- Published on
Beyond code generation: A developer’s guide to agentic engineering
- Authors
- Name
- DeepHorus
In October, GitHub recorded more than 1.2 billion commits in one week, over 400% more than a year earlier. Writing code has accelerated sharply, but the surrounding engineering work has not kept pace. GitHub argues developers now need agentic engineering, not just faster code generation.
A Faros AI study found pull requests spent 441% longer in review when teams moved from low to high AI adoption. That gap shows how implementation speed can shift pressure into planning, review, testing, and production investigation. GitHub frames the response around four stages: Plan, Build, Verify, and Observe.
In planning, Copilot can gather scattered context from Slack, Teams, and connected tools. Plan Mode lets developers compare approaches and approve an implementation plan. Copilot can create GitHub Issues with acceptance criteria, while Agentic Workflows labels issues and estimates complexity. Low-complexity tasks can be assigned directly to Copilot.
For implementation, session continuity lets developers resume the same Copilot session across clients. Work can continue after a laptop closes, helping developers keep multiple tasks moving while handling meetings or edge cases. This reduces dependence on one device or uninterrupted time.
GitHub’s guide also covers review and production investigation, where agents can help evaluate changes and diagnose issues. Still, developers retain judgment over requirements, architecture, approvals, and final decisions. The workflow is designed to speed retrieval and routine triage, not remove human oversight.
The implications are significant as AI adoption rises. More generated code increases review load and coordination demands. Agentic workflows aim to connect planning, coding, verification, and observation so teams gain throughput without simply creating larger review queues.
The outlook depends on disciplined adoption. GitHub positions Copilot as a collaborator across the software lifecycle, with humans approving plans and complex choices. If teams integrate these practices carefully, they may turn faster code generation into broader engineering productivity.
