Agent CI - Continuous AI for Development Quality

Continuous AI extends CI/CD by applying AI agents to quality tasks that require reasoning, interpretation, and context—work that traditional CI cannot handle because it depends on understanding intent rather than enforcing rules.

Core Concepts

Continuous CI vs Continuous AI:

• CI handles deterministic rules: tests pass/fail, builds succeed/don't, linters flag violations
• Continuous AI handles judgment-heavy tasks: doc/code alignment, semantic changes, dependency drift, performance regressions requiring context

Key patterns:

1. Natural-language rules express intent that cannot be reduced to heuristics
2. Agents evaluate repositories continuously (on schedule, PR events, pushes)
3. Outputs are artifacts developers can review (PRs, issues, reports)
4. Developers retain final authority; agents never auto-merge

When to Use Agent CI