Orchestrating AI Coding Agents for Efficient Workflow

🏭 I built an agentic dark factory for AI building. Imagine telling AI agents what you want to build, turning the lights off, and the agentic factory gets to work. That future isn’t hypothetical anymore. Dark Factory is an experiment in spec‑to‑software automation, using the GitHub Copilot CLI. (repo and website link in comments) At the core: Six specialist agents, each with its own prompt, model assignment, and governance rules. They’re stateless and only see what the Factory Manager explicitly passes forward. Here’s how it works: 1. You give Dark Factory a short, natural‑language goal in the GitHub Copilot CLI. 2. The system spins up an isolated, disposable git worktree so every build is clean and contained. 3. Specialist agents (Product, Architecture, Build, QA) move through a checkpoint‑gated pipeline. 4. Each phase must pass before the next begins. 5. A sealed acceptance test suite is generated from the spec before any code is written. 6. The building agents never see these tests, which prevents “teaching to the test.” 7. The output is a review‑ready pull request. This project is about exploring what AI systems can do when agnet orchestration, verification, and governance are designed intentionally. Have an idea you’re curious to see tested? Post it below. I’ll choose one, run it through the Dark Factory, and share the outcome. #AI #GitHub #Copilot #CopilotCLI

GitHub Copilot’s organization custom instructions feature is now generally available. This allows Copilot Business and Enterprise admins to establish default instructions for their teams, streamlining the coding experience and ensuring consistent AI assistance across the organization. A valuable tool for enhancing developer productivity and uniformity. #GitHub #Copilot #SoftwareDevelopment #AI #Productivity 🚀🤖 ⬇️ https://lnkd.in/dNHnm9qp

🪄 AI agents orchestration is becoming a must-have skill for software engineers We’re moving beyond using a single AI assistant… into coordinating multiple AI agents working together inside your codebase. This shift changes how we build software: - From prompting → to orchestrating - From single responses → to collaborative workflows - From tools → to systems There’s a lot of innovation happening in this space right now, especially in developer tooling. Here are two solid examples worth exploring: 👉 Squad https://msft.it/6045vDZAc Squad introduces the idea of an AI team living inside your repo. You describe a task, and Squad: - Breaks it down - Delegates work across agents - Runs tasks in parallel - Keeps shared memory and decision logs All of this happens within your repository context. In short: it turns Copilot into a coordinated development team, not just a coding assistant. 👉 Azure az-prototype https://msft.it/6046vDZAY An Azure CLI extension that empowers customers to rapidly create functional Azure prototypes using AI-driven agent teams. It supports three AI providers - GitHub Copilot, GitHub Models, and Azure OpenAI - for intelligent code and infrastructure generation. Curious, are you already experimenting with multi-agent setups? 👇 #GitHubCopilot #AIAgents #AINativeDevInfra #AZPrototype #AgentSquad #GenerativeAI #msftadvocate

The "Agent Gap" and the Velocity Paradox Microsoft just hit 150M Copilot users, and GitHub Workspace is moving from AI-assisted to AI-autonomous coding. But we are facing a "Velocity Paradox." Opsera’s research shows that while AI-assisted workflows are 58% faster at producing code, those PRs sit 4.6x longer waiting for human review. If you increase generation without increasing orchestration, you aren't accelerating—you're just creating a bigger backlog of unvetted code. True leadership in the AI-SDLC isn't about how fast your agents can type. It's about how smart your orchestration layer is. At Opsera, we are bridging this "Agent Gap" with autonomous remediation agents that don't just report problems—they fix them. Stop guessing. Start orchestrating. #AI #DevOps #GitHub #Opsera #AgenticAI #Microsoft

🪄 AI agents orchestration is becoming a must-have skill for software engineers We’re moving beyond using a single AI assistant… into coordinating multiple AI agents working together inside your codebase. This shift changes how we build software: - From prompting → to orchestrating - From single responses → to collaborative workflows - From tools → to systems There’s a lot of innovation happening in this space right now, especially in developer tooling. Here are two solid examples worth exploring: 👉 Squad https://msft.it/6044QI0T2 Squad introduces the idea of an AI team living inside your repo. You describe a task, and Squad: - Breaks it down - Delegates work across agents - Runs tasks in parallel - Keeps shared memory and decision logs All of this happens within your repository context. In short: it turns Copilot into a coordinated development team, not just a coding assistant. 👉 Azure az-prototype https://msft.it/6045QI0TN An Azure CLI extension that empowers customers to rapidly create functional Azure prototypes using AI-driven agent teams. It supports three AI providers - GitHub Copilot, GitHub Models, and Azure OpenAI - for intelligent code and infrastructure generation. Curious, are you already experimenting with multi-agent setups? 👇 #GitHubCopilot #AIAgents #AINativeDevInfra #AZPrototype #AgentSquad #GenerativeAI #msftadvocate

/𝗳𝗹𝗲𝗲𝘁 𝗶𝘀 𝗵𝗲𝗿𝗲: 𝗧𝘂𝗿𝗻𝗶𝗻𝗴 𝗚𝗶𝘁𝗛𝘂𝗯 𝗖𝗼𝗽𝗶𝗹𝗼𝘁 𝗖𝗟𝗜 𝗶𝗻𝘁𝗼 𝗮 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝗽𝗼𝘄𝗲𝗿𝗵𝗼𝘂𝘀𝗲 🤖💪 The new /𝗳𝗹𝗲𝗲𝘁 command in GitHub Copilot CLI is a game-changer for multi-tasking. Instead of tackling one file at a time, /𝗳𝗹𝗲𝗲𝘁 acts as a behind-the-scenes orchestrator that plans, decomposes and executes tasks in parallel across your entire codebase. 𝗪𝗵𝘆 𝗰𝗮𝗿𝗲? - 𝘗𝘢𝘳𝘢𝘭𝘭𝘦𝘭 𝘌𝘹𝘦𝘤𝘶𝘵𝘪𝘰𝘯: It dispatches multiple sub-agents to work on different files simultaneously. - 𝘚𝘮𝘢𝘳𝘵 𝘖𝘳𝘤𝘩𝘦𝘴𝘵𝘳𝘢𝘵𝘪𝘰𝘯: It automatically identifies which tasks are independent and which have dependencies (though, direct hints help). - 𝘉𝘳𝘰𝘢𝘥 𝘚𝘤𝘰𝘱𝘦: Perfect for refactoring an entire module, updating tests and syncing documentation all in one go. 𝗛𝗼𝘄 𝗶𝘁 𝗹𝗼𝗼𝗸𝘀 𝗶𝗻 𝗮𝗰𝘁𝗶𝗼𝗻: $ 𝘤𝘰𝘱𝘪𝘭𝘰𝘵 -𝘱 "/𝘧𝘭𝘦𝘦𝘵 𝘔𝘰𝘥𝘪𝘧𝘺 𝘜𝘴𝘦𝘳 𝘴𝘤𝘩𝘦𝘮𝘢 𝘪𝘯 /𝘴𝘩𝘢𝘳𝘦𝘥 𝘵𝘰 𝘪𝘯𝘤𝘭𝘶𝘥𝘦 '𝘮𝘪𝘥𝘥𝘭𝘦𝘕𝘢𝘮𝘦' and 𝘶𝘱𝘥𝘢𝘵𝘦 𝘉𝘢𝘤𝘬𝘦𝘯𝘥, 𝘍𝘳𝘰𝘯𝘵𝘦𝘯𝘥 𝘢𝘯𝘥 𝘛𝘦𝘴𝘵𝘴" 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: 1. 𝘉𝘦 𝘚𝘱𝘦𝘤𝘪𝘧𝘪𝘤: The better you define the deliverables (e.g., specific file paths), the better the orchestrator can parallelize the work. 2. 𝘚𝘦𝘵 𝘉𝘰𝘶𝘯𝘥𝘢𝘳𝘪𝘦𝘴: Tell the fleet exactly which directories to touch - and which to leave alone. 3. 𝘊𝘶𝘴𝘵𝘰𝘮 𝘈𝘨𝘦𝘯𝘵𝘴: You can even use specialized agents (like a technical writer for docs) within the same fleet command. It’s like moving from being a solo developer to a 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗟𝗲𝗮𝗱 in your own terminal. Have you tried parallelizing your AI workflow yet? Tell me in the comments! 👇 #AI #SoftwareEngineering #Productivity

The "Golden Age" of unlimited agentic workflows in GitHub Copilot is coming to an end. If your team has been leveraging Copilot’s "Premium Requests" to run complex, long-running agentic workflows, the shift to usage-based billing on June 1, 2026, is a major wake-up call. Under the current model, a 1-hour autonomous agent session might cost just one "request." In the new model, every token—input, output, and iteration—hits your credit pool. Why this matters for AI Leaders: 🔹 No more "Compute Arbitrage": Previously, complex tasks were subsidized by the flat rate. Now, the more "agentic" and iterative a workflow is, the faster it will burn through your $19/month pooled credits. 🔹 The Cost of Context: Long-running agents often have massive context windows. Under a credit-based system, high-context tasks become the most expensive items on your bill. 🔹 Optimization is Mandatory: Success no longer depends just on what the AI can do, but on how efficiently it does it. Developers will need to become "Token Architects"—pruning context and choosing the right model for the right step. 🔹 The Governance Shift: With the "buffer" of the old request system gone, administrative spending caps are no longer just an option—they are your primary defense against runaway agent loops. We’re moving from an era of "unlimited experimentation" to one of "calculated efficiency." Engineering leaders need to start auditing their heavy agentic workflows now before the May billing preview tool goes live. The logic is simple: If your agents aren't efficient, your budget won't be either. Full details here: github.blog #GitHubCopilot #AI #AgenticWorkflows #EngineeringManagement #CloudEconomics #LLM

🪄 AI agents orchestration is becoming a must-have skill for software engineers We’re moving beyond using a single AI assistant… into coordinating multiple AI agents working together inside your codebase. This shift changes how we build software: - From prompting → to orchestrating - From single responses → to collaborative workflows - From tools → to systems There’s a lot of innovation happening in this space right now, especially in developer tooling. Here are two solid examples worth exploring: 👉 Squad https://msft.it/6048QA8QK Squad introduces the idea of an AI team living inside your repo. You describe a task, and Squad: - Breaks it down - Delegates work across agents - Runs tasks in parallel - Keeps shared memory and decision logs All of this happens within your repository context. In short: it turns Copilot into a coordinated development team, not just a coding assistant. 👉 Azure az-prototype https://msft.it/6049QA8Qz An Azure CLI extension that empowers customers to rapidly create functional Azure prototypes using AI-driven agent teams. It supports three AI providers - GitHub Copilot, GitHub Models, and Azure OpenAI - for intelligent code and infrastructure generation. Curious, are you already experimenting with multi-agent setups? 👇 #GitHubCopilot #AIAgents #AINativeDevInfra #AZPrototype #AgentSquad #GenerativeAI #msftadvocate