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An AI agent is software that pursues a goal by interpreting instructions, choosing intermediate actions, using tools, observing results, and deciding whether to continue, ask for help, or stop. Unlike a fixed script, it can adapt its next step to what it finds. Unlike a text-only chatbot, it may read systems, call APIs, or change external state. “Agent,” however, describes a range of designs—not guaranteed independence, correctness, or safe unsupervised action.
This guide explains what AI agents are, how the loop works, how agents differ from chatbots and automation, and how to design permissions, approvals, checkpoints, and evaluations that keep useful autonomy bounded.
What is an AI agent?
There is no single universally binding definition. A practical 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 agent also needs instructions, access to relevant context, and a way to affect or inspect something outside the model itself. Google Cloud, the OECD, Anthropic, and OpenAI describe the concept with slightly different emphases; all treat autonomy as a matter of degree rather than a binary product category.
For example, an agent asked to reconcile invoices could retrieve records, compare fields, ask for a missing purchase order, and prepare exceptions for approval. It is not necessarily allowed to pay a bill. A support agent might search a knowledge base and draft a reply, while a more empowered version can update a ticket after a human confirms the proposed change.
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OpenAI’s practical design model has three core components: a model for reasoning and decisions, tools for retrieving information or taking actions, and instructions that define behavior and guardrails. Memory, context assembly, orchestration, structured outputs, approval services, and logging commonly surround those components. See OpenAI’s practical guide to building agents and the OpenAI API agent definitions.
Autonomy is a design setting
An agent that may only select among two read-only tools has much less authority than one that can send email, delete records, or spend money. The label does not tell you whether a person approves each action, whether the system can recover from mistakes, or how long it may run. Evaluate the actual permissions, stopping rules, and review points.
How do AI agents work?
Most practical agents follow a feedback loop. Anthropic describes them as “typically just LLMs using tools based on environmental feedback in a loop.” The exact planner may be simple or sophisticated; not every agent creates a long written plan.
- Interpret the goal. A user request is combined with system instructions, policies, available context, and any required output format.
- Select a next step. The model decides whether to answer, request information, or call a particular tool. It may sequence several subtasks, but the application should impose limits.
- Call a tool. A data tool can search documents, query a database, or read a calendar. An action tool can write a record, send a message, or update a ticket. An orchestration tool can hand work to a specialist agent.
- Observe the result. The tool response, error, or other environmental signal supplies evidence about what actually happened. This is different from asking the model to imagine success.
- Continue, ask, or stop. The agent can take another step, ask the user for missing information or approval, pause at a checkpoint, finish, or terminate when it reaches a configured limit.
A robust implementation records the goal, tool arguments, results, approvals, and termination reason. Those records make it possible to diagnose a wrong turn instead of treating the final answer as an unexplained black box.
AI agent vs. chatbot, assistant, script, and workflow
These categories overlap. A chatbot can become agentic when it receives tools and permission to act; an assistant can be highly agentic or merely advisory. Compare a system on the dimensions below rather than relying on its marketing name.
| Dimension | Text-only chatbot | Fixed script or workflow | Tool-using AI agent |
|---|---|---|---|
| Action capability | Returns text | Executes predefined operations | Can retrieve data and, if granted, change external systems |
| Choice of steps | Usually none beyond generating a response | Steps are authored in advance | Model selects intermediate actions within policy |
| Feedback | May use conversation context | Checks explicit branch conditions | Inspects tool and environment results and adapts |
| Permissions | Often no external access | Service account or workflow permissions | Whatever tools, credentials, scopes, and approval gates expose |
| Oversight | User reviews the answer | Operator monitors failures | Can expose progress, require approvals, support interruption, and enforce limits |
A workflow can use an LLM for one classification step without being highly autonomous. Conversely, a conversational assistant that books an appointment after checking availability is an agent for that task. The meaningful questions are: what can it do, who authorizes it, and how does it react to feedback?
What tools can an AI agent use?
Data tools
Search, retrieval, database queries, file readers, browser inspection, and monitoring APIs give the agent information. Read access still needs boundaries: restrict which tenants, folders, rows, or secrets are visible, and filter sensitive fields before they reach the model.
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Action tools
These write data or trigger side effects: sending email, changing a ticket, deploying code, cancelling a subscription, or making a purchase. Separate read and write credentials where possible. Anthropic uses subscription cancellation as an example of a consequential decision that should receive human approval.
Orchestration tools
An agent may delegate a bounded task to another agent or service, such as asking a finance specialist to validate tax fields. Handoffs add latency and failure modes, so define the information passed, the specialist’s permissions, and who owns the final decision.
Browser and screenshot tools
Visual inspection can help an agent verify a page, but browser automation introduces cookie banners, popups, bot checks, timeouts, and changing layouts. The capture service should report whether a result is a clean, billable page or a failed attempt so the agent can retry or escalate accurately.
Choosing an agent architecture
Use the simplest design that satisfies the task. OpenAI recommends starting with one focused agent and splitting only when a specialist genuinely needs different tools, instructions, model behavior, output style, or approval policy.
Single agent
One model owns the loop and a small tool set. This is usually easiest to inspect, test, secure, and keep within a cost and latency budget.
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Break a task into fixed subtasks—such as extract, validate, then format—when each stage has a clear contract. Anthropic identifies chaining as useful when the work naturally separates into those predictable steps.
Routing
A router sends distinct request types to different processes, such as billing, technical support, or account recovery. Each route can have narrower instructions and permissions.
Multi-agent coordination
Multiple specialists can collaborate when the work is genuinely separable, but handoffs, shared state, conflicting outputs, and additional tool calls increase complexity. It is not automatically more capable or safer than a single agent.
Model selection
Establish a quality baseline with a capable model, then evaluate whether a smaller or faster model meets the workflow’s requirements. This is vendor guidance, not a universal benchmark: latency, cost, context size, and error tolerance differ by task.
Implementing a bounded agent loop
A minimal controller should make the model’s authority explicit. The pseudocode below is intentionally provider-neutral:
- Load the user goal and policy instructions.
- Expose only the tools required for this task, with narrowly scoped credentials.
- Ask the model for either a final response, a tool call, or an approval request in a validated structure.
- Validate tool name and arguments against a schema and policy before execution.
- Execute the tool, record the result, and return only the necessary data to the model.
- Stop on completion, user cancellation, an approval denial, a maximum number of iterations, a time budget, or an error threshold.
Do not let the model silently widen its own permissions. The application—not the generated text—should enforce authorization, rate limits, idempotency, and irreversible-action checks.
Safety, oversight, and recoverability
Anthropic’s safety framework states: “A central tension in agent design is balancing agent autonomy with human oversight.” More autonomy increases the chance that a plausible interpretation of the goal exceeds what the user intended.
Use least privilege
- Grant only the files, accounts, APIs, and operations required for the task.
- Keep read and write tools separate, and use short-lived or scoped credentials.
- Redact secrets and unnecessary personal data from prompts and tool results.
Add approval gates
Require a person to approve high-impact actions such as external messages, financial transactions, account deletion, production deployment, or subscription cancellation. Show the proposed operation, affected objects, and relevant evidence—not just “approve?”
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Expose the current goal, tool being called, arguments in a safe form, result status, and next decision. A person should be able to pause, redirect, or cancel execution before a side effect occurs.
Bound execution
Set maximum iterations, elapsed time, tool calls, spending, and retry counts. Use checkpoints before irreversible steps and make operations idempotent where possible so a retry does not duplicate an action.
Evaluate the real workflow
Test with the actual tools, permissions, data quality, and failure responses. Include ambiguous requests, missing records, stale information, tool outages, prompt injection in retrieved content, and denied approvals. The reviewed guidance establishes no general agent success or reliability rate; a number from one workflow should not be generalized to another.
Common failure modes and fixes
The agent loops without progress
Set an iteration limit, detect repeated tool arguments, return clearer error messages, and require a human handoff after a threshold.
It chooses the wrong tool
Reduce the tool list, improve names and schemas, add examples of appropriate use, and validate calls in application code. A router or specialist agent may be preferable when domains are genuinely distinct.
It takes an action too early
Separate planning or preview tools from commit tools. Require explicit approval for the commit and display the exact target and parameters.
Results are hallucinated or stale
Require retrieval for facts that change, return authoritative tool output to the model, attach timestamps, and make “not found” a valid result instead of encouraging guesses.
A tool fails or times out
Classify transient and permanent errors, retry only safe idempotent calls with backoff, preserve the original error in logs, and offer a manual path when recovery is uncertain.
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FAQ
Is every AI system that uses an LLM an agent?
No. An LLM can classify text or draft a response inside a fixed pipeline. It is more agent-like when it selects actions, uses tools, and adapts from environmental feedback.
Can an agent work without a human?
Some tasks can run unattended within strict limits, but autonomy does not remove the need for permissions, monitoring, and recovery procedures. Consequential actions commonly need approval.
Do agents always make a plan first?
No. Some select one tool at a time; others generate an explicit plan or use a workflow engine. The defining behavior is goal-directed action informed by feedback, not a particular planning format.
How should I measure an agent?
Define task-specific success criteria, then test normal, ambiguous, adversarial, and failure cases with the exact tools and permissions used in production. Track quality, latency, cost, unsafe-action rate, escalation rate, and recoverability.
Frequently Asked Questions
Is every AI system that uses an LLM an agent?
No. An LLM can classify text or draft a response inside a fixed pipeline. It is more agent-like when it selects actions, uses tools, and adapts from environmental feedback.
Can an agent work without a human?
Some tasks can run unattended within strict limits, but autonomy does not remove the need for permissions, monitoring, and recovery procedures. Consequential actions commonly need approval.
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Do agents always make a plan first?
No. Some select one tool at a time; others generate an explicit plan or use a workflow engine. The defining behavior is goal-directed action informed by feedback, not a particular planning format.
How should I measure an agent?
Define task-specific success criteria, then test normal, ambiguous, adversarial, and failure cases with the exact tools and permissions used in production. Track quality, latency, cost, unsafe-action rate, escalation rate, and recoverability.
The Bottom Line
AI agents are goal-directed systems that combine a model, instructions, tools, and feedback. Build the smallest agent that can do the job, grant only necessary permissions, require approval for consequential actions, and enforce visible, interruptible limits.
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