An AI agent differs from ordinary AI models in three ways:

1. Autonomy: The agent acts without requiring human approval for every step
2. Goal-directedness: The agent works towards a goal, not just a single answer
3. Tool use: The agent can call external tools — databases, APIs, browsers, files

An LLM like ChatGPT answers a question. An AI agent using the same model can answer that question and then — based on that answer — send an email, schedule a meeting and update a CRM record.

The core of an AI agent is a so-called reasoning loop:

1. The model receives a task
2. It determines which steps are needed (chain-of-thought)
3. It selects a tool and executes an action
4. It processes the result of that action
5. It repeats this until the goal is achieved

This pattern is also called ReAct (Reasoning + Acting) or Plan-and-Execute. Modern frameworks like LangChain, AutoGen and Claude's tool use API are built on this principle.

Single-agent systems solve one task with one model that can call multiple tools. Think of an agent that analyses an inbox and categorises emails.

Multi-agent systems distribute complex tasks across multiple specialised agents. An orchestrator agent directs sub-agents that each handle part of the work. This is effective for complex workflows with dependencies.

Human-in-the-loop agents ask for human confirmation at critical moments. This is essential for tasks where mistakes have major consequences, such as processing payments or publishing content.

AI agents are valuable when:

• A task requires multiple steps that react to each other
• Execution depends on external data or systems
• Volume or speed exceeds human capacity
• The task is repeatable but too variable for classic automation

They are less suitable for tasks requiring creative judgement, tasks that are highly contextual, or where compliance requires a complete audit trail of human decisions.

• Customer service: Agent analyses incoming tickets, answers standard questions and escalates complex cases
• Content production: Agent retrieves product data, writes descriptions and publishes them to the CMS
• Data enrichment: Agent looks up missing company information from multiple sources and fills in the CRM
• Reporting: Agent collects data from multiple systems, compiles a report and sends it to the right recipients

An AI agent is a powerful instrument for automating complex, multi-step processes. The value lies in the combination of reasoning, acting and adapting — not in one smart output, but in an entire workflow that runs autonomously.

Want to know which processes in your organisation are suitable for automation with AI agents? Get in touch for a free process analysis.