Is an AI agent just a chatbot with plugins?
No. An AI agent decides which steps to take and in what order. It checks each result and recovers when a step fails. A chatbot with plugins still runs the path a developer laid out.
AI agents vs chatbots: the difference
A chatbot answers questions; an AI agent completes work. Both are built on the same models, but they cost different amounts. Picking the more powerful one when the simpler one would do is an expensive mistake.
- Short answer
- A chatbot answers questions; an AI agent completes work
- Cost
- They differ, and the wrong choice is expensive either way
- Middle ground
- Agentic chat assistants, which are both
The short answer
A chatbot responds to a message. An AI agent takes an objective and works until it is done.
After a chatbot conversation, the user knows something. After an agent runs, something has changed.
A chatbot is an interface; an agent gets work done. Many useful systems are both at once - the agentic chat assistant pattern.
AI agents vs chatbots, side by side
The differences show up in eight dimensions, from tool access to cost.
| Dimension | Chatbot | AI agent |
|---|---|---|
| What it does | Answers questions and holds a conversation. | Completes a task and leaves your systems changed. |
| Multi-step tasks | One turn at a time; multi-step work is scripted in advance. | Plans the steps at run time; the count is not fixed. |
| Tool and API access | Usually none, or a few read-only lookups. | Calls functions, APIs, databases and internal applications. |
| Ability to act on systems | Tells the user what to do next. | Does it: creates the record, produces the quote, routes the request. |
| Error recovery | A wrong answer is returned as the answer; the fallback is a human. | Detects a failed call or a broken rule, then retries or escalates. |
| Evaluation | Judged on answer quality, deflection and satisfaction. | Judged on task completion against explicit criteria, with failures fed back. |
| Build and run cost | Lower. Less integration and orchestration to maintain. | Higher. Integration, permissions, orchestration and evaluation are part of the build. |
| When it is the right choice | Answering questions from known material at high volume. | Finishing work that spans several systems, with a checkable definition of done. |
An agent costs more to build and run. It earns that back only when the finished work is worth more than an answer.
When a chatbot is the right answer
Nothing has to change in your systems for a chatbot to do its job.
Prefer a chatbot when:
- the answers already live in documentation, policies, or a knowledge base;
- the useful outcome is the person understanding something;
- your systems have no API, or integration work cannot start yet;
- each interaction is short and self-contained;
- you want to learn what users actually ask before a bigger build.
The retrieval layer under a chatbot is the same vector database and retrieval work an agent would need later.
When you need an AI agent
You need an agent when the outcome is work completed across systems.
The signals:
- the task takes several steps, and each depends on the last;
- the work touches more than one system - a database, a payments provider;
- a correct result is checkable, so the output can be scored;
- the process runs often enough to pay for the build;
- a person does it by hand today, following a written procedure.
Both Fig AI Systems products fit that description. Build-a-Dress classifies dress complexity and weight to produce a quote. Report Grader assesses school reports against national and international standards, with human-in-the-loop review. Both are written up on the product pages. The practice behind them is custom AI agent development.
The middle ground: agentic chat assistants
An agentic chat assistant is a chat interface with agent behavior behind it.
This is the right shape when the audience already expects to type at a box. Build-a-Dress works this way: shoppers design a dress conversationally with image generation, and the same product routes the order to manufacturers.
Boundaries decide it: which requests get an answer, which trigger real actions, and where a person confirms. Drawing those boundaries is the work of agentic chat assistant development.
How to decide, in four questions
Each answer points to one option, and they rarely disagree.
- What does success leave behind? An informed person means a chatbot. A changed system means an agent.
- Does it need to touch other software? If finishing the task means writing to another system, a chatbot cannot finish it.
- Can you grade the output? If nobody can state what correct looks like, fix that first. That definition drives closed-loop evaluation.
- How often does it run? Low volume favors the simpler build. High volume pays for the agent.
For the concepts, start with what agentic AI is. For cost, timelines and governance, see the AI agent development FAQ.
Frequently asked questions
Can one system be both a chatbot and an AI agent?
Yes, and it is the most common shape. An agentic chat assistant keeps the familiar chat front end and puts agent behavior behind it. Fig AI Systems builds this pattern.
Do AI agents replace the chatbot we already run?
Usually not. If an existing chatbot handles routine questions well, leave it in place. Route only the requests that need action into an agent.
How do we decide which one our workflow needs?
Ask what success leaves behind. An informed person points to a chatbot. A record created or a quote produced points to an agent. If both, build a conversational front end over an agent.