An AI agent is a large language model (LLM) operating inside a loop that allows it to observe the current state, decide what action to take, call an external tool, observe the result, and repeat until a task is complete. Unlike a chatbot that produces a single response, an agent can complete multi-step tasks by interacting with real systems.

How does an AI agent differ from a chatbot?

A chatbot is a stateless question-answering system — one input produces one output, with no ability to take actions or remember across sessions. An AI agent has tools, memory, and a reasoning loop, enabling it to execute sequences of actions across multiple systems and sessions until a goal is achieved.

What tools can an AI agent use?

Agents can call any external system exposed as a function with defined inputs and outputs. Common tools include web search, database queries, code execution, file reading and writing, calendar access, email, and external API calls. The breadth of tool access determines how much the agent can accomplish — and how much damage it can do if it acts incorrectly.

What are the main risks of deploying AI agents in a business?

The key risks are goal misalignment — an agent that misunderstands its objective at step one executes a long chain of confident but wrong actions — and adversarial manipulation, where an agent with broad tool access encounters content designed to redirect its behaviour. High-consequence, irreversible actions should always include human-in-the-loop checkpoints before the agent proceeds.

Generative Ai

Jan 01, 2025

What Are AI Agents? A Guide for Business Leaders

AI agents are not chatbots. They are LLMs that take actions, use tools, and complete multi-step tasks autonomously. Here is what that means for leaders.

“AI agent” is the most overloaded term in the current technology landscape. Walk into any enterprise technology conversation in 2025 and the word appears at least a dozen times, attached to products that are, by any technical measure, doing very different things. A customer service chatbot is called an agent. A workflow automation tool is called an agent. An LLM that browses the internet and writes code on your behalf is called an agent. These are not the same thing — and conflating them leads directly to misallocated investment and misplaced expectations.

The term means something specific. Understanding what it means is the prerequisite for knowing when agents are the right tool, when they are overkill, and when they are the wrong tool entirely.

What a Chatbot Is vs. What an Agent Is

A chatbot is, at its core, a stateless question-answering system. One input arrives; one output is produced. The system has no memory of what came before the current conversation window, no ability to take actions in the world, and no capacity to observe the results of its outputs. It produces text. That text may be useful. That is the extent of what it does.

An agent is architecturally different. At its centre is an LLM — the same kind of model that powers a chatbot — but that model operates inside a loop rather than producing a single response. The loop is the critical structure: observe the current state, decide what to do next, take an action using a tool, observe the result of that action, decide what to do next. This continues until the task is complete.

The pattern has a name in the research literature: ReAct, which stands for Reasoning and Acting. The agent reasons about what to do, acts using a tool, observes the result, and reasons again. What makes this qualitatively different from a chatbot is not the underlying model — it is the loop, the tools, and the memory that persists across iterations of that loop.

What “Tools” Means

When we say an agent has access to tools, we mean it can call external systems and observe the results. Tools are functions with defined inputs and outputs: a web search tool receives a query and returns a list of results; a database query tool receives a SQL statement and returns rows; a code execution tool receives a Python script and returns the output; an email tool receives a recipient, subject, and body and sends the message.

The agent decides which tool to call, with what inputs, based on what it has observed so far. It receives the tool’s output, incorporates it into its reasoning, and decides on the next action. This is fundamentally different from an LLM that generates text, because the agent is not just producing language — it is interacting with real systems and producing real effects.

The range of tools an agent can have is broad: APIs, web search, code execution, file reading and writing, database queries, calendar access, external service calls. The breadth of tool access is a direct measure of how much the agent can do — and how much damage it can do if it does the wrong thing. We will come to that.

Memory: What Makes Multi-Step Tasks Possible

A basic LLM has no memory beyond the current context window. If you ask it a question, it answers from its training data and whatever you have included in the prompt. If the conversation gets long enough to overflow the context window, earlier content falls away.

Agents address this through three forms of memory. Short-term memory is the conversation context — the record of what has happened in the current session, including all tool calls and their results, maintained within the context window. Long-term memory is typically a vector store: past interactions, learned facts about the user or task, and domain knowledge that the agent can retrieve as needed using semantic search. External memory refers to structured databases and knowledge stores the agent can query directly as a tool.

Memory is what makes agents capable of completing complex, multi-step tasks. A task that requires ten sequential steps, each informed by the results of the previous, cannot be completed in a single LLM call. The agent’s loop, combined with its memory across iterations, is what enables it to hold the thread of a task across many steps and, where necessary, across multiple sessions.

What Agents Are Good For

The workflows that benefit most from agents share a common profile. They are multi-step: the path from input to output requires more than one distinct operation. They require decision-making about which step to take next, based on the results of the previous step. They involve using multiple systems or data sources in sequence. And they are too variable in structure to script reliably, but too repetitive for a skilled human to spend significant time on.

Research synthesis tasks fit this profile well: the agent searches for relevant sources, reads and extracts key points, identifies gaps, cross-references across sources, and produces a structured summary — all in a loop, with each step informing the next. Document processing at scale fits this profile: the agent reads a document, identifies its type and structure, extracts the relevant fields using the appropriate method for that structure, validates the results, and routes the output. Workflow automation that requires judgment at decision points fits this profile: the agent reads an incoming request, classifies it, routes it to the appropriate process, and escalates edge cases to a human.

What Agents Are Not Ready For

The failure modes of agents are not minor. An agent that misunderstands its goal at step one will execute a long chain of confident, internally consistent actions that collectively accomplish the wrong thing. An agent with broad tool access that encounters adversarial content — a web page designed to redirect its behaviour — may take actions its operators never intended.

Agents are not ready for fully autonomous high-stakes decisions. A credit decision, a medical diagnosis, a legal ruling — these require not just correct outputs but auditable reasoning, accountability, and the kind of verified logical chain that current LLMs cannot reliably provide. Agents are not ready for tasks where a single wrong tool call has irreversible consequences without a human checkpoint before that call is made. They are not ready for domains where the cost of a wrong action is catastrophic and correction is impossible.

The principle for production agents is: the higher the consequence of an error, the more tightly scoped the agent should be, and the more explicit the human checkpoints should be. An agent that sends a draft email for human approval before sending is fundamentally safer than an agent that sends email autonomously.

Agents vs. Traditional Automation

RPA — robotic process automation — and traditional scripted automation work reliably for predictable, well-structured processes. If an invoice always arrives in a specific format, with fields in specific positions, a script can extract those fields with high reliability and low cost. The script does not require judgment.

Agents add value when the process requires judgment that cannot be scripted. Invoices arrive in dozens of formats. Regulatory documents use variable structures. Customer requests are phrased in ways no deterministic parser anticipates. The LLM at the centre of the agent handles the variability — it reads the invoice in whatever format it arrives and extracts the relevant fields — while the tool calls handle the actions: writing the result to a system, routing the exception, sending the notification.

The distinction matters for investment decisions. RPA is cheaper to build and more reliable in controlled conditions. Agents are more expensive to build safely and more capable in variable conditions. The right choice depends on how variable the inputs actually are and what the cost of failures is. Many processes that organisations reach for agents to solve are actually well-suited to RPA or simpler automation. Not everything needs a loop and an LLM.

What You Are Getting Into

Agents are a genuine step change in what software can do autonomously. A well-built agent can complete a research task that would take a human analyst half a day, run overnight, and deliver a structured output ready for review in the morning. That is real value.

It is also meaningfully more complex to build and operate safely than a chatbot. The orchestration loop, the tool definitions, the memory architecture, the error handling, the security posture, the observability infrastructure, the human-in-the-loop checkpoints — these are all additional surface area that requires engineering discipline to get right.

The organisations that are doing well with agents are not the ones that moved fastest. They are the ones that started with the smallest possible well-defined task, built the infrastructure correctly, and expanded scope once they had confidence in the foundation. That is the approach we recommend.

Building Your First AI Agent: A Practical Guide — The technical follow-on to this overview, covering architecture, tool design, and human-in-the-loop patterns for your first production agent.
Agentic AI Security: Prompt Injection and Containment — The risk side of agentic AI that every business leader needs to understand before approving a deployment.
Nematix Generative AI Services — How Nematix helps organisations design and deploy agentic AI systems safely in enterprise environments.

Learn how Nematix’s Innovation Engineering services help businesses build production-ready AI systems.