Benchmarks are useful, but they do not tell you whether an agent will survive your workflow. Production evals need traces, tools, permissions, edge cases, and human review.
A practical guide to running agents in shadow mode so you can compare outputs, measure risk, and launch with fewer surprises.
A practical playbook for keeping agent knowledge current with source SLAs, expiration rules, and retrieval pipelines that age gracefully.
A practical guide to keeping agent memory useful, safe, and inexpensive by design.
A practical guide to designing clean handoffs so humans stay in control without slowing your agent down.
A practical guide to designing intake forms and request schemas so your AI agent stops guessing and starts delivering consistent outcomes.
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