agentic-engineering

Eval-first agentic engineering executes AI implementation work by defining capability and regression evals, running baselines, and comparing deltas to guide improvement. It controls AI code generation by capturing failure signatures and validating progress against established baselines.

Decompose software engineering tasks by applying the 15-minute unit rule, breaking work into independently verifiable units with clear done criteria. This decomposition ensures AI agents execute structured task breakdown while maintaining traceability and human quality control.

Cost-aware model routing assigns tiered models like Haiku, Sonnet, and Opus to tasks based on complexity. This routing optimizes resource allocation and outcomes by aligning model capabilities with the specific requirements of each engineering task.

Maintain coherent sessions for closely-coupled AI engineering work by preserving context across related tasks, and reset sessions after major milestones or phase transitions. This session strategy ensures structured task breakdown remains reliable and traceable.

Apply risk controls to AI-generated code by emphasizing invariants, edge cases, error boundaries, security, and deployment considerations. This review focus ensures human quality control over agentic engineering workflows and validates regression checks.

Structure agentic engineering workflows by defining a project goal, decomposing it into agent-sized units, routing tasks by complexity to tiered models, and running an eval-first loop to validate progress. This maximizes reliability and traceability across sessions.