Every agentic AI project we have taken into production has looked the same underneath, whatever the industry on the front. A model is given a small set of tools, a hard boundary around what it may do without a human, and a way of proving afterwards that it did the right thing. Everything else is regular software.
The three parts that matter
The tool boundary. An agent is only as safe as the smallest set of actions you give it. An AI dispatcher can read jobs, propose an assignment and message a driver; it cannot cancel a booking or issue a refund. Every tool is a function with validation, an audit row and a permission check, exactly as it would be if a junior colleague were calling it.
The escalation path. Decide before launch what "I do not know" looks like and where it goes. Low confidence, an angry customer, a payment question, anything touching money above a threshold — those route to a person with the full transcript attached. Systems without this do not fail gracefully, they fail invisibly.
The evaluation loop. A fixed set of real cases, replayed on every prompt or model change, with the results compared against what a competent human did. Without this you cannot change anything after launch without guessing.
Where it earns its keep
The pattern pays off where a queue exists and the work is repetitive but not trivial:
- HoReCa — a concierge that takes reservations, answers the same twelve questions about parking and breakfast, and hands the rest to reception.
- Transport — a dispatcher that proposes assignments from live vehicle positions and messages drivers, with the human keeping the override.
- E-commerce — a support agent grounded in order state, so "where is my parcel" is answered from the actual shipment record rather than a guess.
- Services — an intake agent that qualifies an enquiry, collects what the estimator needs and books the site visit.
What we will not do
We do not put an agent in front of a workflow nobody has written down. If your operations team cannot describe the decision, the model will invent one. Mapping the current process is part of the pilot, and occasionally the honest outcome of that mapping is that you need a form and a rule engine, not an agent.
Cost, plainly
The pilot buys a measured result on one workflow. Production buys the hardening around it: monitoring, evaluation suite, escalation UI, rate limits, cost controls. The group tier is for organisations running several agents on shared plumbing. Numbers for each are published on the pricing page — including what is out of scope — so you can compare us before you talk to us.
If you want the full architecture and the case studies with before-and-after numbers, the Agentic AI page has both.
Frequently asked
What is an agentic AI system, in practical terms?
A model given a narrow set of tools — read a booking, create a job, send a message — plus rules about when it must stop and hand over to a person. The interesting engineering is in the tool boundary and the escalation path, not in the model choice.
How long does an agentic AI pilot take?
Our pilot is a bounded four to six week slice against one real workflow with real data, ending with measured deflection or handling-time numbers rather than a demo.
What does agentic AI cost to run?
Build cost is the smaller half. Plan for model inference per handled conversation plus the monitoring and evaluation you need to keep quality honest. We publish the tier pricing on our pricing pages rather than quoting per meeting.