What makes a chat assistant agentic?
A chat assistant is agentic when the conversation causes something to happen. It plans across turns, calls tools, and produces a real artifact. An assistant that can only retrieve information is a chatbot.
Agentic Chat Assistant Development
A chatbot answers a question. An agentic chat assistant completes the work being discussed - planning across turns, calling tools, and producing something real at the end. Fig AI Systems runs one of these inside a live business of its own.
- What it is
- A conversational surface that completes work, not just answers
- Best for
- Intake, configuration and support that has to end in an action
- Proven in
- Build-a-Dress, a live business
What is an agentic chat assistant?
An agentic chat assistant is a conversational interface in front of an AI agent. It plans, calls tools, and completes the work under discussion.
What makes an assistant agentic is everything behind the chat window. It calls tools, retrieves data, and keeps state across turns.
The conversation is an input method for an agent, not the product. The general framing is in AI agents versus chatbots, and the capability model in custom AI agent development.
A chatbot answers; an agent completes work
The difference is not conversational quality. It is whether the system can act. A chatbot returns text about a task. An agentic chat assistant performs it and returns the outcome.
- Scripts versus plans. A chatbot follows a decision tree. An agentic assistant plans ahead, and revises when a step returns something unexpected.
- Text versus tool calls. A chatbot names the form to fill in. An agentic assistant calls the API and writes the record.
- Session versus process state. A chatbot forgets. An agentic assistant carries the work forward, so an order survives the conversation that created it.
- Sounding right versus being right. A fluent wrong answer is invisible without measurement. That is why closed-loop evaluation belongs in the build.
Build-a-Dress: a live agentic chat assistant
Build-a-Dress is a made-to-order fashion marketplace we built and run, with over 2,000 monthly active users. Its design assistant is an agentic chat assistant, answering to real shoppers rather than to a demo script.
A shopper describes the dress they want. The assistant generates images as the conversation goes on, so the shopper can react and change the design. Shoppers design in minutes, and a settled order routes to verified manufacturers.
Behind the conversation sit an agentic quoting workflow that classifies dress complexity and weight, plus a manufacturer verification and payout system. Stripe and Wise integration provides a global payment gateway.
More on the Build-a-Dress product page, or visit build-a-dress.com.
What an agentic chat assistant build involves
The conversation and the machinery underneath are one system, and get built as one system. A good chat surface over a weak tool layer just fails more politely.
- Define the finished result. Decide what the conversation must produce: a design, a quote, or a routed request. The assistant is designed backwards from that artifact.
- Design the tools before the prompts. Every action the assistant can take becomes an explicit tool with a narrow contract. Narrow contracts keep it inside its remit.
- Ground it in real data. Product catalogs, policies, and history are made retrievable through vector databases and retrieval, so the assistant answers from records instead of recall.
- Manage state across turns. A partly specified request has to survive interruptions, changes of mind, and a user who comes back later.
- Split the work when it grows. Conversation, generation, pricing, and fulfillment are different jobs; multi-agent orchestration keeps each one small enough to test.
- Evaluate with simulated users. Harnesses where agents imitate real users expose the turns where an assistant stalls or loops.
When an agentic chat assistant fits
Two conditions matter: the request is easier to describe than to specify, and something concrete has to happen afterwards.
It is a good fit when:
- Users would explain what they want more readily than fill in a form.
- The request has many valid shapes, so fixed dropdowns exclude options or overwhelm the user.
- Seeing an interim result changes what the user asks for next.
- A real system waits downstream to price or fulfill the request.
It is a poor fit when:
- The task is three fields long.
- The assistant would have no tools to call.
- Nobody can say what a correct outcome is, so no evaluation loop can be built.
The other six services are on the Fig AI Systems services overview.
Frequently asked questions
What does an agentic chat assistant need access to?
The tools that do the work, and the data it has to be accurate about. It needs to call the systems that carry an action out. It also needs retrieval over the catalogs and records the conversation depends on.
How do you test an agentic chat assistant?
With simulated conversations, because real users do not follow a fixed script. Fig AI Systems builds harnesses where agents test agents and imitate real user behavior. Those harnesses surface the turns where the assistant fails or stalls.