What is an AI agent?

AI agent: definition and scope

An AI agent combines a language model with the ability to act. It receives a goal, such as “Process the incoming order requests,” and decides for itself which steps are needed. To do so it calls tools, reads the results and then decides whether to continue, ask a question or hand the task over.

A chatbot answers questions; a classic automation follows fixed steps. The agent sits in between: it can react to unexpected situations but is less predictable as a result. Agents therefore need clear limits, permissions and a point where people can intervene. The higher the risk of an action, the narrower the agent’s room for maneuver should be.

How an AI agent works

At its core, an agent works in a loop: it takes in the situation, plans the next step, carries it out through a tool and evaluates the result. The tools are usually functions or interfaces, such as “check stock level,” “create appointment” or “draft email.” Techniques such as function calling are behind this.

A memory holds intermediate states and earlier cases. For larger tasks, several specialized agents work together, for example one for research and one for review. The more steps an agent carries out on its own, the more important logs become, so that it stays traceable what it has done.

Real-world example: order requests at a wholesaler

A wholesaler receives order requests by email. The agent reads the message, matches items, queries availability and customer price via the interface to the inventory system and creates a quote draft. If something is unclear, such as a missing quantity, it drafts a follow-up question to the customer.

Nothing is sent automatically: the draft goes to the inside sales team for approval. Only after several weeks with a good hit rate was the agent allowed to confirm simple standard cases itself. This gradual expansion of authority has proven to be a good approach. Trust grows in the team, and mistakes show up while their consequences are still small.

Opportuni­ties, limits and security

Agents can take on multi-step tasks that cannot be squeezed into rigid rules. However, they make mistakes, can cause costs through many model calls and are vulnerable to prompt injection, meaning hidden instructions in emails or documents. Minimal rights, approval steps and logs are therefore mandatory.

Nano Concepts develops AI agents for clearly defined tasks and connects them to your existing systems. This includes choosing the tools, clean interfaces, a permissions concept and tests with real cases before the agent goes live. If needed, a person stays in the process permanently.

• Define a narrowly scoped task rather than an “all-rounder” agent
• Limit access rights to the necessary minimum
• Write actions and payments only after human approval
• Log every step and monitor costs per case
• Start with read-only tasks and expand authority step by step