Beyond code generation: A developer’s guide to agentic engineering

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Nick Liffen & Philip Holleran November 6, 2026 | 8 minutes Share: This October, GitHub saw more than 1.2 billion commits in one week—over 400% more than a year earlier. This is more commits than GitHub received in all of 2025. Chances are, you’re generating more code than you were a year ago. But ask yourself: do you feel 400% more productive than you were last year?

Building software takes more than a good prompt. As AI speeds up implementation, software developers are feeling more pressure in other places: a Faros AI study found that pull requests spent 441% longer in review when teams went from low to high AI adoption. As a software developer, you gather requirements and steer implementation, review changes and evaluate test results, then investigate what happens in production. Agents can help you work more efficiently across all of these stages: Plan, Build, Verify, and Observe. We’ll walk through what an effective agentic engineering workflow looks like on GitHub. Plan: Get everyone and every agent on the same page See if this sounds familiar. You’re planning an API change. The design is in one tool, a compatibility requirement is buried in Slack, and an old pull request explains why the current implementation works this way. Before you can write a useful plan, you need to connect those details, compare approaches, and agree on how existing clients will keep working. What looked like a straightforward planning task has become hours of searching, cross-checking, and chasing answers. You’re exhausted before implementation even begins. You can use Copilot to pull that scattered context into one place, work through the tradeoffs, and turn the decisions into an implementation plan. Plan together where your team already works. Use Copilot in Slack or Teams to collaborate with everyone involved—from product managers and designers to analysts and business stakeholders. Bring in context from connected tools so your team can check assumptions, contribute ideas, and refine requirements together. Turn agreed-upon requirements into actionable work. Ask Copilot to create GitHub Issues with the context and acceptance criteria needed for implementation, breaking out tasks that can be handled separately. Keep routine triage from consuming planning time. Use GitHub Agentic Workflows to label your issues and estimate task complexity. Assign low-complexity tasks directly to Copilot, then spend your time focusing on harder decisions. Give complex work the planning it needs. Use Plan Mode in your preferred Copilot surface to compare approaches and refine an implementation plan. Review and approve the plan before handing it off to Copilot to begin implementation. What took days of searching and context gathering now produces a reviewable plan, explicit acceptance criteria, and scoped issues, with your time spent on the decisions instead of the retrieval. Then, once you settle on an approach, you’ve already set yourself and your agents up to implement your plan. Build: Move more than one task forward at a time You’re ready to turn that plan into code. But life has other ideas. Your agent is halfway through a task, your next meeting starts in five minutes, and your dog has been staring at the door for twenty. Then someone in the meeting points out an edge case the implementation needs to handle. Everyone needs something from you at once. You’d like to keep making progress, but you can’t be in three places at the same time. You can keep your work moving without being tied to the computer where you started it. Continue your conversation from another device. Session continuity lets you pick up the same Copilot session across clients, so you can work wherever it’s most convenient for you. Let work continue after you close your laptop. Copilot runs tasks in cloud sandboxes, keeping execution off your machine and allowing you to leave and return to a session later. Work on separate tasks without mixing their edits. The Copilot app gives sessions isolat