Tarun Kumar’s Post

🚀 New Product Update: GitHub Copilot CLI – Custom Agents & Delegation 📄 https://lnkd.in/gqw5bzaR ⸻ 🤖 What it’s about: • The update introduces custom agents within GitHub Copilot CLI. You can define agent personas that reflect your team’s workflows, code style, tools and prompts. • Custom agent configurations can live in .github/agents at repo/org level or in ~/.copilot/agents locally. • You can invoke an agent with /agent slash-command. The CLI also exposes your custom agents as tools that the Copilot model may pick in an “agentic loop” when needed. • You can delegate tasks from the CLI to the Copilot Coding Agent using /delegate TASK-DESCRIPTION. The CLI commits unstaged changes, creates a new branch, the coding agent proceeds asynchronously, opens a draft PR, and notifies you with link in terminal. • Performance improvements: streaming token-by-token output, parallel tool-calls support, reduced memory footprint, screen flickering fixes. ⸻ 💡 Why it matters: • Offers tailored automation: teams can build agents that encode their standards and tools, increasing consistency and speed. • Enables hands-off workflows: you delegate tasks and let the Copilot Coding Agent take them from branch creation to draft PR, freeing up developer time. • Improves responsiveness and scalability: streaming output and parallel tool execution reduce waiting time and bottlenecks. • Integrates deeper into your dev ecosystem: local config for agents means less friction, more control. • Makes the whole CLI tool from passive assistance to proactive autonomous agent platform. ⸻ 🧠 Takeaway: If you are working with GitHub and Copilot in a development team, this update gives you two major levers: customising agent behaviour to match your workflows, and delegating non-interactive tasks to autonomous agents. Update your CLI (npm install -g @github/copilot@latest) and explore what agents you can build.

Like many developers, I use GitHub Copilot through the VS Code/Visual Studio extensions. Copilot helps in everyday development, assisting with everything from simple syntax questions to more complete implementation details. Like any AI tool, it requires a responsible approach: without solid understanding and code review, it's easy to introduce technical debt. To get the most out of Copilot, I've found it works best when: 🔍 Patterns are clear: It's most effective within an established codebase that has consistent patterns to follow. This allows it to handle complex tasks like mapping API calls to database code. 🎯 Prompts are specific: The best results come from well-structured prompts that reference files and code blocks as examples. ⚙️ Task Management: It's great for boilerplate and repetitive work (like generating unit tests). While it's capable of larger implementations, the results are less predictable, requiring closer review. I'm trying out the GitHub Copilot CLI Public Preview (https://lnkd.in/g-K3QzhP). Here are some observations that I thought were notable: ⚡ Faster Feel: The command-line interface feels more immediate and responsive than the integrated extensions. 🪄 Automated Workflow: The ability to delegate tasks to a Copilot coding agent via the /delegate TASK-DESCRIPTION command is a feature that I've been experimenting with. The agent can then automatically create a draft pull request, make changes based on your prompt, and then request a PR review. Seeing those automated notifications feels like I'm working with someone. 💡 Manage Expectations: Because the CLI is disconnected from the IDE, it feels like it should handle more—but it’s still using the same underlying models. My recommendation is to keep tasks simple and always use /delegate to keep the work isolated. Both the IDE extensions and the CLI show the potential of AI-assisted development—as long as we remain intentional and maintain ownership of the final code. Curious how others are experimenting with the GitHub Copilot CLI or combining it with the IDE extension. What's been your experience? #GitHub #Copilot #AI #DeveloperTools #Programming

🚀 From Requirements to Draft PR — Using GitHub Models in GitHub Actions I’ve been experimenting with how far we can push autonomy in the software delivery chain. The result? A GitHub Actions workflow that turns plain-English requirements documents dropped into an inbox/ folder into: ✅ Structured GitHub Issues 🤖 Auto-assigned Copilot agents 💡 Draft Pull Requests ready for review All powered by GitHub Models (GPT-4o) and a bit of smart YAML. It’s an elegant loop — from requirements to working code stub with zero manual triage. If you’re exploring how to integrate Generative AI directly into your engineering workflow, this piece breaks down the logic, system prompts, and guardrails I’ve found most effective. 📖 Read the full breakdown here: 👉 https://lnkd.in/dCYueRuy

This article breaks down the impactful role of Copilot in enhancing the GitHub platform. I found it interesting that Copilot not only streamlines coding processes but also empowers developers to be more creative and efficient. What stood out to me was how AI can evolve traditional workflows in significant ways. How do you see AI tools like Copilot changing the landscape of software development in the future?

🚀 GitHub is quietly winning the Agentic Coding race Over the last few weeks, I’ve been switching between tools — Cursor, Windsurf, Rovo Agent (Atlassian), OpenAI's Codex — testing how far AI can actually go in assisting real-world development. And honestly… GitHub Copilot feels way ahead. Not because it has the flashiest UI or the latest models — but because it’s deeply integrated into how developers actually work. Here’s what I mean 👇 🧠 It’s everywhere — A CLI agent for command-line workflows A built-in agent inside VS Code Insiders A Mission Control dashboard on the web Just today, I had: 💻 an agent helping with a feature implementation, 🧹 another cleaning up dead code using Grok Code Fast, ⚙️ one setting up a GitHub Action for my frontend, 🧩 and I was still exploring backend hosting options — which could’ve been another agent if I’d finalized the stack 😅 All completely unrelated tasks, running in parallel, inside one ecosystem. 💰 Another underrated point: almost all the models are billed at 1× request pricing — not inflated. And during GitHub Universe, their global developer conference, they announced plans to integrate third-party AI agents — from Anthropic, OpenAI, and more — directly into the GitHub ecosystem under the same Copilot plan Oh, and if you’re a repo maintainer, teacher, or student, you get Copilot Pro free. For me, that makes GitHub Copilot the most developer-first and value-packed AI tool out there right now. ⚙️ 💬 Have you started using Copilot or any other AI agent in your workflow yet? Would love to know how you’re pairing these tools with your stack 👇

Exciting update from GitHub Copilot: you can now configure agent-specific instructions via .instructions.md files, including a new excludeAgent property to control when certain instructions apply (for code-review vs coding agents). This gives teams more granular control over how Copilot behaves across workflows. #GitHubCopilot #DevTools #SoftwareDelivery #developerproductivity #developers #AI #GitHub #developercommunity https://lnkd.in/gA2j3eir

This article provides a comprehensive guide on using GitHub Copilot to enhance your coding efficiency, covering everything from building to shipping code. I found it interesting that the tutorial not only showcases practical prompts but also highlights how Copilot can streamline the development process. How do you see AI tools like Copilot changing the future of coding in your projects?

Boost Your Coding Productivity with GitHub Copilot + AI If you haven’t tried GitHub Copilot yet — you’re missing out on one of the easiest ways to bring AI power right inside your code editor! Here’s my quick take after using it 👇 💻 Ease of Use • Simply install the GitHub Copilot extension in VS Code, JetBrains, or Neovim — and it’s ready to assist you instantly. • It understands your context, predicts your next line, and even writes entire functions or tests. • Works across multiple languages: Python, C#, JavaScript, TypeScript, SQL, and more! ⚡ Productivity Boost (Not a Myth!) Some say Copilot “slows you down” or “writes wrong code” — but the truth is: ✅ It helps you code faster, especially for boilerplate or repetitive logic. ✅ It improves focus by reducing the need to Google small syntax issues. ✅ It can even suggest better patterns based on best practices. It’s not here to replace developers — it’s here to supercharge them. 💪 💰 Plans & Limits • Free for verified students and open-source maintainers. • Individual plan: ~$10/month • Business plan: ~$19/user/month • Enterprise plan: for large teams with policy controls and insights. Each plan includes AI-powered code suggestions, chat-based help (Copilot Chat), and integrations with GitHub Actions and Pull Requests. 🌱 The Real Benefit Copilot doesn’t just write code — it helps you think better as a developer. You learn faster, debug smarter, and ship cleaner solutions. 🧠 Have you tried GitHub Copilot yet? Share your experience below 👇 — what’s your favorite AI use case in daily coding? #GitHubCopilot #AIForDevelopers #Productivity #CodingTools #ArtificialIntelligence #SoftwareEngineering #GitHub #VSCode

GitHub Copilot CLI: Use custom agents and delegate to Copilot coding agent Two Key Points 1. In addition to defining agents under .github/agents in a repository or in the {org}/.github repository, GitHub Copilot CLI will recognize custom agent configurations in ~/.copilot/agents. You can explicitly invoke an agent interactively with the /agent slash command. Your custom agents are also made available as tools to Copilot, and the model will start a new agentic loop using a relevant custom agent when necessary. See More: https://lnkd.in/eu6FcxJR 2. Copilot coding agent is our asynchronous, autonomous background agent. Running the /delegate TASK-DESCRIPTION slash command from GitHub Copilot CLI will commit any unstaged changes to a new branch. After that, Copilot coding agent will open a draft pull request, make changes in the background, and then request a review from you. Copilot will provide a link to the pull request and its coding agent’s session in your terminal once this process begins. See More: https://lnkd.in/eKN_F5xp Complete Blog: https://lnkd.in/etD8iRjV