--- title: "What Is an AI Agent? How 'Agentic' AI Differs From a Chatbot" description: "'AI agents' are the phrase of the moment in tech, pitched as software that doesn't just answer questions but gets things done. The distinction from a chatbot is real and matters for investors — but so do the reliability problems that come with letting software act on its own." category: "Tech" category_url: https://boursel.com/category/tech author: "Hannah Blackwood" published: 2026-07-03T21:45:00.000Z updated: 2026-07-03T21:45:00.000Z canonical: https://boursel.com/article/what-is-an-ai-agent-how-agentic-ai-differs-from-a-chatbot tags: ["ai-agents", "agentic-ai", "artificial-intelligence", "technology", "explainer"] --- # What Is an AI Agent? How 'Agentic' AI Differs From a Chatbot 'AI agents' are the phrase of the moment in tech, pitched as software that doesn't just answer questions but gets things done. The distinction from a chatbot is real and matters for investors — but so do the reliability problems that come with letting software act on its own. Listen to any tech company this year and you'll hear the same word: **agents**. AI is moving, they say, from tools that talk to tools that *act*. Cutting through the marketing, there's a genuine shift underneath — and understanding it explains where a lot of investment is now heading. ## Chatbot vs. agent Start with what most people have used. A **chatbot** built on a large language model (LLM) — software trained on vast text to predict and generate language, [as IBM describes](https://www.ibm.com/topics/large-language-models) — takes your prompt and returns an answer. It's conversational and reactive: you ask, it responds, and the interaction ends there. It produces **words**. An **AI agent** is designed to produce **actions**. Given a goal, an agent can break it into steps, use external **tools** (search the web, run code, call software, query a database), observe the results, and adjust — looping through "think, act, check" until the task is done, with limited human intervention. The chatbot tells you how to book a trip; the agent, in principle, books it. ## What makes something "agentic" A few capabilities separate an agent from a plain chatbot: - **Goals, not just replies.** It works toward an outcome across multiple steps rather than answering one question. - **Tool use.** It can reach beyond text to *do* things — execute code, send requests, operate other software. - **Memory and planning.** It keeps track of progress, plans a sequence of actions, and revises when something fails. - **Autonomy.** It can take some steps on its own, rather than waiting for a human prompt at every turn. The engine underneath is often the same kind of LLM that powers chatbots; "agentic" describes the **scaffolding** around it that lets the model plan and act, [part of the broader field of artificial intelligence](https://www.investopedia.com/terms/a/artificial-intelligence-ai.asp). ## Why the hype — and the money The commercial excitement is easy to see. A chatbot can draft an email; an agent that can reliably **complete multi-step work** — reconciling invoices, writing and fixing software, handling a customer request end to end — automates labor, not just conversation. That is a far larger prize, which is why so much of the current AI investment wave is aimed at agents rather than chat. ## The catch: reliability Here is the part the marketing skips. Autonomy multiplies the cost of error. An LLM that occasionally "hallucinates" a wrong fact is a nuisance in a chat; an **agent that takes a wrong action** — deletes the wrong file, sends the wrong payment, ships broken code — causes real damage. The more steps an agent takes, the more chances for a small mistake to compound. Today's agents still need **guardrails and human oversight**, and deciding how much freedom to give them — especially in sensitive systems — is the central practical question. An agent is only as valuable as it is trustworthy. ## Why it matters For **investors**, the shift from chatbots to agents is where much of AI's promised economic value now sits — automating tasks, not just producing text — so it's worth understanding what companies mean when they use the word. For **businesses**, agents offer to take on real workflows, but demand new caution about oversight, security and accountability. And for **everyone**, it marks a change in what we're being asked to trust software to do: not just to inform us, but to act for us. Boursel gives no investment advice; the useful frame is simple — a chatbot gives you an answer, an agent takes an action, and the whole opportunity, and risk, lives in that difference. ## Sources - [Artificial Intelligence (AI)](https://www.investopedia.com/terms/a/artificial-intelligence-ai.asp) - [What are large language models (LLMs)?](https://www.ibm.com/topics/large-language-models)