add a comment: 'Generated by Copilot'." },
{ "text": "In TypeScript always use underscore for private field names." },
{ "file": "code-style.md" }
]
Managing the Context Window
Token limitations become a problem in long sessions. The Agent can lose track of the main plan or which files have changed. A strategy is to maintain a “source of truth” in a .md file, saving your high-level design decisions, requirements, or PRD/DR as you go. Then you can prompt the Agent to reference that file. It’s essentially the memory-bank approach that tools like cline adopt systematically. Even if you don’t use a separate tool, you can replicate that logic with Copilot by consistently updating a single “master plan” file any time you refine tasks.
I highly recommend reading Cline's article about their memory bank and reading the prompt!!!
Using MCPs
Model Context Protocol aka MCP is a new standard by Antropic, the people behind Claude, that allows tools to be discovered and used by any Agent that supports the protocol, and by the time of this article I think most Agents support it already.
By having a standard way of adding any tool you can basically extend the Agent to access and use remote and up to date data sources and tools which are also after the model cut off.
VS Code insiders already has great support for installing and using MCPs which IMHO is superior to the other Agent tools out there.
CONCLUSION & FUTURE OUTLOOK
Right now, GitHub Copilot Agent mode is still in beta, but from my experience it’s already a game-changer. In just a few weeks, I went from curiosity to solving real world challanges, generating PRs, and teaching my team to adopt agentic flows.
- Time savings: Quick project setups, automated troubleshooting, and immediate feedback loops.
- Better adoption: Once my colleagues saw it solving real-world issues, they embraced it.
- Ongoing learning: Prompt engineering, custom instructions, and context management take practice, but each improvement yields better outcomes.
I’m excited to see how GitHub Copilot Agent mode evolves once it’s officially out of beta. I suspect more advanced features, improved context handling, and possibly an even simpler user experience for new devs. For now, I suggest you give it a shot if you’re already paying for GitHub Copilot—no additional vendor or compliance overhead, just jump in and start experimenting. Happy coding, and may the Agent be with you!
Keep me posted in the comments about your experience with Github Copilot Agent mode.