Syphax GUEMGHAR’s Post

I recently asked a coworker for advice on using GitHub Copilot more strategically. Their answer clarified a distinction that can be confusing early on: the difference between Copilot’s coding agent capabilities and Agent Mode. Copilot Coding Agent refers to an autonomous, task-oriented way Copilot can do coding work for you, like implementing a change, refactoring code, or updating tests based on a request. In practice, you do not always “pick Coding Agent” as a separate option in the UI. Instead, the experience is typically presented as Copilot taking on an assigned task and working through it, often using repository context and producing a concrete set of changes for you to review. A key point is that this style of Copilot interaction is scoped and goal-driven. You give it a defined objective, and it focuses on completing that objective with minimal extra orchestration. Agent Mode is the broader workflow behavior. When Agent Mode is enabled, Copilot is set up to operate more like an orchestrator: it can plan multi-step work, keep track of progress across steps, and coordinate tools or “skills” to complete a larger workflow. Rather than just making one change, it is oriented around executing a sequence, like: inspect code, propose an approach, modify files, update tests, validate assumptions, and iterate. So the practical takeaway my colleague emphasized was: - Use coding agent style tasks when you want a scoped change completed quickly and you want to review a focused set of edits. - Use Agent Mode when you want Copilot to manage a multi-step workflow that would otherwise require you to repeatedly prompt, context switch, and coordinate the sequence manually. For anyone using GitHub Copilot, what’s your rule of thumb for when you keep things scoped versus when you lean on Agent Mode for multi-step workflows? https://msft.it/6047QOo4J #GitHubCopilot #AgentMode #DeveloperWorkflow #Automation #Productivity (Interesting note: Agent Mode is less about “a different helper” and more about Copilot taking responsibility for planning and coordinating steps. Even then, you still stay in control by reviewing changes and deciding what gets merged.)

I recently asked a coworker for advice on using GitHub Copilot more strategically. Their answer clarified a distinction that can be confusing early on: the difference between Copilot’s coding agent capabilities and Agent Mode. Copilot Coding Agent refers to an autonomous, task-oriented way Copilot can do coding work for you, like implementing a change, refactoring code, or updating tests based on a request. In practice, you do not always “pick Coding Agent” as a separate option in the UI. Instead, the experience is typically presented as Copilot taking on an assigned task and working through it, often using repository context and producing a concrete set of changes for you to review. A key point is that this style of Copilot interaction is scoped and goal-driven. You give it a defined objective, and it focuses on completing that objective with minimal extra orchestration. Agent Mode is the broader workflow behavior. When Agent Mode is enabled, Copilot is set up to operate more like an orchestrator: it can plan multi-step work, keep track of progress across steps, and coordinate tools or “skills” to complete a larger workflow. Rather than just making one change, it is oriented around executing a sequence, like: inspect code, propose an approach, modify files, update tests, validate assumptions, and iterate. So the practical takeaway my colleague emphasized was: - Use coding agent style tasks when you want a scoped change completed quickly and you want to review a focused set of edits. - Use Agent Mode when you want Copilot to manage a multi-step workflow that would otherwise require you to repeatedly prompt, context switch, and coordinate the sequence manually. For anyone using GitHub Copilot, what’s your rule of thumb for when you keep things scoped versus when you lean on Agent Mode for multi-step workflows? https://msft.it/6049QKcMV #GitHubCopilot #AgentMode #DeveloperWorkflow #Automation #Productivity (Interesting note: Agent Mode is less about “a different helper” and more about Copilot taking responsibility for planning and coordinating steps. Even then, you still stay in control by reviewing changes and deciding what gets merged.)

I recently asked a coworker for advice on using GitHub Copilot more strategically. Their answer clarified a distinction that can be confusing early on: the difference between Copilot’s coding agent capabilities and Agent Mode. Copilot Coding Agent refers to an autonomous, task-oriented way Copilot can do coding work for you, like implementing a change, refactoring code, or updating tests based on a request. In practice, you do not always “pick Coding Agent” as a separate option in the UI. Instead, the experience is typically presented as Copilot taking on an assigned task and working through it, often using repository context and producing a concrete set of changes for you to review. A key point is that this style of Copilot interaction is scoped and goal-driven. You give it a defined objective, and it focuses on completing that objective with minimal extra orchestration. Agent Mode is the broader workflow behavior. When Agent Mode is enabled, Copilot is set up to operate more like an orchestrator: it can plan multi-step work, keep track of progress across steps, and coordinate tools or “skills” to complete a larger workflow. Rather than just making one change, it is oriented around executing a sequence, like: inspect code, propose an approach, modify files, update tests, validate assumptions, and iterate. So the practical takeaway my colleague emphasized was: - Use coding agent style tasks when you want a scoped change completed quickly and you want to review a focused set of edits. - Use Agent Mode when you want Copilot to manage a multi-step workflow that would otherwise require you to repeatedly prompt, context switch, and coordinate the sequence manually. For anyone using GitHub Copilot, what’s your rule of thumb for when you keep things scoped versus when you lean on Agent Mode for multi-step workflows? https://msft.it/6047QRkOF #GitHubCopilot #AgentMode #DeveloperWorkflow #Automation #Productivity (Interesting note: Agent Mode is less about “a different helper” and more about Copilot taking responsibility for planning and coordinating steps. Even then, you still stay in control by reviewing changes and deciding what gets merged.)

I recently asked a coworker for advice on using GitHub Copilot more strategically. Their answer clarified a distinction that can be confusing early on: the difference between Copilot’s coding agent capabilities and Agent Mode. Copilot Coding Agent refers to an autonomous, task-oriented way Copilot can do coding work for you, like implementing a change, refactoring code, or updating tests based on a request. In practice, you do not always “pick Coding Agent” as a separate option in the UI. Instead, the experience is typically presented as Copilot taking on an assigned task and working through it, often using repository context and producing a concrete set of changes for you to review. A key point is that this style of Copilot interaction is scoped and goal-driven. You give it a defined objective, and it focuses on completing that objective with minimal extra orchestration. Agent Mode is the broader workflow behavior. When Agent Mode is enabled, Copilot is set up to operate more like an orchestrator: it can plan multi-step work, keep track of progress across steps, and coordinate tools or “skills” to complete a larger workflow. Rather than just making one change, it is oriented around executing a sequence, like: inspect code, propose an approach, modify files, update tests, validate assumptions, and iterate. So the practical takeaway my colleague emphasized was: - Use coding agent style tasks when you want a scoped change completed quickly and you want to review a focused set of edits. - Use Agent Mode when you want Copilot to manage a multi-step workflow that would otherwise require you to repeatedly prompt, context switch, and coordinate the sequence manually. For anyone using GitHub Copilot, what’s your rule of thumb for when you keep things scoped versus when you lean on Agent Mode for multi-step workflows? https://msft.it/6048QuhKn #GitHubCopilot #AgentMode #DeveloperWorkflow #Automation #Productivity #MicrosoftEmployee (Interesting note: Agent Mode is less about “a different helper” and more about Copilot taking responsibility for planning and coordinating steps. Even then, you still stay in control by reviewing changes and deciding what gets merged.)

My Colleague Explains the Difference between Coding Agent and Agent Mode in GitHub Copilot I recently asked a coworker for advice on using GitHub Copilot more strategically. Their answer broke down a key distinction that can be confusing if you are just getting started: the difference between the Copilot Coding Agent and Agent Mode. The Coding Agent is designed for targeted tasks - think writing, refactoring, or reviewing code in response to a specific prompt. You create a request (like “fix this function” or “add input validation”) and the Coding Agent carries out that assignment. It works inside a secure sandbox so you get reliable automation without risking unintended changes. Agent Mode is a bigger concept. Turning on Agent Mode is like switching Copilot from a helpful “sidekick” into an orchestrator that can manage complex, multi-step workflows. In Agent Mode, Copilot can coordinate several Coding Agents or Skills, handle context switching, and chain together tasks that span different tools, files, or even repositories. It is about workflow automation at scale, not just single fixes. The main thing my colleague emphasized: use the Coding Agent for targeted, one-off jobs that need accuracy and speed. Use Agent Mode when you want Copilot to think like a conductor, moving multiple agents through a process from start to finish with no manual steps required between each one. For anyone using GitHub Copilot, how do you decide when to stick with the Coding Agent and when to scale up with Agent Mode? Any lessons learned about building reliable automation workflows? https://msft.it/6048QKAgr #GitHubCopilot #CodingAgent #AgentMode #Automation #DeveloperWorkflow #Productivity (Interesting note: Agent Mode lets Copilot coordinate a whole team of digital helpers, but you still keep precision and control by assigning the right jobs to the right agent at every step.)

Unlocking the full potential of GitHub Copilot isn't about replacing developers—it's about enhancing their craft. In software engineering, Copilot stands as a collaborator, not a replacement. It supports developers in system design and refactoring, offering an architecture-aware workflow that respects and amplifies human judgment. Why does this matter? - 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐯𝐞 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬: Copilot seamlessly integrates into team dynamics. - 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐚𝐥 𝐀𝐰𝐚𝐫𝐞𝐧𝐞𝐬𝐬: It aids in understanding complex service-layer architectures. - 𝐒𝐲𝐬𝐭𝐞𝐦𝐚𝐭𝐢𝐜 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡𝐞𝐬: Streamlines tasks like feature additions and migrations. Imagine decomposing a system or implementing new features with Copilot's support. It’s about maintaining control while leveraging AI to elevate your work. So, what's next? Enable agent mode. Dive into GitHub Skills exercises. Embrace Copilot as a tool for decision-making, not as a decision-maker. The future of coding is collaborative. Let's lead with both AI and human ingenuity. How do you see AI reshaping your development process? Let's discuss!#github #agentic

One of GitHub Copilot’s best features is access to the best closed models across major providers. That flexibility means you’re not limited to a single generalist. You can choose the right model for the job and, if you’re deliberate, build a fleet of specialised agents. One of GitHub’s worst features is its UI. As I pushed Copilot CLI harder, I became increasingly frustrated by the lack of visibility and control. I couldn’t see how tasks were being decomposed, how delegation was happening between agents, which model was handling which responsibility, or what the system was actually doing on my behalf. I’ve always believed the best way to learn is to build. So instead of complaining, I built my own terminal UI on top of Copilot. Under the hood, it uses @github/copilot-sdk to talk to Copilot CLI. I didn’t replace the plumbing. I wrapped it. Agents are registered as SDK custom agents, and delegation flows through the SDK’s task tooling. What I changed is the interface and the orchestration around it. My TUI exposes the execution plan, active agents, subagents, escalation paths, model selection per role, files modified, and real-time progress. On top of that, I structured a set of agents to operate like a small engineering team: Intake clarifies requirements, a Tech Lead decomposes and coordinates, architects and investigators design, engineers implement, specialists contribute domain depth, and a Reviewer acts as a quality gate. All orchestrated within a single request. It feels less like pair programming and more like leading a team. It didn't take long to get something functional with Copilot CLI before I was able to start dogfooding it from the TUI itself. It's still a little rough around the edges, but my engineering team can fix that. I’m not releasing this. It’s a hobby project. If you want one, you’ll have to build your own. That’s intentional. If you’re trying to understand how agentic coding works and how it changes the role of senior engineers, don’t just consume content about it. Design the roles. Choose the models. Define delegation and escalation paths. Debug coordination failures. You’ll learn more by building your own system than by watching anyone explain it.

Over the last 6-8 months, I've been using GitHub Copilot regularly and have narrowed it down to two ways I rely on it. Not for autocomplete. But as a thinking and execution partner. 1️⃣ Understanding existing code When I need to understand unfamiliar or complex code, I use Ask mode. What I usually do: - Share the code snippet and any references I already know are relevant. - Before asking for an explanation, I ask Copilot what additional references or context it needs to explain the code better. - Once all required context is attached, I ask it to explain the code step by step. This approach reduces guesswork and leads to much more accurate explanations, especially in large codebases. 2️⃣ From requirements to code This is where Copilot helps me the most. Step 1: Clarifying the problem I paste the requirements into Ask mode and start a discussion: - What approach should we take? - What assumptions are we making? - What are the edge cases? There's a lot of back-and-forth here. Sometimes I explain why something won't work. Sometimes Copilot points out gaps in my reasoning. Step 2: Confirming understanding Before any code is written, I ask Copilot to: - Re-list my requirements (to confirm it understood them correctly) - Explain the approach it plans to take - List the files / classes / functions that will be created or modified - Break everything down into numbered tasks Only after this do I move forward. Step 3: Writing the code I then switch to Agent mode and use premium models (Claude Sonnet 4 earlier, Sonnet 4.5 lately) to implement the tasks. Step 4: Review and iteration I first read and validate the generated code myself. Based on that validation, I decide how to proceed: - If the change is small, I make it manually - If it's a logic issue, I point it out and ask Copilot to fix it - If it's a bigger issue, I switch back to Ask mode to discuss the correction or approach, and then either apply the fix myself or switch back to Agent mode to update the code This loop continues until I'm satisfied. What this taught me Copilot works best when you stay in control and treat it like a collaborator, not a replacement. #GitHubCopilot #VSCode #DeveloperProductivity

My team-mate has been digging into GitHub Copilot CLI’s new ACP server support, and explains it is a genuinely interesting step toward making coding agents easier to integrate anywhere. ACP stands for Agent Client Protocol, a standard way for a client (an editor, IDE, terminal UI, or even a pipeline runner) to talk to a coding agent (like Copilot CLI). Instead of every tool inventing its own bespoke integration, ACP provides a shared contract for how requests, responses, and agent actions flow. A quick note: ACP support in GitHub Copilot CLI is currently in public preview, so details may evolve. Why this matters: IDE integrations: Bring Copilot style agent workflows into editors or internal dev environments that do not have first class Copilot plugins. CI/CD pipelines: Orchestrate agentic coding tasks in automated workflows, like generating patches, refactoring, or assisting with test updates as part of builds. Custom frontends: Build purpose specific interfaces for your team’s workflow, like a lightweight internal “coding assistant console.” Multi-agent systems: Coordinate Copilot alongside other AI agents with a standard protocol, rather than stitching together fragile adapters. The bigger takeaway for me is that standard protocols are what turn “cool demos” into ecosystems. If you have ever built a custom IDE integration or tried to automate code changes in a pipeline, you know how much time disappears into glue code. ACP aims to reduce that friction. If you are experimenting with agentic workflows, ACP server support in Copilot CLI is worth a look, especially if you want Copilot assistance outside the usual editor plugin path. #GitHubCopilot #DeveloperTools #AIEngineering #DevEx #Automation #CICD #IDEs

My team-mate has been digging into GitHub Copilot CLI’s new ACP server support, and explains it is a genuinely interesting step toward making coding agents easier to integrate anywhere. ACP stands for Agent Client Protocol, a standard way for a client (an editor, IDE, terminal UI, or even a pipeline runner) to talk to a coding agent (like Copilot CLI). Instead of every tool inventing its own bespoke integration, ACP provides a shared contract for how requests, responses, and agent actions flow. A quick note: ACP support in GitHub Copilot CLI is currently in public preview, so details may evolve. Why this matters: IDE integrations: Bring Copilot style agent workflows into editors or internal dev environments that do not have first class Copilot plugins. CI/CD pipelines: Orchestrate agentic coding tasks in automated workflows, like generating patches, refactoring, or assisting with test updates as part of builds. Custom frontends: Build purpose specific interfaces for your team’s workflow, like a lightweight internal “coding assistant console.” Multi-agent systems: Coordinate Copilot alongside other AI agents with a standard protocol, rather than stitching together fragile adapters. The bigger takeaway for me is that standard protocols are what turn “cool demos” into ecosystems. If you have ever built a custom IDE integration or tried to automate code changes in a pipeline, you know how much time disappears into glue code. ACP aims to reduce that friction. If you are experimenting with agentic workflows, ACP server support in Copilot CLI is worth a look, especially if you want Copilot assistance outside the usual editor plugin path. #GitHubCopilot #DeveloperTools #AIEngineering #DevEx #Automation #CICD #IDEs