Industries

No, and the tag next to each sector is there for exactly this reason. What determines whether agentic AI works is not your industry label but the shape of the work: is there a defined source of truth, a countable outcome, and a reviewer already in the loop? A specialist insurer, a legal team and a clinical content publisher look like different industries and are the same engineering problem. Pick the page whose unit of work resembles yours and the mechanism will read as familiar.

Print on demand and e-commerce, through Mixam — a production three-agent order configurator. Engineering software through Synera, healthcare through Schmitt-Thompson, and multilingual support through ARIJ Network. On several of the other pages we say plainly that we have not yet shipped inside that sector and show the adjacent engagement instead; we would rather you know which is which before the first call.

The mechanism is the same and the constraints are not. Retrieval grounded in your own sources, staged validation before anything reaches a person, an explicit path for the cases the agent should refuse, and a full audit trail — that part does not change. What changes is what counts as a source of truth, who is allowed to approve, and what a wrong answer costs. In clinical triage a confident wrong answer is a patient-safety event; in catalogue enrichment it is a return. Those two systems share an architecture and almost nothing else.

With the one where a wrong answer is cheapest and the volume is highest — not the one with the best story for the board. First production agents earn their credibility on workflows where mistakes are recoverable, and that credibility is what buys you the regulated workflow six months later.