Enterprise AI agents: from assistant to autonomous system
Understand what an AI agent is, when to use it, how to architect it and deploy it in production — with guardrails.
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An AI agent combines language understanding, access to your data (RAG) and actions in your business tools to automate complex tasks: customer support, pre-sales, back-office. Unlike a chatbot, it acts under control with guardrails.
In 30 seconds
An AI agent is a system that combines language understanding, access to your knowledge (often via RAG) and actions in your tools, under control (permissions, validation, logs). Unlike a chatbot, it can consult your document repositories, interact with your business tools and provide sourced answers — to automate or assist tasks with measurable ROI.
Typical problems
The signs that this solution is right for you.
Confusion between chatbot and AI agent
A chatbot answers simple questions. An AI agent understands, searches, reasons and acts. If you only need a FAQ, don't build an agent.
Overwhelmed customer support
Your teams spend 60% of their time on recurring questions. An AI agent can handle 80% of these requests and escalate sensitive cases.
Inaccessible information
Your procedures, contracts and documents are in 5 different systems. Nobody finds the information in time. An AI agent connected to your sources solves this problem.
Uncontrolled security risks
Prompt injection, data leaks, false answers — a poorly designed AI agent is a risk. Guardrails are needed from the design stage.
Our approach
A proven method, in clear steps.
Use case identification
We identify high-volume tasks with repetitive logic: customer support, pre-sales, back-office, compliance. Key criterion: the gain in hours/week.
RAG + tools architecture
We connect the agent to your data sources (documents, databases, APIs) via a RAG architecture. We define the actions it can perform and the guardrails.
Controls and human validation
We define when the agent acts alone and when it escalates to a human. Systematic logging, role-based access rights, validation on sensitive cases.
Testing and progressive deployment
We test on a limited scope, we measure quality (accuracy, hallucinations, response time), then we deploy progressively.
Monitoring and continuous improvement
In production, we monitor quality, costs and usage. We continuously adjust prompts, sources and guardrails.
What you get
- Functional AI agent connected to your data
- Documented technical architecture (RAG, tools, controls)
- Validation and escalation policy
- Monitoring dashboard (quality, costs, usage)
- User guide for teams
- Continuous improvement plan
AI agent vs chatbot vs classic automation
A chatbot answers predefined questions. Classic automation (RPA) executes rigid sequences of actions. An AI agent combines the two with intelligence: it understands freely phrased requests, looks up the relevant information in your sources, and acts in your tools — all under supervision.
When to deploy an AI agent (and when not to)
An AI agent is relevant when: (1) the volume of requests is high, (2) the answers require consulting internal sources, and (3) the processing logic is repeatable. If your need is a simple FAQ, a chatbot is enough. If your processes change every week, classic automation is better suited.
Our agent projects always start with a narrow scope — one type of request, one data source — then expand based on measured results.
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