AI agent
An AI agent is a program built on a language model that carries out a task end to end: it reads data, decides on a course of action, and triggers steps through tools (APIs, business software, databases) without waiting for approval at every step. It differs from a standard chatbot, which only answers a question inside a conversation.
Updated on July 10, 2026 · Bertrand Dumast
AI Agent vs. Chatbot: The Real Difference
A chatbot answers. It receives a question, produces text, and stops there: the user still has to go find the information elsewhere, verify it, and act on it. An AI agent goes further. It receives a goal, breaks the task into steps, calls tools (read a file, query a database, send an email, update a product listing), and keeps working until the task is done. The underlying technology is the same language model as a chatbot, but wired to tools and a decision loop instead of a plain conversation window.
Use Cases for Small and Mid-Sized Businesses
- Customer support: qualify an incoming request, look up the answer in the knowledge base, draft a reply, and hand off to a human when the case falls outside the approved scope.
- Back office: match supplier invoices against purchase orders as part of a broader business process automation effort, flag discrepancies, and prepare the accounting entry for approval.
- Product catalog: read a supplier data sheet, generate a description that fits the template, and push it to the site once approved.
- Reporting: pull data from multiple sources, summarize it, and populate a dashboard without manual re-entry.
How to Scope an AI Agent Without Losing Control
An AI agent is not something you set loose. Every action it triggers (sending an email, changing a record, approving an order) needs to run inside a scope defined ahead of time: which tools it can call, which actions require human sign off, which volume limits it can't exceed. A well scoped project always starts small: one specific workflow with a human checkpoint on high stakes decisions, then a gradual expansion once reliability is proven. That's the approach behind our Business Automation and AI offer, starting from one limited workflow rather than an autonomous system across the whole operation.
- Define the action scope before development: which tools, which data, which limits.
- Build in a human approval step for irreversible or financially significant actions.
- Log every decision the agent makes so it can be audited and corrected.
Does an AI agent cost more than a standard chatbot?
Usually yes, because it requires defining the action scope, connecting business tools, and setting up supervision guardrails. A chatbot that only answers questions deploys faster. An AI agent makes sense when the task spans multiple steps and multiple systems, not a single question and answer.
Does an AI agent replace an entire team?
No, that's not the goal. An AI agent takes on the repetitive, well defined tasks inside a workflow, with a human checkpoint on high stakes decisions. The team keeps control over ambiguous cases and quality control, which limits the risk of an undetected error.
What's the main risk to watch with an AI agent?
The main risk is letting it act across too broad a scope without supervision, for example by changing a sensitive record or sending an unapproved message. You manage it by limiting which tools it can access, logging every action, and requiring human approval on irreversible steps.
Related terms.
LLM (large language model)
An LLM (large language model) is an artificial intelligence system trained on vast amounts of text to understand, summarize, and generate natural language.
Learn moreRAG (Retrieval-Augmented Generation)
Retrieval-Augmented Generation (RAG) is a method that connects a language model to an external knowledge base: before answering, the model retrieves the relevant passages from your documents, then writes its response grounded in that retrieved content.
Learn moreBusiness process automation
Business process automation is the practice of handing repetitive tasks that move between people or applications, such as data re-entry, follow-up emails, spreadsheet updates, and file transfers, over to software tools instead of staff.
Learn moreA project where AI agent comes into play?
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