What is an AI Agent?
An AI agent is a system built around a large language model (LLM) that can execute multi-step tasks autonomously. Unlike a chatbot that only responds to questions, an agent can call external tools (APIs, databases, email), remember context across steps, and make decisions about what to do next to achieve a stated goal. You give the agent an objective; it determines the steps and executes them.
An AI agent is a system that uses a language model to independently execute tasks and make decisions. It does more than answer questions: it takes actions on your behalf.
What Makes Something an AI Agent
The word “agent” implies action and autonomy. An AI agent is a system designed to pursue a goal, not just answer a question. It receives an objective, plans the steps required, executes those steps using tools, and adjusts based on what it observes. The key distinction from a chatbot is that an agent acts: it calls APIs, queries databases, sends messages, reads documents, and makes decisions about what to do next without a human steering each step. A chatbot is a conversational interface; an AI agent is a system that performs work.
How an AI Agent Works
The core components of an AI agent are: a language model (the reasoning engine), tools (functions the model can call to interact with the world), memory (context about the current task and sometimes past tasks), and a feedback loop (the results of each tool call are fed back to the model, which then decides the next step). In practice: the model receives the task, reasons about what to do first, calls a tool, receives the result, reasons about what to do next, and repeats until the task is complete or it needs human input.
A Practical Example
A simple business use case: an AI agent monitors an email inbox, reads each incoming email, queries a database to look up the relevant customer record, decides whether the issue can be resolved automatically or needs human review, drafts a response or creates a support ticket accordingly, and sends or saves the result. No human is involved in the loop unless the agent decides it cannot handle the email on its own. This is buildable today with tools like n8n and the OpenAI or Anthropic API.
Risks and Pitfalls
The main risks are irreversible actions and unpredictable behaviour. An agent that can send emails and delete records can do damage quickly if it misinterprets its instructions. Guard rails matter: restrict what tools the agent has access to, require human confirmation for irreversible actions, set limits on the number of steps per task, and test thoroughly with sandbox data. Agents that run in loops without a stopping condition can also accumulate significant API costs in a short time. See risks of AI in business processes for a broader treatment of these concerns.
Practical Business Applications
The most reliable business applications for AI agents today are: email triage and routing, document review and summarisation, data extraction from unstructured sources (PDFs, emails, forms), scheduled reporting with narrative summaries, and internal knowledge base search. For use cases where an agent needs to understand and search large document collections, Retrieval-Augmented Generation (RAG) is often part of the solution.
Our tip: Start with an agent that has read-only tools (search, query, summarise) before giving it write access (send, delete, update). Read-only agents are easier to test, cheaper to run, and far safer to put into production.
Frequently asked questions
What is the difference between an AI agent and a chatbot?
A chatbot responds to questions: you send a message, it generates a response, the interaction ends. An AI agent acts: it receives a goal, plans steps to achieve it, executes those steps using tools (APIs, databases, browsers), observes the results, and decides what to do next. A chatbot is a conversational interface; an AI agent is a system that performs work. Many products marketed as 'AI agents' are actually sophisticated chatbots with some tool access, so it is worth asking: does it plan, act, and adapt, or does it only respond?
What tools can an AI agent use?
An AI agent can use any tool that has been made available to it by its system design. Common tool types include: web search (to look up current information), database queries (to read or write data), API calls (to interact with external services like email, calendars, or CRMs), file read/write operations, and code execution. The agent's language model decides which tool to call based on the current goal and the results of previous steps. Tools are the mechanism by which the agent affects the real world.
What is an AI agent's 'memory'?
AI agents typically have three types of memory. Working memory is the conversation context within a single session: the agent remembers everything that happened in the current task. External memory is a database or vector store the agent can query to retrieve information beyond its context window (this is related to retrieval-augmented generation). Long-term memory is information persisted across sessions, such as user preferences or previous task outcomes stored in a database the agent can access. Without memory management, agents are limited to tasks that fit within a single context window.
What are the main risks of using AI agents in business processes?
The main risks are: incorrect actions that are difficult to reverse (sending an email you did not intend, deleting data, making an API call that triggers a downstream effect); cost overruns when an agent makes many API calls in a loop; and unpredictable behaviour when the task is ambiguous or the tools return unexpected results. Mitigation involves: keeping humans in the loop for irreversible actions, setting hard limits on the number of steps an agent can take, and testing extensively with sandbox data before running agents on production systems.