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An AI chatbot is a conversational interface. An AI agent is a system that can select and perform steps toward a goal, often using tools and feedback. These categories overlap: an agent can speak through a chatbot, and a chatbot can call a tool without becoming a broadly autonomous system.
The practical difference is who controls the next operation and what the system is allowed to change. Compare the complete application, including deterministic rules and human review, rather than classifying it from a chat window or the model's marketing name.
This guide uses illustrative designs, not measured product comparisons. It is intended for developers deciding how much action-taking behavior a task needs. For the broader packaging question, read AI apps versus agents.
For a primary discussion of fixed workflows versus model-directed agents, see Anthropic’s engineering explanation. The design examples here are illustrative and do not describe measured customer deployments.
Compare behavior rather than appearance
| Question | Conversational assistant | Tool-using agent design |
|---|---|---|
| Primary output | An answer or draft | An outcome reached through a sequence of operations |
| Next step | Commonly prompted by the user | May be selected from feedback within configured limits |
| External access | Optional | Often important to task completion |
| Main failure | Incorrect or misleading information | Incorrect information plus potentially incorrect actions |
| Required evidence | Sources and answer quality | Sources, tool results, changed state and completion criteria |
These are design patterns, not a protocol definition. A fixed support flow with one model call may be more reliable than an open-ended agent loop. More autonomy is useful only when the task benefits from choosing steps at runtime.
Example: explaining a refund policy
Consider a support assistant asked whether a purchase qualifies for a refund. A read-only design retrieves an approved policy and explains the relevant conditions. Its useful result is a checked answer, with uncertainty when information is missing.
That design does not need permission to change an order. Give it access to the minimum policy information and, where authorized, the relevant purchase facts. Do not grant refund permissions merely because the interface is conversational.
An incorrect explanation can still harm a customer. Check material facts, keep policies current and provide escalation. Read-only does not mean risk-free; it means the system has a smaller action surface to control.
Example: completing a refund workflow
A more capable application might inspect an order, propose the applicable policy decision and prepare a refund request. A consequential final action should remain behind the appropriate authorization and deterministic checks.
The model's proposal is not proof of eligibility. The application must verify account ownership, payment state, permitted amount and whether the operation already occurred. User-supplied text cannot authorize access to another account.
After execution, inspect the payment system's result. Distinguish a proposed action, an accepted request and a confirmed outcome. If a timeout leaves the result unknown, investigate before retrying; repeating a consequential operation can duplicate its effect.
Reasoning is not a permission boundary
A persuasive explanation of why an action seems helpful does not grant the authority to perform it. Keep authorization outside the model's generated reasoning. Validate tool arguments and enforce ownership and scope on the server.
For example, a support agent should not receive a generic database command when a narrow order lookup is sufficient. Tools that expose specific business operations are easier to authorize, test and observe than unrestricted shell or SQL access.
When asking for approval, show the material effect and destination. A vague “continue?” is weaker than a reviewable proposed change. The approval mechanism must match the real operation rather than merely confirming the model's description of it.
When a chatbot or fixed workflow is enough
Use conversational assistance when the reader mainly needs an explanation, a draft or help clarifying a question. Use a deterministic workflow when the sequence is already known and the important decisions can be expressed as rules.
For example, extracting a purchase identifier, validating its format and showing the order's status may not require an agent to plan its own sequence. Keeping that path explicit simplifies testing and makes failures easier to diagnose.
Do not introduce a loop solely to call the product an agent. Each additional step adds opportunities for latency, cost and uncertain state. First establish that dynamic tool selection solves a real problem.
When an agent can be useful
Agent behavior is worth evaluating when the next useful action depends on intermediate results. A developer assistant investigating a failing test may need to inspect different files based on what it discovers. A research workflow may need another source after identifying a contradiction.
Even then, define the allowed operations, deadline and completion criteria. An agent should know when to stop, ask for missing information or return a partial result. Endless retries are not persistence if the system has no evidence that another attempt can help.
Begin with read-only operations. Add writes only after the read path works and the application can verify the effects. Keep a record of actual tool calls and results rather than treating a final narrative as an execution log.
Evaluate the actual outcome
Build task cases with clear expected properties. A support answer should preserve policy conditions. An action workflow should modify only the authorized record and avoid duplicate effects. Include missing data, stale information, denied permissions and partial failure.
Measure task success separately from fluent output. If you report cost or duration, include tool calls, retries and human correction. This article contains no benchmark results or assumed success rates.
Test untrusted content that tells the system to ignore its instructions or reveal information. The application should retain its access restrictions regardless of what a retrieved document says. Treat tool responses and uploaded files as information to interpret, not new authority.
Where MCP fits
MCP can standardize how an application connects to external capabilities. It does not decide whether the application should act autonomously or what the user has authorized. A read-only assistant and an action-taking agent can both use MCP.
Read the MCP server architecture guide for the connection layer. Keep the control loop, permissions and business rules in the application design instead of assuming a protocol supplies them.
Choose the smallest useful action surface
Write down the outcome, allowed tools, forbidden actions and evidence of completion. If a conversation or fixed sequence meets the need, start there. Introduce agent behavior when intermediate feedback genuinely needs to change the path.
Before releasing the result, use the AI app launch checklist. Developers distributing a workflow should read how to publish an AI agent. You can discover software in the Theaimart AI marketplace, checking each product's actual permissions and operating model.