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

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

Software engineering has always included work that’s repetitive, necessary, and historically difficult to automate. This isn’t because it lacks values, but because it resists deterministic rules.  Continuous integration (CI) solved part of this by handling tests, builds, formatting, and static analysis—anything that can be described with deterministic rules. CI excels when correctness can be expressed unambiguously: a test passes or fails, a build succeeds or doesn’t, a rule is violated or isn’t.  But CI is intentionally limited to problems that can be reduced to heuristics and rules.  For most teams, the hardest work isn’t writing code. It’s everything that requires judgment around that code: reviewing changes, keeping documentation accurate, managing dependencies, tracking regressions, maintaining tests, monitoring quality, and responding to issues that only surface after code ships.  But a lot of engineering work goes into work that requires interpretation, synthesis, and context, rather than deterministic validation. And an increasing share of engineering tasks fall into a category CI was never designed to handle: work that depends on understanding intent.  “Any task that requires judgment goes beyond heuristics,” says Idan Gazit, head of GitHub Next, which works on research and development initiatives...

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