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An AI agent is an AI system that pursues a goal by choosing steps, using tools, observing results, and continuing until the task is complete or a person must decide. It is not defined by having a chat window. The important differences from a chatbot or conventional automation are who selects the next step, whether the system can act on external services, and where people must approve or correct it.
Agents can include fixed workflows, and chatbots can have tool connections, so these categories overlap. The practical test is how much decision-making happens at runtime and what the system is permitted to do.
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What an AI agent is
There is no single, universally adopted standards definition of “AI agent.” In its developer documentation, OpenAI describes tools as capabilities an agent can call directly. Anthropic’s April 9, 2026 article defines an agent as “an AI model that directs its own processes and tool use when accomplishing a task—that is, deciding for itself how to achieve what users want, rather than following a fixed script.” Anthropic summarizes the operating pattern as a loop that plans, acts, observes, adjusts, and repeats until the goal is reached or a human check-in is required. See OpenAI’s agent definitions and Anthropic’s explanation of trustworthy agents.
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- A goal: The system is given an outcome, such as resolving a support case or preparing a report, rather than only a request for text.
- Choice of steps: It can decide which available action to take next instead of executing one completely predetermined sequence.
- Tools and feedback: It can call approved services, inspect their results, and change course when the result differs from expectation.
An agent may communicate through chat, voice, a form, an API, or a background process. The interface does not determine whether it is an agent; its decision-making and ability to act do.
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The agent loop
- Interpret the objective. The model turns a request into a desired outcome and identifies missing information or constraints.
- Plan a next action. It selects among the tools and steps made available by its developer or administrator.
- Act. It performs an operation such as searching a knowledge source, reading a record, calling an API, or drafting an update.
- Observe. It receives the tool’s output, error, status, or other environmental feedback.
- Adjust. It may choose another step, ask the user a clarifying question, request approval, or stop if the objective is satisfied.
This loop can run once or many times. A short task might involve one search and one answer; a complex task can involve several systems and decisions. More iterations do not automatically mean better results: each additional action creates another opportunity for a mistaken interpretation or an unsafe side effect.
Autonomy is bounded
An agent is only as autonomous as its permissions, tools, and approval rules allow. An implementation can let it read information but not change it, require confirmation before sending a message or issuing a refund, or block a category of action entirely. Anthropic’s safety framework describes keeping people in control, while its practical research discusses permission choices such as allowing an action, requiring approval, or blocking it. Read Anthropic’s safety framework and its discussion of permission controls.
“Autonomous” therefore does not mean unsupervised or unlimited. A system with no write access cannot update a customer record, and one that must ask before every external action may behave more like an assistant than a fully hands-off process.
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AI agent vs. chatbot
A chatbot is primarily a conversational interface: it receives a message and returns a response. It may answer a billing question, explain a policy, or generate text. Some chatbots are connected to search, databases, or business APIs, so the label alone does not prove that the system is non-agentic.
An agent can also use chat, but its objective is to complete an outcome through actions. Microsoft illustrates the distinction with customer service: a chatbot might answer a billing question, while an agent could process a refund, update the customer record, and notify the customer. That is an example of action and integration, not a guarantee that every product marketed as an agent can safely or reliably perform those operations. See Microsoft’s Copilot Studio comparison.
| Question | Chatbot behavior | Agent behavior |
|---|---|---|
| Primary output | A conversational answer or generated content | Progress toward a goal, potentially including external changes |
| Who chooses the next step? | Usually the user, by sending another prompt | The system can select among approved steps at runtime |
| Tool access | May have none, or a narrow connected tool | Designed to use one or more approved tools, services, or records |
| Interaction pattern | Often request, answer, and stop | Can plan, act, observe results, and continue or escalate |
| Human oversight | Review is commonly applied to the answer | Approval can be required before specific actions, during the loop, or at completion |
The boundary is not binary. A support bot that retrieves an order status and changes an address has agent-like capabilities. Conversely, an agent may be configured only to draft responses, making its visible behavior similar to a chatbot. Evaluate the actual tools, permissions, and control flow rather than the product label.
AI agent vs. conventional automation
Traditional automation executes a sequence defined in advance: trigger an event, validate fields, call service A, then service B, and report success or failure. OpenAI describes a workflow as a sequence of steps executed to meet a user’s goal. This approach is predictable when inputs and exceptions are known.
An agent can encounter an input it did not anticipate, select a different tool, and adapt after seeing a result. The OECD’s 2026 report contrasts deterministic workflows with agents that respond to dynamic inputs and alter their actions. The distinction is about where decisions are made, not whether code exists. An agent may contain fixed sub-workflows, and an automation can include conditional branches.
| Dimension | Defined automation | Agentic system |
|---|---|---|
| Process design | Most steps and branches are specified before execution | The developer sets goals, tools, and constraints; the model can select steps during execution |
| Response to unfamiliar input | Usually follows an exception path or fails for human handling | May reinterpret the situation, try another permitted action, or ask for clarification |
| Predictability | High when conditions match the design | More flexible, but outputs and paths can vary |
| Testing focus | Verify each known branch and data transformation | Test tool selection, permissions, intermediate results, escalation, and recovery as well as final answers |
| Best fit | Repetitive, stable, high-volume procedures with clear rules | Tasks requiring interpretation, changing context, or several possible routes |
Choose a workflow when the process is stable and a wrong branch is costly. Consider an agent when the work cannot be completely specified beforehand and the value of adapting to context outweighs the additional oversight and testing required. A hybrid often works best: deterministic code handles sensitive or repeatable operations, while an agent interprets requests and chooses among those bounded operations. Sources: OpenAI’s practical guide and the OECD’s 2026 conceptual report.
AI agent vs. generative AI
Generative AI produces content such as text, images, audio, or code in response to an input. An agentic system adds a goal-directed process, tool use, or external action. Generative output can be one step inside an agent, but generation alone does not make a product an agent.
For example, a model that drafts an email is generative AI. A system that decides which customer records to inspect, drafts the email, checks required fields, asks for approval, and sends it through an approved mail service is operating as an agent. Microsoft makes this distinction in its overview of autonomous agents: content generation is different from autonomous decisions and actions. See Microsoft’s overview.
What agents can do: concrete examples
Customer-service resolution
A customer-service agent could identify a customer, check a billing record, determine whether a refund policy applies, submit the refund, update the record, and notify the customer. Each operation requires a specific integration and permission. A safer deployment might allow read access automatically but pause for human approval before the financial transaction or customer-facing notification.
Research and reporting
Anthropic describes a board-presentation scenario in which an agent searches connected Drive files for sales reports and spreadsheets, extracts metrics, and creates a report. This is a described use case, not a performance guarantee. In practice, the system still needs access controls, source validation, handling for missing or conflicting figures, and a review step before distribution. The example appears in Anthropic’s framework article.
Internal operations
An agent might triage incoming requests, gather information from several internal systems, and route the case to the right team. If it cannot establish the requester’s identity, finds contradictory records, or encounters a failed API call, its correct behavior may be to pause and ask a person rather than improvise.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare an agent implementation
Product names are a poor basis for comparison. Use the following questions when evaluating a system or designing one. These criteria synthesize the control and capability issues documented by OpenAI, Anthropic, and the OECD; they are not a universal scoring standard. See OpenAI’s agents documentation, Anthropic’s research, and the OECD report.
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|---|---|---|
| Task scope | Does it answer one question, complete a multi-step task, or monitor an ongoing process? | Documented task boundaries and examples of escalation |
| Step selection | Are steps fixed in code, or can the model choose among tools and branches? | Workflow diagrams, tool-selection rules, and logs |
| Tool access | Can it search, read, write, call APIs, or only generate text? | Permission scopes and a complete tool inventory |
| Autonomy and oversight | Which actions require approval? Can it pause for clarification? | Approval policies, blocked actions, and escalation behavior |
| Integration and recovery | What happens after an API error, timeout, bad result, or ambiguous request? | Retry limits, rollback or compensation procedures, and human handoff paths |
Safety, reliability, and failure modes
Misread intent
An agent can infer the wrong objective and still execute valid tool calls. Require explicit confirmation for ambiguous requests, high-impact changes, and actions affecting money, access, legal status, or personal data.
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Excessive permissions
Grant the narrowest access needed for the task. Separate read and write credentials, limit which records or services are reachable, and make approvals action-specific rather than treating “agent access” as one broad permission.
Compounding errors
A mistaken early result can steer later decisions. Keep intermediate results visible in logs, validate critical values against authoritative systems, and stop after defined uncertainty or failure thresholds instead of allowing an endless loop.
Unclear recovery
Every tool call should have a defined outcome for success, failure, timeout, and partial completion. If an action may have happened despite an error, the agent should check status before retrying to avoid duplicate refunds, messages, or updates.
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Human review is not merely a final quality check. People may need to approve a particular action, resolve conflicting information, change the objective, or take over when the system cannot establish a safe next step. Anthropic’s safety guidance emphasizes keeping humans in control while agents operate.
When to use an agent, automation, or chatbot
- Use a chatbot when the main requirement is conversation, explanation, or content generation and external changes are unnecessary.
- Use conventional automation when the inputs, sequence, and business rules are stable and predictable execution matters more than flexibility.
- Use an agent when the system must interpret an open-ended goal, choose among several approved tools, and adapt to information discovered during execution.
- Use a hybrid when interpretation is variable but sensitive actions must remain deterministic, auditable, and approval-gated.
Before deployment, write down the goal, permitted tools, data boundaries, approval points, stopping conditions, and human handoff route. Then test not only successful demonstrations but also ambiguous requests, missing data, contradictory records, denied permissions, tool failures, and partially completed actions.
The bottom line
An AI agent is best understood as goal-directed software that can choose and execute steps with tools, observe results, and adapt within defined permissions. A chatbot is primarily a conversational interface; automation follows a designed sequence; generative AI creates content. Real systems can combine all four, so judge them by runtime decision-making, integrations, autonomy, oversight, and recovery—not by the label on the product page.
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