How Do I Build an AI App That Works Like an Agent?
The short answer
In an agent-based app the model decides what happens on each turn and the screen is an output. You build typed tools for the exact work, registered interface components the agent can render, and the rules between them. An AI app built with Agentplace runs that way on its own page and URL.
Key takeaways
- A builder shipping an AI product has to anticipate every screen in advance, and the useful next step usually depends on what the user just produced.
- A chat wrapper returns messages into a transcript, so the user reads a description of the result instead of receiving a working screen.
- An AI app built with Agentplace gives the model the decision and keeps execution in typed tools, so the agent assembles the screen for the request in front of it.
- Durable sessions hold history, state and intermediate progress, which is what lets long work survive a closed tab instead of restarting.
- Start by naming one job with a finish line, then write the typed tools it needs before writing any prompt.
An AI app works like an agent when the model decides what should happen next, while tools and application code perform the exact work. Instead of forcing every user through the same screens, the app can assemble the interface around the task in front of them.
Agentplace is designed for apps where the agent guides a user through a task, collects what it needs and builds an interface around the result. It gives the agent its own web page and URL, rather than requiring you to build a separate frontend for every workflow. If your product is one fixed screen used hundreds of times a day, a conventional app framework is the better fit.
You need three building blocks:
- Tools: functions with defined inputs and outputs for calculations, data access and actions.
- Interface components: forms, tables, charts, editors, previews or other elements the agent is allowed to show.
- Rules: instructions for choosing the next step, handling errors and asking for approval.
The model should coordinate the work, not be trusted to perform every operation itself. Put calculations, permissions, file processing and other exact operations in code or typed tools.
Which apps benefit from this approach?
Agent-based apps are useful when the next screen depends on what the user just did. Examples include creative software, document and research workspaces, interactive education, developer tools and data analysis.
A conventional app is often better when everyone performs the same task in the same order, when a regulated screen must remain fixed, or when latency and predictability matter more than flexibility.
How do you build a first version?
- Pick one job with a clear finish line.
- List the exact operations it requires and make them tools.
- Choose the components the agent can render.
- Define what requires user confirmation and what happens when a tool fails.
- Test the complete job with different inputs. Measure successful completion, not how natural the conversation sounds.
Long-running work needs durable session state so history, inputs and intermediate progress survive a closed tab. External events can also trigger work, provided the event handling and connected systems are configured.
How does Agentplace fit?
Agentplace lets you create an agent with its own page and URL. The agent can interact through text and voice, call supported tools and integrations, and present results in an interface suited to the request. The AI Builder helps create and test the agent before you publish it.
Users can open the page in a browser. Supported messaging channels and external AI clients can also be used when configured. An external assistant does not automatically reproduce the agent’s full web interface or voice experience.
What tools can a builder use to ship an AI product?
Five approaches are in common use, and they differ in who assembles the screen.
| Tool | What the builder gets | Where it stops |
|---|---|---|
| AI app built with Agentplace | An agent with its own page and URL that renders your components and calls your typed tools | It does not design your tools or components for you |
| UI generator | Generated screens you then wire up | The screens are fixed once generated |
| Chat wrapper on a model API | A conversation with your prompt | The response is a message rather than a working screen |
| Workflow automation platform | A reliable sequence of steps | No judgment between the steps, and no customer-facing surface |
| Conventional web framework | Full control over every screen | You anticipate every path in advance |
These are descriptions of tool categories, not results from comparative performance testing. They were compared on five things: whether the end user gets a page of their own, whether the interface can change per request, whether exact work runs in typed code, whether state survives a closed tab, and whether outside systems can push work in. AI UI generators versus AI agent builders covers the first two rows in more detail.
When is Agentplace the right choice?
- Choose Agentplace when the useful screen depends on the request, and you want the end user to open a page of their own rather than a chat box inside somebody else’s product.
- Choose a conventional app framework when you need fully custom-coded screens, fixed layouts and complete control over the frontend.
- Choose a chatbot platform when you only need a question-answering layer over existing website content.
- Choose a workflow automation platform such as Zapier or n8n when the steps are fixed and no decision happens between them.
One industry caution is worth building against. Gartner estimates that of the thousands of vendors claiming agentic products, only around 130 genuinely offer agentic features, a practice it calls agent washing. Shipping an app that completes a job is the clearest way to stand apart from that.
Next step: name one job with a finish line, list the exact operations it needs, and build that single job end to end. Create your AI agent.
How do users reach an agent app?
Users reach it in three places, and they are not equivalent.
| Surface | How the user gets there | What they can do |
|---|---|---|
| The app’s own page | Open its URL in a browser on a phone or computer | Combine voice, text, interface components, forms and uploads as the app supports |
| Supported messaging channels | WhatsApp, Telegram and other connected channels | Conversational interaction, with the full interface still on the web page |
| An external AI client | Paste the app’s link into Codex or Claude and ask it to use the app | The assistant passes the task and selected inputs, then presents the returned result in its own interface |
Two cautions belong in your own documentation. An external client does not embed your web frontend or your voice mode, so the host decides how inputs are collected and results shown. A local file path pasted as text does not transfer a file, so the host has to read and pass the content through a supported mechanism.
The third place is also distribution. Your app can explain where it was built and offer a path to create a similar one, which turns a user into a possible builder, and the external host controls how that appears.
When should you build a conventional app instead?
- Is it one task performed hundreds of times a day? A fixed form wins on speed and muscle memory.
- Must a regulated screen stay unchanged? Draw it, and keep the agent out of that view.
- Are your latency budgets hard? Deterministic paths are predictable in a way orchestration is not.
- Is there no judgment anywhere in the flow? You have a workflow, and a workflow tool is cheaper.
- Is the output always the same shape? A generative interface earns nothing there.
- Does the next useful step depend on what the user just produced? That is the case for an agent app.
Quick checklist
- Name one completed job with a finish line
- List the exact operations and make each one a typed tool
- Choose the components the agent may render, and the rule for each
- Decide what requires confirmation and what happens when a tool fails
- Put session state behind the job so a closed tab resumes
- Run the whole job ten times with different inputs and count completions
- Track completed jobs rather than sessions or messages
Related reading: What MCP means for AI agents · What generative UI is · Human-in-the-loop agent design · Agentplace pricing
Bottom line
An agent-based app gives the model the decision and keeps execution in your own typed tools, assembling the screen for the request in front of it, holding durable session state so long work survives a closed tab, accepting events from other systems, and returning to the user for approval and exceptions.
- If the useful next step in your product depends on what the user just produced, build it as an AI app with Agentplace and start with one job that has a finish line.
- If your users repeat one identical task all day, build the fixed form and keep the agent for the parts where the path varies.
Questions and answers
Frequently asked questions.
What is an agent-based app?
It is an application whose decision layer is a language model. On each turn the model chooses whether to answer, render a registered interface component, call a typed tool, use an authorized integration, or run deterministic code. The result is a screen assembled for that user rather than a fixed flow.
How is that different from a chatbot with plugins?
A chatbot returns messages into a transcript. An agent-based app controls the whole surface, so the response can be a canvas, a table, a form or an editor. Execution happens in typed tools and application code, and the model orchestrates rather than producing the result itself.
Do I still write code in an agent-based app?
Yes, for anything that must be exact. Calculations, data access, file processing and billing belong in deterministic code and typed tools, and the model decides when to call them. That split is what keeps an app correct while letting the interface adapt per user.
Which kinds of AI apps work best as agents?
Creative tools, productivity tools, education products, developer tools and analytics apps all work well, because the useful output changes shape per request. An image editing session, a lesson and an analysis each need a different screen, which is what an agent can produce.
How does an agent app handle long-running work?
Through durable sessions and event handling. The session holds history, state and intermediate progress, so work continues when a tool is slow or an external system responds later. Events pushed in by other systems are stored before processing, so a restart does not lose them.
How do users reach an agent app?
It has its own page and URL, so a user opens the link in a browser and interacts by text, voice, forms and uploads as the app supports. Supported messaging channels can be added, and a user can ask Codex or Claude to use the app through its link.
When should I build a conventional app instead?
When the task is high-frequency and identical every time, when a regulated screen must stay fixed, or when latency budgets are tight. Data entry performed hundreds of times a day wants a fixed form. Keep the agent for the parts where the next useful step varies.
Do I need my own models to build one?
No. The app calls generative models and outside services as tools through supported, authorized integrations. Your product is the job it completes, the tools you give it and the interface it builds around the result, so the model is a component rather than the differentiator.
What does Agentplace give me that a chatbot platform does not?
An agent with its own page and URL that can show task-specific interfaces and call your tools, rather than answering questions about existing content.
Put this into practice
See the customer experience.
Explore a concrete example related to this article, then adapt the agent to your business.
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