How to Codify Nuance in Agentic Engineering

AI coding agents can pass local tests while overlooking broader commitments such as cross-repository client dependencies, prior review rationale, and support policies, making context-aware engineering decisions essential. Using the example of removing a deprecated API field still consumed by supported mobile clients, the discussion argues that agents need inspectable evidence from codebases, pull-request history, policies, and local worktrees to identify compatibility constraints and disclose remaining uncertainty. Agent memory can preserve prior reasoning, but it should record the evidence, scope, and conditions under which a decision can be reconsidered rather than treating past conclusions as permanent rules. Codebase intelligence, memory, standards, procedural skills, and execution harnesses serve complementary roles in bringing relevant knowledge into tasks and learning from review outcomes. Such systems should be evaluated with scenarios where the correct recommendation changes according to client support, migration status, relevance of a constraint, or gaps in available evidence, with emphasis on accurate citations, appropriate scope, and transparent reasoning.

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