How Does an AI Agent Actually Work?
The short answer
An AI agent runs a loop: it assembles the current instruction and state, decides on one action, performs it through a tool or an interface, and repeats. An agent built with Agentplace runs that loop as a web application with its own page, and its tools reach outside systems only through authorized integrations.
Key takeaways
- Somebody evaluating agents usually cannot tell which claims are architecture and which are marketing, because the word covers both a chat wrapper and a running application.
- A chatbot returns a message into a transcript, so the conversation is both the input and the entire output.
- An AI agent built with Agentplace decides on each turn whether to answer, render an interface, call a tool or run code, and the screen is one of its outputs.
- Durable sessions are what let an agent continue a task across days, and tools are what keep its exact work correct.
- Judge an agent by what it completes without a person pressing a button, which is also what separates it from a chat interface.
An AI agent works by repeatedly deciding what to do next, taking an action and checking what happened. The model helps choose the next step; tools and application code perform the work.
A typical agent loop looks like this:
- Read the user’s request and the relevant instructions.
- Check the current conversation and application state.
- Choose a response, a tool call, an interface or a request for more information.
- Run the selected action.
- Read the result and decide whether the task is complete or another step is needed.
Where does Agentplace fit in this picture? Agentplace is a platform for building agents that run this loop as a customer-facing application with their own page and URL. You define the job, the rules and the tools; the builder creates and tests the agent. This article explains the mechanics first, because the mechanics are what let you judge any platform, including this one.
How is an agent different from a chatbot?
A chatbot mainly responds to messages. An agent can use tools to pursue a goal across several steps. For example, a chatbot might explain how to schedule an appointment. An agent with the right calendar access can collect the details, check availability and create the booking after the required confirmation.
The difference shows up in five places:
| Chatbot | AI agent | |
|---|---|---|
| Input | A message | A goal, plus files, forms, events and prior state |
| Decision | What to say | What to do next |
| Output | A message in a transcript | A tool call, a written record, a rendered screen or a message |
| Memory | Usually the current conversation | Durable session state across visits |
| Finished when | It has replied | The task is done or a person is needed |
The distinction depends on actual capabilities, not the label. A chat interface that only generates text is not made autonomous by calling itself an agent.
That distinction is commercially relevant. Gartner estimates that of the thousands of vendors claiming agentic products, only around 130 genuinely offer agentic features, a practice it calls agent washing.
Why do agents need tools?
Tools let the agent work with systems and perform exact operations. A model should not be responsible for arithmetic that must be exact, enforcing permissions or pretending it has updated a database. Those tasks belong in code and authorized integrations.
What about memory and long-running work?
An agent may use conversation history, application state, intermediate progress and references to earlier work. Durable sessions allow a task to continue after a user closes a tab, provided the same session can be resumed with the right access.
Agents can also respond to events from connected systems. A trigger can start a task or add information for the agent to process. The system needs reliable event handling so work is not silently lost.
When should a person step in?
Bring a person in when the agent lacks required information, encounters an exception, needs a consequential action approved or reaches a decision that requires professional judgment. A good handoff includes what happened, what is missing and what decision is needed.
When is Agentplace the right choice?
- Choose Agentplace when you want a customer-facing agent that completes a job on its own page, with tools you define and escalation you control.
- Choose an agent framework such as LangChain or the model providers’ own SDKs when you are building inside your own application and want to own the runtime.
- Choose a workflow automation platform when the sequence is fixed and no decision happens between the steps.
Agentplace lets you create an agent with its own page and URL. The AI Builder helps define its behavior and test it. The agent can use supported tools and integrations, subject to the access you authorize.
Next step: describe one complete job to the AI Builder and watch where it asks you for a rule you had not written down. Create your AI agent.
How do you tell a real agent from a chat wrapper?
Ask five questions, and judge the answers against what you can observe.
| Question | A chat wrapper | An agent |
|---|---|---|
| What does it finish without a person pressing a button? | Nothing | A named job, end to end |
| What happens when a tool fails? | It apologizes | It retries, routes around or escalates with the error |
| Does its work survive a closed tab? | No | Yes, through durable session state |
| Can an outside system start it? | No | Yes, through triggers into a stored inbox |
| What does it return? | Text | Records, actions, files and rendered screens |
These are descriptions of two product shapes, not results from comparative performance testing. They were compared on five things: what completes without human input, failure behavior, state durability, what can start the work, and what the user receives.
When do you not need an agent?
- Is the question always the same? A help center answers it once for everybody.
- Is the flow fixed by regulation? Draw the screens and keep the decision deterministic.
- Is there no judgment in the task? A workflow tool runs the sequence for less.
- Is latency the binding constraint? Deterministic paths are predictable in a way orchestration is not.
- Do you need a widget inside another product? That is a smaller piece than an agent.
- Does the task need several steps and a decision between them? That is where an agent earns its place.
Quick checklist
- Write down the job the agent should finish, with a finish line
- List the exact operations and make each one a tool
- Decide what state has to survive a closed tab
- Name which outside systems it may reach, and who authorizes each
- Write what happens when a tool fails
- Name the five points where a person is brought in
- Measure completed jobs rather than messages
Related reading: What MCP means for AI agents · AI agent skills · Webhook and event-driven agent architecture · Agentplace pricing
Bottom line
An AI agent assembles what it knows, decides one action at a time, performs exact work through tools rather than doing it itself, keeps durable state so a task survives a closed tab, accepts events from outside systems, and brings a person in at approval and exceptions.
- If you need something that completes a job rather than describing it, build an AI agent with Agentplace and start with one job that has a finish line.
- If your users ask the same question from a fixed set, improve the help center first and revisit when the answers start depending on each person’s situation.
Questions and answers
Frequently asked questions.
What is an AI agent?
An AI agent is an application whose decision layer is a language model. It receives a goal, decides what to do next, performs that action through a tool or an interface, observes the result and repeats until the task is finished or a person is needed.
How is an agent different from a chatbot?
A chatbot answers a message with a message. An agent chooses actions: calling a tool, rendering an interface, writing to a system, waiting on an external service. The conversation is one input among several rather than the whole product.
What does the model actually decide?
On each turn it decides the next action from the assembled context: respond, show an interface component, call a typed tool, use an authorized integration or run deterministic code. It does not perform the arithmetic or the data access itself, which is why exact work belongs in tools.
Why do agents need tools?
Because models reason well and calculate badly. A tool is a function with a declared input and output, so the result is produced by code that behaves identically every time. The model decides when to call it, and the tool decides what the answer is.
What is agent memory?
It is the state carried between turns and between visits: conversation history, application state, intermediate progress and references to data already produced. It means the agent continues work rather than restarting it, and it is different from recognizing who a visitor is.
Can an agent work while nobody is watching?
Yes, where the runtime supports it. It can call tools, wait on external systems, process asynchronous updates and continue when new information arrives, then surface the result. Events pushed in by other systems are stored before processing, so a restart does not lose them.
When does a person get involved?
At approval, missing business input, exception handling, ambiguous judgment and before a consequential action. A well-built agent surfaces the current task state, the relevant result and the exact decision needed, rather than stopping with a generic error.
How do I tell a real agent from a chat wrapper?
Ask what it completes without a person pressing a button, what it does when a tool fails, and whether its work survives a closed tab. Gartner estimates that of thousands of vendors claiming agentic products, only around 130 genuinely offer agentic features.
When should I use a framework instead of a platform?
When you are embedding the agent inside your own application and want direct control of the runtime rather than a hosted customer-facing page.
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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