Paul Shell

GitHub Copilot can now generate images! Our team is using this to generate app icons, explore designs for upcoming features, and (ofc) step up our meme game.

Probably shouldn't be posting this... 🤫 #GitHubUniverse

Ever been to a hackathon? Follow participants throughout their day, from badge to the final demo. What you can learn from attending a hackathon ⬇️ github.blog/developer-skills…

very much so. software engineering principles are still relevant

Dynamic Workflows in @GitHubCopilot were just introduced. We've been talking a lot about multi-agent orchestration, subagents, parallel execution and handing off tasks between agents. But there's a difference between asking an agent to figure out how to complete a task and defining the actual process it should follow. That's what Dynamic Workflows bring to Copilot. A dynamic workflow is essentially a program that orchestrates agents, deterministic operations, tools and APIs into a structured process. You define the steps, the dependencies, where agents are needed, what they should return, and how the results flow into the next stage. The agents handle the reasoning. The code handles the orchestration. What makes this interesting? - Deterministic orchestration: Define which steps run, in what order, and what happens next, instead of leaving the entire process to the agent. - Parallel execution: Break down a task into independent workstreams, run multiple agents in parallel, and combine their structured results. - Validation and verification: Have agents review each other's findings, enforce output formats, and add deterministic checks between stages. - Human-in-the-loop: Pause at specific checkpoints, review the results, provide input and resume the workflow. - Observability and control: Track workflow phases, agent activity, AI credit consumption and execution time. Set limits on concurrency, total agents, runtime and approximate AI credits. - Resumability: Pause long-running tasks or resume workflows that stopped at a limit, reusing saved results from completed steps. And these aren't just theoretical capabilities. Workflows can run commands, call external services, use tools, and combine all of these with agent reasoning. A practical example: Imagine a workflow for investigating a production incident. 1. Collect logs, telemetry and relevant deployment information using deterministic operations. 2. Spawn multiple agents to independently investigate different services. 3. Require each agent to return structured findings. 4. Cross-check the findings and ask agents to challenge each other's conclusions. 5. Combine the verified results into a timeline and root-cause analysis. 6. Pause for an engineer to review the findings before proceeding with remediation. The process is defined once and can be reused, while the agents still have room to reason about the specific incident. How to get started Dynamic Workflows are available in the GitHub Copilot app, Copilot CLI and Copilot SDK, currently in public preview. In the Copilot app, no additional setup is required. In Copilot CLI, enable experimental features with --experimental or /experimental on. You don't even have to write the workflow yourself. Just ask Copilot something like: Create a dynamic workflow named review-changed that lists changed files, asks an agent to review them, and summarizes the findings. Copilot authors the workflow inside an extension. By default, it's scoped to your current session, but you can move it into a personal or project extension and share it with your team. You can then run it by name, monitor it through /workflows in the CLI or the Workflows panel in the app, and revise its definition as you learn from actual runs. You can also run existing workflows directly from the CLI, making them usable in scripts and CI/CD pipelines. How it differs from Claude Code's Dynamic Workflows? Anthropic has also introduced Dynamic Workflows in Claude Code, with a similar idea of generating a task-specific orchestration harness rather than relying on a fixed, generic sequence. The interesting difference is in the implementation and integration. Claude Code's approach focuses on dynamically composing agents through workflow scripts, while Copilot's implementation defines workflows in extensions, with explicit steps, integrations and lifecycle controls across its app, CLI and SDK. Both are exploring the same broader direction: moving beyond simple agent delegation toward more structured, reusable and observable agentic processes. My take We've been putting a lot of effort in optimizing individual agents, their prompts, skills, tools and context. But as we start running more complex tasks, the orchestration layer becomes just as important as the agents themselves. I see Dynamic Workflows as another step toward treating agents as components inside software-defined processes, rather than expecting a single agent to figure out everything from start to finish. The next challenge is making these workflows reusable, testable, versioned, measurable and governed across teams. More links in the comments: