Most agentic-AI work in the wild is a prompt, a chain, and a deployment prayer. We build it differently. Every agent we ship runs through eval harnesses against real task traces. Each run carries a cost budget, tool calls hit a guardrail, and a human-in-the-loop escape hatch catches the cases the model gets wrong.
We've built production agents for customer support triage, document processing, research synthesis, code review, and internal-ops copilots. The pattern repeats. Measure first. Scope the action space tightly. Instrument everything. Ship behind feature flags with a rollback path.