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An AI agent is software that pursues a goal by using a model to choose steps, tools to take actions, and feedback from its environment to decide what to do next. Unlike a chatbot limited to returning text, an agent may search, read, update records, or call other services. The label does not guarantee independence or correctness: an agent’s autonomy depends on its design, tools, permissions, and oversight.
What is an AI agent?
There is no single universally binding definition of an AI agent. A useful working definition is software that pursues a goal with some autonomy, using a model and available tools to observe and act in an environment. The model might be a large language model, but the defining idea is not a particular model or product name: it is the ability to select actions toward a goal and use what happens next to guide further steps.
Autonomy comes in degrees. One system may suggest a next step but wait for a person to perform it. Another may independently call a read-only search tool. A more capable workflow may update data or send a message, subject to approval rules. Calling any of these an “agent” does not mean it can handle every situation, work indefinitely, or safely act without supervision. Google Cloud’s overview and the OECD’s 2026 landscape paper describe a varied field rather than one settled definition (Google Cloud; OECD).
How do AI agents work?
A common design is a feedback loop: interpret a goal, select an action, use a tool, inspect the result, and then continue, ask for help, or stop. Anthropic describes agents as “typically just LLMs using tools based on environmental feedback in a loop” (Building Effective AI Agents). The loop can be simple; it does not require a sophisticated planner or a team of agents.
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- Receive the goal and instructions. A user states an outcome, such as finding the latest status of an order. Instructions set boundaries: which sources to use, what not to change, and when to request approval.
- Interpret and choose a step. The model determines what information is missing and picks an available action. Depending on the task, that could mean searching a knowledge base, opening a record, or asking the user a clarifying question.
- Call a tool. A tool connects the model to data or an action. The agent may pass arguments such as a search query or record identifier to the tool. The tool performs the operation and returns a result.
- Observe the result. The agent uses the tool response or other environmental feedback as evidence of what happened. A failed search, missing record, or permission error may change the next step.
- Continue, ask, or stop. The agent can make another tool call, present its findings, request information or approval, pause at a checkpoint, or stop when a completion condition or configured limit is reached.
This is not a promise that the model’s internal reasoning is complete or always reliable. The useful operational distinction is that it can act on tool results and adapt its next step, rather than merely return one answer.
What are the parts of an AI agent?
OpenAI’s practical design guide identifies three core components: a model, tools, and instructions (A practical guide to building agents). Implementations can add memory, context management, orchestration, structured output, and human approvals; those additions do not remove the need to define what the agent may do.
| Part | What it does | Questions to settle |
|---|---|---|
| Model | Interprets the request and selects or sequences actions. | Does it meet the task’s quality needs at an acceptable latency and cost? |
| Tools | Connect the agent to information and external actions. | Can a tool only read, or can it write, send, delete, or spend? |
| Instructions | Define the task, boundaries, and expected behavior. | What is out of scope, and when must the agent ask first? |
| Context and memory | Supply relevant prior information or task state where the implementation supports it. | What information is retained, for how long, and who can access it? |
| Orchestration and approvals | Route work, coordinate tools or agents, and pause for checkpoints. | Which decisions need a person, and what happens at a limit? |
Tools determine the agent’s authority
Data tools retrieve or read information, such as search results or account records. Action tools change external state, for example by sending a message or updating a record. Orchestration tools can delegate work to another agent. These are materially different permissions: access to a read-only lookup does not grant the authority to modify the underlying account.
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For example, an agent that can inspect a support ticket and draft a reply has less authority than one that can send the reply and issue a refund. The model may be similar; the available tools and permission checks make the practical risk different.
AI agent vs. chatbot: what is the difference?
“Agent” and “assistant” are overlapping labels, not mutually exclusive technical categories. A conversational assistant may simply explain or recommend, leaving every action to the user. An assistant with tool access can also be agentic. Conversely, a workflow using an LLM may follow a fixed sequence and have little autonomy.
| Compare | Text-only chatbot or fixed workflow | More agentic system |
|---|---|---|
| Action capability | Returns text, or executes predetermined operations. | Can select among tools, including tools that affect external systems. |
| Autonomy | A person supplies each next step, or the sequence is fixed in advance. | Can choose intermediate steps toward a stated outcome. |
| Feedback | May not inspect action results or change its next step. | Uses tool or environmental results to decide what to do next. |
| Scope and permissions | Limited to the response or configured sequence. | Depends on the systems, data, and actions the tools expose. |
| Oversight and recovery | Often easy to review before a person acts. | Needs visible progress, interruption, checkpoints, and suitable approval controls. |
These are comparison points, not a rigid test. A system can combine a conversational interface with agentic actions, and a workflow can use a model without being highly autonomous. Judge the actual capabilities and controls, not the marketing label. Google Cloud’s overview discusses the range of agent types, while Anthropic’s guide distinguishes flexible agent loops from simpler fixed workflows (Google Cloud; Anthropic).
When should you use an agent, a fixed workflow, or multiple agents?
Use the simplest design that can do the job. If the work has clear, stable steps, a fixed workflow or prompt chain can be easier to understand and test than an open-ended loop. If different kinds of requests need different processes, routing each to a suitable path may be useful. An agent loop is a better fit when the next step depends on information discovered during execution.
Start with one focused agent when its instructions, tools, and approval rules fit together. Consider splitting responsibilities when a specialist genuinely needs different tools, instructions, model behavior, output style, or approval policy. Multiple agents add handoffs and coordination complexity; they are not inherently more capable or reliable. OpenAI recommends beginning with one agent and expanding when there is a concrete reason (OpenAI API: Agent definitions).
Choose a model by evaluating the actual workflow
Model quality, latency, and cost trade off against one another. OpenAI’s practical guide recommends first establishing a performance baseline with capable models, then evaluating whether smaller, faster models meet the task’s requirements. This is vendor guidance, not a guarantee that a particular model will perform well in your application. Evaluate with the actual instructions, tools, permissions, and representative cases you intend to deploy.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to make AI agents safer and more dependable
An agent can misunderstand a goal or take an action that is reasonable under its instructions but beyond what the user intended. Anthropic’s framework for safe and trustworthy agents frames the central design tension as “balancing agent autonomy with human oversight” (August 4, 2025 framework). Practical safeguards should match the possible consequences of a mistake.
- Grant only necessary permissions. Separate reading from changing data. Do not expose tools or accounts the task does not need.
- Require approval for consequential actions. Put a person in the loop before actions such as cancelling a subscription, sending sensitive communications, or making an irreversible change.
- Show progress clearly. Make the plan, tool activity, and current status visible enough for someone to catch a wrong direction and intervene.
- Use checkpoints and stopping limits. Specify when the agent must pause, how many attempts or iterations it may make, and what to do if it cannot complete the task.
- Test the whole workflow. Evaluate realistic tasks using the deployed tools and constraints, including failures and ambiguous requests. There is no general agent success rate established by the cited sources that can substitute for this evaluation.
OpenAI’s guide covers tool boundaries and orchestration; Anthropic’s development and safety guidance emphasizes feedback, control, and oversight (OpenAI; Anthropic; Anthropic safety framework).
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Agents can use tools to gather information from websites. If your task is to capture a site rather than build and maintain browser automation, ScreenshotNeo is a website screenshot API and MCP server for developers. One GET request can return a PNG, JPEG, WebP, or PDF. For example, this cURL request saves a WebP screenshot of Stripe:
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curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webp
See the ScreenshotNeo documentation for API details. It removes cookie and consent banners, newsletter popups, and chat widgets before capture; each cleanup step can be turned off. Bot checks, blank pages, timeouts, failed loads, and cache hits are not billed, and response headers report the page verdict and billing status. Its MCP server provides take_screenshot, get_page_info, and capture_pdf for AI agents using Claude, Cursor, or another MCP client. The free plan includes 1,000 screenshots per month without a card; paid plans start at $5 for 3,000 screenshots.
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Frequently Asked Questions
Does every AI agent use a large language model?
No. The term describes a goal-directed system with some ability to choose actions; the definition does not require one particular model type.
Can an AI agent work without internet access?
It can, if its task and tools are available locally. Internet access is only needed when the agent must use online services or data.
Are multi-agent systems always better than one agent?
No. They can add coordination overhead, so use multiple agents only when distinct roles or permissions justify the added complexity.
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