An AI agent understands plain-language instructions, uses your systems and carries out multi-step tasks on its own. Where a decision matters, it asks a human to approve.
An AI agent is a program built on a language model that understands plain-language instructions, plans the next steps and carries them out in your systems: looking up information, filling in forms and preparing documents. Unlike classic automation, it can handle unstructured data such as emails, contracts and PDF files.
The agent gets an instruction from an employee or reacts to an event, such as a new document.
It reads documents, customer history and company procedures from the knowledge base.
It breaks the task into steps and picks the tools it needs.
It looks up data, fills in forms, drafts letters and updates systems.
The result goes to a person for approval or runs automatically, depending on the rules you set.
Every day the agent reviews new requests, compares requirements with your offer, rates the chances and drafts a reply. The salesperson gets a ready list with recommendations and decides which ones to pursue.
| Aspect | Rule-based automation | AI agent |
|---|---|---|
| Input | Structured: forms, tables | Unstructured: emails, PDFs, conversations |
| Decisions | Fixed if-then rules | Weighs context and picks next steps |
| Example | Moving an order into a system | Analysing a request and drafting a quote |
| When to choose | The process is repetitive and predictable | Every case is a little different |
Experts focus on decisions, not on preparing material.
The agent keeps working at night and on weekends.
The same procedure every time, no off days.
The agent can only access the data and actions you allow.
A chatbot mainly answers questions. An AI agent completes tasks: it reads documents, takes the next steps and works in your systems, for example updating the CRM or preparing a quote. An agent can have a chat interface, but its purpose is to get work done, not just to talk.
Yes, which is why I design agents with human approval where a decision matters. The agent prepares the result and a person approves it. Every action is logged, and the agent’s permissions are kept to a minimum, so it cannot do anything you have not allowed.
I match the model to the task: OpenAI or Anthropic Claude models when top quality matters, or open models such as Llama or Mistral running on your servers when data cannot leave the company. The architecture lets you switch models without rebuilding the whole solution.
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