Set up the knowledge base, pick the right retriever, and ground your agent in real-world questions. Tools, prompts, and deterministic flows are configured to your business, not a generic template.

Simulate end-to-end conversations against real personas and scenarios before a single customer touches the agent. Annotate failures, turn each failure mode into an evaluator, and validate that evaluator against a human-labeled set so you can trust it in production.

Push the agent live with confidence and keep the evaluators running on every real conversation. Code-based evaluators measure what's measurable; LLM-as-judge evaluators score the  subjective parts. Each conversation gets bucketed into pass, fail, or a specific failure mode.

See exactly which failure modes are most prevalent, why they happen, and which conversations hit them. Cohort-based analysis tracks whether a fix actually moved the number in  production, not just in a test set.

Fix the failure modes the system surfaces. Add new evaluators as your bar rises. Each loop catches more, fixes more, and raises the floor so the agent gets meaningfully better not   from a model upgrade, but from the loop itself.