agentic-engineering

An eval-first workflow for AI code generation requires defining evaluation gates and capturing baseline failure signatures before implementation. After changes are made, re-running evals verifies deltas to reduce rework and ensure software quality.

Model routing for agentic software engineering assigns smaller models to classification or boilerplate tasks, while reserving larger models for deep implementation and architecture. It manages context resets to balance cost discipline with execution reliability.

Break down refactoring tasks into agent-sized, independently verifiable units. Each unit must have a clear completion condition and a dominant risk, allowing the agentic engineering process to implement and validate changes safely.

Human code review during AI software development should focus on architectural invariants, security assumptions, and rollout coupling. It bypasses style-only differences to prioritize edge cases and critical system invariants.

Yes, agentic engineering workflows manage costs for multi-file architectural changes through measured retries, wall-clock time tracking, and explicit task routing across model tiers to prevent runaway token usage.

Agentic engineering workflows may be unnecessary for trivial edits lacking clear completion criteria. If a task cannot be decomposed into independently verifiable units with regression gates, the overhead of eval-first planning offers little value.