Production is where AI products get honest

In eight weeks, we took an agentic AI system from concept to production for a confidential client.

At its core is a conversational agent with function-calling access to the application's own database. It can search, create, and update records on the user's behalf, inside a bounded tool-execution loop with explicit confirmation before anything changes. Giving a language model write access to a database raises the stakes, so much of the engineering went into safety: identifiers resolve server-side so the model never sees them, every streamed chunk is scrubbed before it reaches the user, and everything retrieved is treated as untrusted input.

The agent was the heartbeat, but we shipped the whole product around it: multi-tenant workspaces, document upload and ingestion pipelines, background processing, and analytics views built from the structured data the AI extracts. One team, end to end, from the data model to the frontend.

We shipped it with an eval suite. The production system prompt and tool schemas run against the live API, checking that the model picks the right tools with the right arguments, alongside an in-product feedback loop that captures expected answers from reviewers. Costs got the same attention: model tiers per task, token caps, and deterministic bypasses that answer predictable requests without an API call.