Every lever for reducing token spend also removes information the model was using for something. Here's where we actually cut cost in a chat-with-your-database product, and the one place cutting naively breaks the answer instead of just making it cheaper.
A harness governs one model call. A loop is what happens when the agent gets to call again, and again, based on what it just saw. Designing that cycle — its evidence, its termination, its honest failure — is a separate discipline. Here's what we've learned building ours.
Production-grade agents aren't won with a better prompt. They're won in the harness: the constraints, retries, context assembly, and verification wrapped around every model call. Here's how we build ours.
A look at how retrieval-grounded context (not fine-tuning) keeps AI-generated answers honest about your business, and honest about what it doesn't know.