agentic-engineering

To manage risk in agentic engineering, you use an eval-first loop where AI agents implement solutions while humans validate quality. Tasks are decomposed into independently verifiable units with defined done conditions to ensure safety.

Eval-first routing runs capability and regression evaluations before execution to ensure verifiable outcomes. It routes tasks across Haiku, Sonnet, and Opus model tiers based on complexity, maintaining cost discipline through per-task tracking.

You track AI agent costs by applying per-task cost discipline that monitors model usage, tokens, retries, and wall-clock time. This auditable cost tracking ensures financial control across complex, multi-step software engineering workflows.

Yes, you can route tasks across Haiku, Sonnet, and Opus tiers based on task complexity. This model-tier routing ensures efficient resource allocation while maintaining session coherence across related units and clean handoffs between phases.

The best way to decompose projects is through systematic task decomposition into independently verifiable units with defined done conditions. This approach enables clear session strategies for maintaining coherence and ensuring auditable outcomes.

You should avoid agentic workflows when tasks cannot be decomposed into independently verifiable units or when eval-first loops are impractical. Complex projects lacking defined done conditions or clear phase handoffs are poor candidates.