A chatbot answers questions. An AI agent goes further: it takes actions. It reads an email, looks up the customer in your CRM, checks stock in your ERP, drafts a reply and creates a task — chaining several steps across your tools to reach a goal.
What agents do well today
Agents are most reliable on repetitive processes with clear rules and digital inputs: qualifying incoming leads and routing them to the right salesperson, processing supplier invoices and matching them to orders, answering first-level support requests and escalating the rest, preparing weekly reports from several data sources, or following up on unpaid invoices.
Where to be careful
Agents can make mistakes, and a mistake that sends an email or changes a record costs more than a wrong answer in a chat. The right approach is to give each agent limited permissions, log every action, and require human approval for sensitive steps such as payments, contracts or messages to key clients. Autonomy is then increased step by step, as trust is earned.
Connecting agents to your systems
An agent is only as useful as the tools it can reach. Most of the work is integration: secure access to your CRM, ERP, email, databases and documents through APIs. Standards such as the Model Context Protocol (MCP) now make it easier to connect AI models to business tools in a controlled way.
How to start
Pick one process that is repetitive, rule-based and measurable. Map the steps, define what the agent may do alone and what needs approval, and run it alongside your team for a few weeks. If the results are good — time saved, errors avoided — let it run and move on to the next process.