An AI agent is not a chatbot
A chatbot answers questions. An AI agent receives a request, analyses it, fetches what it is missing from your tools, then acts: it creates the entry, prepares the file, sends the reply, alerts the right person. The difference lies in access to tools and in the rules you give it.
That is what makes an agent useful, and it is also what demands rigour: an agent that acts inside your business systems must do so within a corridor, with precise permissions and a trace of every action.
The right first use cases
Projects that succeed start from a concrete, quantifiable pain point, not from a desire for AI. Here are the situations in which devlab has already shipped production solutions, for companies in Papeete and Punaauia.
Automatic document reading: thousands of supplier invoices processed every month at an importer, figures captured without re-entry.
Approval flows: hundreds of paper purchase orders taken out of circulation every month at a retail group.
Field teams: binders of client sheets turned into mobile access, synchronised even offline, at an after-sales specialist.
Import file preparation: documents read, understood and captured through to regulatory filing.
Incoming requests: qualification and first reply to e-mails, forms or messages, with hand-off to a human when needed.
What you need before starting
Three things, none of them technical. Accessible data: if your invoices sit in a cardboard box, the first step is to digitise them, not to buy AI. An owner on the company side, who knows the process and will settle ambiguous cases. And a baseline measurement: how much time, how many errors or how many francs the task costs today.
That measurement is the only way to know, three months later, whether the agent pays off. It is the "Quantify" stage of the devlab method, and we never skip it.
The risks to scope from day one
A model can be confidently wrong. The safeguard is to define what it may do alone, what it proposes for approval, and what it never touches. Permissions are set before go-live, not after the first incident.
Sensitive data (customers, payroll, health) requires knowing where it travels and who hosts it. Finally, do not depend on any single model provider: devlab works multi-model, which makes it possible to switch engines when a better or cheaper one appears, without rebuilding the agent.
Permissions: act alone, propose, or never touch, decided per task.
Trace: every action of the agent is logged and reviewable.
Data: known hosting and transit, sensitive data kept separate.
Evaluation: an unmeasured agent is a demo; we instrument before we connect.
Independence: several models possible, no provider imposed.
How devlab starts an agent project
With a free 30-minute call, to understand the pain point and check that an agent brings a real gain. If so, we scope the first agent, connect it to your tools and your ERP, and put it into service progressively, on real flows, with measured results.
We recommend a subscription to keep up with rapid changes in AI and continuously improve your tools, without a large upfront investment. Purchasing lets you own your solution, with a significantly higher initial budget; further development then needs to be planned to keep it current. And we tell you frankly when AI is not the right answer: sometimes a well-built form solves the problem.