Building a code review agent on the Cline loop

Cline describes how to build a lightweight, read-only AI pull request reviewer using the Cline SDK, motivated by the growing code-review bottleneck created as LLMs accelerate code production. The design combines a review-guidelines prompt that defines what constitutes bugs, security issues, performance concerns, and repository-specific convention problems; deterministic guardrails that prevent file edits, dangerous shell commands, and GitHub mutations; an audit journal that records tool usage, runtime status, token consumption, and cost; and custom tools for recording and later approving findings. The agent operates in two passes: an initial reviewer examines a PR diff and relevant repository context to create line-specific findings, while a stricter judge independently rechecks those candidates and retains only defensible, high-signal issues. A deterministic driver then sorts approved findings by severity and prepares a batched GitHub review using the non-blocking COMMENT event, with dry-run behavior enabled by default and fallback posting if inline comments cannot be attached. The approach emphasizes that effective automated review does not necessarily require a complex multi-agent architecture, but instead depends on clear instructions, constrained tool access, structured outputs, validation, and a final human-friendly reporting workflow.

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