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

GitHub reports that AI-assisted development has sharply increased code output, with more than 1.2 billion commits in one October week, but argues that higher commit volume does not automatically produce equivalent productivity because planning, reviewing, and operating software remain significant bottlenecks. The proposed “agentic engineering” workflow uses GitHub Copilot across four stages: planning, where it consolidates scattered context, creates issues, and helps develop implementation plans; building, where cloud-based, persistent, isolated sessions and shared repository knowledge allow multiple tasks to progress; verification, where smaller stacked pull requests, automated quality and security checks, dependency controls, and agent-driven fixes aim to reduce review delays; and observation, where connections to observability and business-data tools help teams investigate production incidents and turn findings into future improvements. The authors recommend starting with a single frustrating task and gradually extending agent support across the development lifecycle.

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