agentic-engineering

Eval-first execution prioritizes capability evaluation and regression checks before implementation. It decomposes engineering tasks into agent-sized units with explicit success criteria, ensuring verifiable results and structured session management for AI agents.

Decompose large engineering tasks by breaking them into agent-sized units with defined success criteria. This structured decomposition enables tiered model routing based on complexity, balancing cost and quality while maintaining unit-level verifiability.

Cost-aware model routing assigns engineering tasks across model tiers based on complexity and required accuracy. This tiered approach balances implementation cost against quality requirements, routing simpler units to efficient models and complex units to higher-tier models.

Yes, this approach explicitly satisfies requirements for regression checks and capability evaluation. It maintains verifiability through structured session management with clear done criteria, applying eval-first planning to AI-assisted implementation work with human oversight.

The best way to manage AI agent sessions is through structured session management with clear done criteria. This involves eval-first planning, decomposing work into agent-sized units, and applying tiered model routing to ensure verifiable, human-oversight engineering results.

You need tiered model routing when AI engineering tasks vary in complexity and required accuracy. It enables cost-aware execution by routing agent-sized units across model tiers, ensuring high-accuracy tasks receive appropriate models while simpler tasks use cost-effective alternatives.