agentic-engineering

You decompose multi-step software tasks by breaking them into independently verifiable units with clear done criteria and a single dominant risk. This allows AI agents to execute implementation while humans enforce quality and risk controls to reduce drift.

An eval-first loop is a workflow that runs capability evaluation, regression checks, baseline capture, and delta comparison before implementation. It ensures AI-assisted software development maintains strict quality assurance and safety guardrails throughout the process.

You route AI models by task complexity using a tiered strategy: Haiku for simple tasks, Sonnet for moderate complexity, and Opus for critical work. This balances speed and safety across modular AI-assisted software development workflows.

You review AI-generated code by focusing on invariants, error handling, security, and risk management. This review focus ensures the code meets strict safety guardrails and passes the quality assurance checks required by the agentic workflow.

Yes, agentic engineering supports risk-aware production planning across evolving projects. It uses a session strategy for multi-phase work and milestone-driven closure to ensure stable, verifiable implementation as your project scales.

The best way to plan AI-assisted module implementation is to provide an initial agentic plan using an eval-first approach. This enforces task decomposition, tiered model routing, and strict code review from the very beginning of the workflow.