AI Agent Development in Paris

AI agent development for systems that finish the job

An agent is a model given tools, memory and permission to act. That last word is where most projects go wrong. e10 Infotech Private Limited builds agents with the permission boundary drawn first: which tools exist, what each one may touch, which steps need a person to approve them, and what happens when the model is confidently incorrect. The result is a system that completes multi step work and can be audited afterwards, rather than a demo that impresses once and is quietly switched off. Businesses in Paris come to us when an agent has to touch production systems.

What we build

1. Task agents that research, summarise, draft and file work into your systems
2. Customer facing agents that resolve requests end to end with escalation paths
3. Operations agents that triage tickets, reconcile records and chase exceptions
4. Coding and data agents that work inside your repositories and warehouses
5. Multi agent systems with a planner, specialist workers and a reviewer
6. Voice agents for inbound and outbound calls with live tool access
7. Agent platforms with a tool registry, permissions and shared observability

How an agent build runs

Scoping picks one job with a measurable outcome, because an agent asked to do everything reliably does nothing. Tools are defined as a typed contract with least privilege, and anything irreversible starts behind an approval step that can be relaxed once the evidence supports it. State and memory are explicit rather than an ever growing transcript, so context stays small, cheap and predictable. Every run produces a trace of the plan, the tool calls, the arguments and the result, which is what makes failures debuggable. We build an evaluation set from real cases before the agent ships and score every change against it. Loop protection, step budgets, timeouts and cost ceilings are configured from the first deployment rather than after the first surprise bill.

Where this fits

Agent work sits inside our AI First Development practice. Agents ground themselves in your content through RAG and Knowledge Systems, reach models through LLM Integration and Fine Tuning, and produce content through Generative AI Development. Conversational surfaces come from AI Chatbot Development, deterministic back office runs through AI Workflow Automation, and serving, tracing and cost control from MLOps and AI Infrastructure. Use case selection is handled by AI Consulting and Strategy, and your own engineers work faster with Building with AI Coding Tools. Surrounding applications come from Software Development and Web Development.

Tools, memory and control

Tools are the agent's real capability, so they are designed like an API: narrow, typed, idempotent where possible, and returning errors the model can act on. Permissions are scoped per tool and per user, so an agent acting for a customer can never read another customer's record. Memory separates the current task, durable facts and retrieved knowledge, each with its own lifetime. Where a plan is long, we checkpoint it so a failure resumes rather than restarts. Human review sits at the points where a mistake would be expensive, and the interface shows what the agent intends to do before it does it.

Reliability and safety

Content arriving from web pages, documents and emails is untrusted input, so instructions inside it are never executed as commands. Output is validated against a schema before it reaches another system. Rate limits, step budgets and spend caps stop runaway loops. Every action is logged against the user who authorised it, and there is always a way to stop an agent mid run and see exactly what it had done up to that point.

What you get at handover

Source code in your repository, infrastructure as code in your cloud account, the tool registry with permission definitions, the evaluation set and its scores, tracing dashboards showing plans and tool calls, a cost per run breakdown, an escalation and rollback runbook, and a thirty day warranty on behaviour that does not match the specification.

Working with businesses in Paris

Work for clients in Paris runs remote first: a named engineering team, a scope agreed in writing before anything starts, and demos on a fixed cadence you can hold us to. Working hours overlap your business day and everything is delivered in English.

Rules on automated decisions affecting individuals, and on where data may be processed, differ by jurisdiction, so we confirm what applies to businesses in Paris before an agent is given authority to act. That answer shapes the approval design rather than following it.

You get one point of contact, senior engineers on delivery, and reporting tied to evaluation scores and completed tasks rather than activity. Businesses in Paris and the wider region are supported on the same terms.