Agentic CI with guardrails: GitHub Next’s “Safe Outputs” for Continuous AI

GitHub Next describes “Continuous AI” as an agentic workflow pattern that runs alongside CI to tackle repository tasks that require judgment, not deterministic rules.

• The framing: traditional CI stays focused on binary checks (pass/fail), while agents handle intent-level drift (docs vs. behavior, subtle perf issues, confusing UX text, or behavior changes hidden behind dependency updates).
• The proposed definition: natural-language expectations combined with agentic reasoning, executed continuously inside a repo, producing reviewable outputs rather than silent changes.
• Example outputs include suggested patches and written findings, and—only when explicitly allowed—artifacts like issues, pull requests, or discussion comments.
• Safety model: agents are read-only by default, and teams define a strict allowlist of permitted artifacts and constraints (“Safe Outputs”) so the blast radius is deterministic, logged, and auditable.
• Named lead: Idan Gazit (GitHub Next) positions this as complementing YAML-based automation, not replacing it.