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An AI agent is software that uses an AI model to choose steps toward a goal and can request configured tools—such as a weather lookup or a record update—to help carry them out. The model does not independently reach into the world: a host application or platform runs the requested operation and returns its result. What an agent can do therefore depends on its model, instructions, tools, runtime access, permissions, and oversight.
What is an AI agent?
An AI agent is a model-driven software system that can work through a task by selecting a next step, requesting an available tool when useful, and using the result to continue or stop. “Think” here means choosing a likely next step from the request and the context the system has—not human-like consciousness or guaranteed correctness.
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There is no single universal component list. OpenAI’s practical guide describes an agent in terms of a model, tools, and instructions. Anthropic’s account also distinguishes the harness—the software and rules that run the agent—and the environment it can access. These are complementary ways to describe the system:
- Model: Interprets the task and context and selects a likely next step. It can misunderstand or make mistakes.
- Tools: Defined operations for retrieving information, taking actions, or coordinating with other agents.
- Instructions or harness: The directions, runtime logic, and constraints that shape behavior and determine how tool requests are handled.
- Environment: The files, websites, services, and other systems available to the running agent. A different environment can mean different access and different stakes.
These descriptions come from vendor guidance, not a settled formal standard. They are useful as a practical map: the model is only one part of the system.
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How does an agent use tools?
Consider a low-stakes request: “What will the weather be like in Boston tomorrow?” If a weather tool is configured, the exchange can work like this:
- The user gives the agent a goal.
- The model interprets the request and decides that current forecast data would help.
- The model emits a structured request for the weather tool, including arguments such as the location and date.
- The host application or platform executes that operation.
- The tool’s result is returned to the model as context.
- The model answers, requests another tool, or stops when the task is complete or a checkpoint requires human input.
Anthropic’s Claude Platform documentation states: “The model never executes anything on its own.” The model proposes a structured tool call; the application or platform runs it and passes the result back. A tool definition describes the available operation and the expected shape of its inputs and outputs. In a client-executed setup, the application repeatedly handles requests, execution, and returned results.
This distinction matters when the tool can change something. If an agent sends an email, edits a file, or updates a record, the external tool and its runtime perform that side effect. A proposed call is not proof that the action succeeded; the returned result is what the model can use to report what happened.
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How is an AI agent different from a chatbot?
The difference is not simply that one uses AI and the other does not. A basic chatbot mainly generates responses in a conversation. An agentic system can also request configured operations, receive their results, and continue through a task. A chatbot can include tools, too; “agent” describes how the system is set up to pursue a goal, not a guarantee that it is fully autonomous.
| Question | Chatbot focused on replies | Tool-using agent |
|---|---|---|
| What does it do? | Generates a conversational response. | Can request configured operations and use returned results while working toward a goal. |
| Who performs outside actions? | If tools are not enabled, it only replies. If they are enabled, an application or platform still executes them. | The host application or platform runs each requested tool; the model itself does not perform the operation. |
| Can work continue without another user message? | Usually responds to the current conversational turn. | May repeat the decide–tool–result loop until a stopping condition or human checkpoint, depending on its setup. |
| What access does it have? | Depends on the product and its configured capabilities. | Depends on the tools and environment exposed to it; “agent” does not imply access to every file, service, or website. |
| What controls are needed? | Controls appropriate to its capabilities. | Controls matched to tool permissions, possible side effects, and the risk of acting on a misunderstood request. |
What keeps an agent from doing the wrong thing?
No single safeguard makes an agent infallible. A system can misread intent, choose an unsuitable tool, or be influenced by prompt injection. A capable model can still be put at risk by an overly permissive tool or environment. Safety depends on the model, harness, tools, and environment together.
For a beginner building or evaluating an agent, useful controls include:
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- Limit access: Expose only the tools and data the task needs.
- Specify behavior: Give clear instructions, define tool inputs and outputs carefully, and set conditions for stopping or asking for help.
- Check evidence of completion: Review tool results rather than treating confident prose as proof that an operation worked.
- Require approval for high-impact actions: Keep a person in the loop for sensitive or hard-to-reverse steps. OpenAI’s examples include cancellations, large refunds, and payments.
- Increase autonomy gradually: Begin with a bounded task, observe how the system handles it, and widen its scope only when the controls fit the risks.
When should a task use one agent—or several?
A single agent is one model equipped with tools and instructions, typically running until a stopping condition is met. A multi-agent system coordinates work across agents, which can introduce delegation and handoffs as well as additional complexity.
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- Manager-style delegation: A lead agent assigns parts of a task to other agents.
- Decentralized handoffs: Agents pass work to one another without one manager directing every step.
More agents are not automatically better. If one agent can complete a bounded task with clear tools and stopping rules, orchestration may add work without solving a real problem.
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How can a beginner learn the pieces?
Microsoft Learn’s “Get started with Agent Framework” tutorial lays out a progression from creating a first agent to adding tools, handling multi-turn conversations, adding memory and persistence, composing workflows, adding a planning harness, and hosting the agent. The page was last updated on 2026-08-25 and labels its Go framework public preview; both details describe the page at that time and may change.
The progression is useful beyond that particular framework: first understand a bounded agent, then introduce the capabilities that make it more persistent or coordinated. Each addition changes what the system can do and what should be checked.
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Quick Recap
What to remember
- An agent combines a model with tools and operating instructions; its harness and environment also shape its behavior.
- Tool use is a request-and-result exchange. The runtime executes the operation, not the model alone.
- Agents can repeat a decide, act, observe, and continue-or-stop loop, subject to configured limits and checkpoints.
- Permissions, review, and human approval matter whenever tools can create meaningful side effects.
- Start with the simplest system that can do the task; add autonomy or agent-to-agent coordination only when it addresses a real need.
Sources
- OpenAI, “A practical guide to building agents”
- Anthropic, “How tool use works”
- Anthropic, “Trustworthy agents in practice”
- Microsoft Learn, “Get started with Agent Framework”
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