agentic-engineering

Eval-first AI engineering enforces explicit evaluation and regression checks before implementation. It coordinates agents to deliver verifiable work units by validating invariants, error handling, and security through an incremental testing loop.

Manage AI agent costs by applying tiered model routing based on task complexity, routing simple work to Haiku and complex logic to Opus. This cost-aware approach tracks per-task model usage, retries, and time to maintain discipline.

Decompose large features by breaking end-to-end requirements into independently verifiable, agent-sized work units. This incremental decomposition allows AI agents to execute tasks with human oversight and explicit risk controls.

Yes, tiered model routing balances speed and quality by matching task complexity to the appropriate model tier. You route simpler agent tasks to faster models and reserve advanced models for complex implementation and review.

Handle long-running agentic workflows using session lifecycle management to track milestones. This strategy maintains context and coordination across extended AI-driven engineering tasks with human oversight and incremental validation.

Without risk management, AI-driven engineering lacks verifiable implementations and cost discipline. Bypassing eval-first loops and task decomposition leads to untracked model usage, missed regressions, and unchecked security vulnerabilities.