Documents, events and API state normalised into something the model can reason over without hallucinating structure.
Explicit state machines where the process is known, model-led planning only where it genuinely varies.
Typed, idempotent tools with least-privilege credentials. Every write is reversible or requires approval.
Traces, evals, cost per run and a review queue. This is the layer that decides whether anyone trusts it in month three.
Fixed scope, fixed price, one workflow. At the end you have a working agent on your data, an eval report, and a written recommendation, including whether to stop.
The repository, the infrastructure code, the eval set and the traces. In your accounts, under your licence.
Shadow the team doing the work today. Write the eval set from real cases, including the ugly ones.
Two or three real tools, guardrails in place, running against a copy of production data.
Shadow mode first, then a percentage of live volume, then the queue. Kill switch throughout.
Monthly eval review, model upgrades tested before they ship, or a clean handover to your team.
Yes, through tools scoped exactly like a junior employee's permissions: least privilege, idempotent writes, and approval gates above a threshold you set.
Whichever wins your eval set. The architecture keeps the model swappable, because the leader changes every few months and your bill shouldn't.
No. Inference runs in your cloud or under enterprise terms with training disabled, and we document the data path before we build.
You get the report saying so, and the reason. A cheap no is a good outcome. The expensive version is finding out after a year of platform work.