AI agents orchestration
An agent that works in a demo and an agent that holds up in production are two different objects. The first needs a good prompt; the second needs an architecture, guardrails, a way to measure its mistakes, and someone accountable for them.
What this covers
We design and ship systems built on language models: conversational agents, automation of business tasks, extraction and structuring of information, and the orchestration of several models and tools in service of a single journey.
The work rarely starts with picking a model. It starts with a duller and far more decisive question: which precise use case has to work end to end, and how will we know that it does? The answer separates a useful agent from an impressive demo.
From there we work across the whole chain: which tasks belong to the model and which have no business being there, the choice and sequencing of tools, context management, guardrails on outputs, failure handling, observability, and continuous evaluation against real cases rather than intuition.
What we bring
AI is not a subject we discovered with language models. We were trained in Machine Learning inside a French unicorn, and we have worked on these problems for several years, through client projects and through our own products.
Fourteen projects running AI models are in production today. We also built Naaro, our own conversational experience, the one that lets you discover Vezero by talking to an agent rather than reading a website. Living with one from the inside changes what you know about its blind spots.
We know, therefore, where agent projects fail: a scope decided before the use case, evaluation that is absent or purely qualitative, a cost per request discovered too late, latency incompatible with the journey, and above all an agent handed a decision that nobody then owns.
How we work
A team of four covering strategy, design, frontend, backend, data and infrastructure. No delegation, no outsourcing, no intermediary layer: the people discussing the trade-offs are the ones writing the code and living with those choices over time.
That proximity matters especially on an AI project, where product decisions and technical constraints are inseparable. How much autonomy an agent gets, what it is allowed to invent, what stays locked down: those are trade-offs settled with design and infrastructure in the same conversation, not across two separate meetings.
What we measure
An agent cannot be validated on impressions. Together with you we assemble a set of representative cases drawn from your real usage, including the awkward ones nobody raises in a framing meeting. That set becomes the reference every change is evaluated against.
It turns questions that would otherwise stay opinions into answers: is this new version better or merely different? What share of requests does the agent handle alone, and which ones must it know to refuse? What does a request cost, and how does that move when traffic doubles?
We also instrument what happens in production: what users actually ask, where the agent fails, what triggers a human handover. That loop, far more than the choice of model, is what improves a system over months.
When to bring us in
- _You have a prototype that convinces in a demo and you cannot tell what separates it from production.
- _You want to integrate an agent into an existing product without degrading what already works.
- _You have a hunch about a use case and want to test it against reality before committing a team.
- _You have an agent in production whose quality you have no way to measure.
Deliverables
- _The use case framed, with explicit success criteria
- _System architecture and technical trade-offs, documented
- _Production implementation, with guardrails and failure handling
- _An evaluation setup against real cases, plus observability
- _Business and technical documentation maintained throughout
How we frame, arbitrate and commit is set out in detail in the Vezero Method.