agentic-engineering

To optimize compute costs in AI agent workflows, apply cost-aware model routing that matches model tiers like Haiku, Sonnet, and Opus to specific task complexity, ensuring output quality is maintained without wasted compute spend.

Eval-first development for AI code generation pipelines defines capability and regression evaluations upfront to measure implementation impact and catch quality regressions before they reach production environments.

Decompose tasks for AI agent automation tools by breaking work into 15-minute independent units with clear done conditions, which reduces implementation risk and improves the verifiability of agent outputs.

The best way to manage ungoverned AI agent engineering sessions is to apply structured session management for agent work, combining granular task decomposition with tiered model routing to control compute spend.

Tiered model routing works for complex software refactoring tools by routing simple classification tasks to Haiku, core implementation to Sonnet, and architecture design to Opus, while tracking cost and running evals.

AI agent workflows produce inconsistent outputs and high compute spend due to ungoverned engineering processes lacking eval-driven development, granular task decomposition, and tiered model routing by task complexity.