agentic-engineering

To reduce compute costs in AI agent systems, apply tiered model routing to assign tasks to the most cost-effective LLM tier, reserving high-capability models like Opus for complex analysis and using simpler models for basic tasks.

Eval-first development for LLM workflows is the practice of defining capability and regression evaluations before implementation to catch failures early. You need it when building production-grade AI agents requiring reliable, verifiable outputs.

Break down complex AI agent tasks using 15-minute task decomposition to split work into small, independently verifiable units with clear completion criteria, reducing implementation risk and improving project traceability.

Tiered LLM model routing works for multi-agent research tools by distributing workloads across Haiku, Sonnet, and Opus tiers, allowing development teams to structure workflows and run baseline evals while optimizing spend.

The best way to prevent context waste in AI agent workflows is to implement session management alongside granular task breakdown and eval-driven development loops, ensuring context is strictly scoped to verifiable work units.

Undecomposed AI agent development leads to unreliable outputs because missing evaluation checks and unoptimized model usage cause wasted compute and delayed timelines, whereas structured decomposition enforces objective progress tracking.