· ai-devtools
Beyond code generation: A developer's guide to agentic engineering
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. 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.
