Continuous AI in practice: What developers can automate today with agentic CI

The concept of Continuous AI is introduced as a complement to Continuous Integration (CI) by focusing on tasks requiring reasoning and judgment rather than deterministic rules, which CI handles well. GitHub Next is exploring this pattern, where AI agents operate within repositories to manage context-dependent engineering tasks, such as checking for documentation discrepancies, generating project reports, and detecting performance regressions. Unlike CI, which deals with binary outcomes, Continuous AI employs natural-language rules and agentic reasoning to address complex tasks that depend on understanding intent. Safeguards are integral to this approach, ensuring agents operate within defined boundaries and with explicit permissions to maintain control and accountability. This methodology allows developers to delegate judgment-heavy tasks to AI, thus transforming episodic chores into continuous processes without replacing existing CI systems.