AI agents & API integrations
Design an agent that can consult your tools, prepare an action and carry it out within a defined scope. From cloud services to sovereign deployment, with explicit access and verifiable results.
A single request means looking up information, copying it, then updating an application. You want to connect these steps while keeping the checks that matter and each user’s permissions.
You want to move from an answer to an action: check availability, prepare a booking or look up a reference in an external source. You need to decide what the agent may do, what it must have confirmed and when it must stop.
The model handles a few examples. It still has to be connected to your applications, with access management, error handling and checks on the result across the complete workflow.
The scope and deliverables are agreed together during scoping.
Start from a concrete request, the applications involved and the expected result. Identify the business decisions, the access available and the steps where a simple rule is enough.
Connect the tools required and test anonymized cases, including missing information, denied access and interruptions. Check that a retry does not trigger a second, unwanted action.
Validate the workflow with its users, document operations and organize onboarding. Observations from real use then inform which actions to extend or adjust.
Preparing a legal brief combines research through the official APIs of Légifrance (the official French legal database) and Judilibre (the French courts’ case-law database), drafting, and reference checks. The lawyer reviews the draft: this work illustrates an AI pipeline connected to external sources and to professional review.
Read the case study : Plaidoria — sourced legal draftingThe booking assistant checks availability, books and cancels within the application. This work shows how a conversational request can trigger an action in a business tool.
Read the case study : Le Salon — meeting room conciergeNot always. If inputs, rules and outputs are predictable, deterministic automation may be enough. A model is justified when a request or variable content has to be interpreted. Scoping may lead to a solution that combines both, or one that uses no AI at all.
We look at their APIs, imports and exports, the access you can grant and the limits of each service. Some connections are direct; others need adaptation or are not possible with the permissions available. The first conversation can start from a list of tools and a described workflow, without sharing any access credentials.
Yes. External APIs and cloud services are among the options considered. When your constraints call for a sovereign environment or local processing, the architecture is adapted accordingly. The choice depends on the data, the permitted flows, the dependencies you accept and your capacity to operate the system.
Acceptance testing looks at the completed task: the right information consulted, the right action taken, the right person asked for approval. It also covers ambiguous requests, tool errors and repeated attempts. Results, required corrections and usage costs are reviewed on agreed cases before the scope is widened.
Next step
Describe a task, the tools involved and what currently requires your intervention. This first example helps determine whether you need an agent, an integration or a simpler automation.
