AI agent or chatbot?
Start with the job.
A chat interface helps you talk to software. An agent chooses and executes steps toward an outcome. One product can do both.
Field guide / Interface and execution
What should happen after the answer?
Chat describes an interface. Agency describes how a system chooses and carries out steps. One product can combine them.
Two ways of serving an illustrative request. A conversational interface can sit above either approach.
THE DECISION IN VIEW
An answer may be the whole job.
If the task is to explain a known policy, a sourced answer can be enough. The important checks are relevance, accuracy, and whether the user can inspect the source.
- Start with
- A question and a policy source
- Make possible
- An explanation with supporting context
Adding autonomous steps does not automatically make an answer better.
01 / The distinction
A conversation is an interface. An agent is a way of working.
“Chatbot” describes an interaction: a person asks, the software responds. “Agent” describes execution: the system selects steps, uses tools, observes results, and continues toward a goal. Memory and tool access alone do not separate the two. A modern chat product may have both.
01
A good answer is the deliverable
A support assistant explains a policy and points to the relevant page. Its value is a clear, grounded response.
02
A completed task is the deliverable
An assistant investigates a support request, finds the account, drafts the next action, and prepares it for review. Its value depends on the work it completes.
The distinction between fixed workflows and model-directed execution also appears in Anthropic’s engineering guide. The practical design question is how much choice the system needs.
Source notes
02 / Compare
Choose by responsibility, not the label.
| Design question | Conversation-led assistant | Agentic workflow |
|---|---|---|
| What is the output? | An answer, explanation, or draft | An observable task result |
| Who chooses the next step? | Usually the person | The system, within its permissions |
| What does it need to read? | Enough context to respond | Enough context to decide and verify |
| What happens after an error? | The person asks again or escalates | The workflow retries, changes course, or stops |
| What should be reviewed? | Answer quality and sources | Decisions, actions, and the final result |
03 / A concrete example
From “what should I do?” to a reviewable next action.
Consider a real estate team preparing follow-up. A conversational assistant can summarize a contact. A connected workflow can find context, prepare a draft, and attach the next task to the CRM. The team still decides what to send.
- 01ReadFind the relevant contact history.
- 02PrepareDraft a message with context.
- 03ReviewLet the team inspect and edit.
- 04RecordKeep the next action in the CRM.
From reading to doing
Watch an answer become a reviewed action.
A useful conversation can lead to work in another system. Follow an illustrative CRM request from context to draft, then make the review decision yourself.
Try the workflow
04 / Make the decision
What does your team actually need?
We answer the same questions all day.
Start with a well-grounded conversational interface and a clear escalation path. Keep the information current and measure whether the answer resolves the question.
Every request needs several systems.
Map the necessary tools, the evidence each step needs, and the review points. An agent can help when the next step depends on what it finds.
The steps are already known and repeatable.
Use a conventional workflow for those steps. Add an AI decision only where interpretation is useful; you do not need an agent to move a record from A to B.
Some actions must stay with a person.
Make that boundary part of the product. The system can gather context and prepare work, then stop at an explicit review point.
05 / Define success
Write the operating contract before the prompt.
Outcome
Describe what a completed task looks like in the system of record.
Permissions
List the tools the assistant can read or change and the limits on each action.
Review
Identify decisions that require a person, including ambiguous or incomplete inputs.
Evidence
Keep enough context to explain what happened and verify the result.
Keep exploring
The questions behind the question.
What is the difference between an AI agent and a chatbot?
A chatbot is a conversational interface. An agent selects and executes steps toward an outcome. A product may combine both: conversation for the user, and a controlled workflow behind it.
Can a chatbot and an AI agent work together?
Yes. The conversation can collect a request, explain progress, and present the result while an agent performs the connected work.
Does an agent need complete autonomy?
No. Its scope can be narrow, with explicit review points and limited tools. The useful question is which decisions it should make and which should remain with a person.
How is an AI agent different from RPA?
RPA typically follows prescribed interactions with software. Agentic execution can select the next action based on context. Both still need error handling, permissions, and an owner.
Are ChatGPT and Claude chatbots or AI agents?
Both. In a standard chat interface, they behave as chatbots. Paired with tools, memory, and orchestration logic — often via the Model Context Protocol — the same model becomes the reasoning core of an AI agent. The model doesn't change; the deployment pattern does.
What are the types of AI agents?
They're grouped by how they decide what to do next: reflex agents react to the current input using fixed rules, goal-based agents plan a sequence of actions toward an objective, and utility-based agents weigh outcomes against a scoring function to pick the best one. Most production business agents — a CRM lead-scoring agent, a legal intake agent — are goal- or utility-based with tool access layered on top.