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This article focuses on agentic engineering workflows where AI agents assist developers in planning, building, verifying, and observing software projects across the entire lifecycle. It highlights how tools like GitHub Copilot integrate with existing collaboration platforms to synthesize scattered context and automate complex tasks beyond simple code generation. The core value lies in shifting developer effort from manual coordination of requirements to higher-level judgment on implementation tradeoffs and production evaluation.
Beyond code generation: A developer’s guide to agentic engineering

Writing code got faster. The work around it didn't. See how you can plan, write code, review changes, and investigate production issues with GitHub Copilot, and what work still needs your judgment. The post Beyond code generation: A developer’s guide to agentic engineering appeared first on The GitHub Blog.
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. See if this sounds familiar. You’re planning an API change. The design is in one…
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