A chatbot is a conversational interface; an AI agent is a system that can choose steps or tools while pursuing a defined task. They can overlap: an agent may appear in chat, and a chatbot may call tools. The useful distinction is how the workflow makes choices, preserves state, and controls authority.
Interface and behaviour answer different questions
Chat describes how a person interacts with software. The user types or speaks, and the interface returns a response. That tells us little about the work behind the response. The application might retrieve a fixed answer, run a predetermined workflow, or ask a model to select an appropriate next step.
Agent describes the behaviour being designed. Within a bounded task, the system can interpret the current situation and choose an allowed step, such as looking up a record, seeking additional evidence, or preparing a proposal. Those choices remain subject to the application's permissions and stopping rules.
These categories therefore overlap without being interchangeable. A conversational assistant can contain an agent. An agent can also work behind a form, in a review queue, or as part of a background process. Deciding where to display it should follow the user's task rather than an assumption that AI requires a chat window.
The enterprise agentic AI service describes the engineering scope behind this behaviour. The product still needs integration, evaluation, oversight, and an operating owner regardless of its interface.
Compare the same task in different forms
Consider a hypothetical service application used to prepare an internal support request. A conversational interface could ask the employee for a description and then submit the same predefined search each time. It could show matching guidance and ask whether the employee wants to continue. This is a useful chatbot interaction without requiring flexible planning.
An agent-assisted version might inspect the request, determine that an application identifier is missing, ask for clarification, retrieve permitted support information, and prepare a proposed routing decision. Its next step depends on what it finds. The task remains bounded: prepare a request for review, using declared sources and permitted tools.
The difference is not the friendliness of the answer or whether a tool was called. A fixed workflow can call tools, and an agent can produce a brief answer. Inspect who or what determines the next step and which possible steps the application actually allows.
A buyer should also distinguish a recommendation from a completed operation. In this example, suggesting a support queue is different from creating or reassigning a ticket. The interface should make that distinction visible instead of presenting the agent's plan as an accomplished result.
Tool use does not settle the question of authority
A tool is an interface to a capability. It might read a permitted record, search a document collection, validate an identifier, or expose an execution operation. Calling a tool does not inherently make a workflow agentic, and describing a system as agentic does not justify giving it broad permissions.
The product needs separate decisions about what the system can read, what it can propose, and what can change an external record. Those boundaries should be enforced by the integration and execution design. They should not depend on the model faithfully remembering a warning in its instructions.
CodeDTX's Propose–Decide–Execute pattern keeps the proposing agent from holding an outward write tool. The agent prepares a proposal supported by evidence. A named human approves, edits, or rejects it with a reason. A separate execution layer performs approved work and records the resulting artefact.
For the support-request example, an agent could suggest the destination and attach the relevant evidence. A reviewer decides whether that proposal should proceed. The executor creates or updates the ticket only under the recorded authority. None of those responsibilities change merely because the user began in chat.
Look for state beyond the conversation
A business task often needs durable state that a transcript cannot provide by itself. The application may need to distinguish a draft from a submitted proposal, identify the proposal under review, record the decision, and associate the confirmed result with the original request.
Conversation history can provide context, but it should not become the only source of truth for those states. A user returning later needs to know whether work is waiting for review or whether an external system rejected it. An operator needs a request identifier and linked events rather than an inference from the assistant's final sentence.
State also controls changes. If the employee corrects the affected application after a routing proposal has been approved, the product must determine whether a new decision is required. Treating the entire chat as continuing permission can authorise a materially different action from the one a reviewer saw.
This is a product engineering concern that applies to both chatbot and agent interfaces. It is also a useful way to examine claims that a conversational feature is ready for routine business use.
Evaluate behaviour that matches the task
For a chatbot serving fixed guidance, acceptance may focus on answer relevance, source fidelity, access control, and clear escalation when guidance is missing. If the workflow is agentic, evaluation must also inspect the choices between steps and the conditions under which it stops.
In the hypothetical support workflow, useful cases include ambiguous application names, missing source information, an inaccessible record, and evidence suggesting different routing destinations. The expected behaviour might be clarification, a qualified proposal, or escalation. It should not always be a confident answer.
The article on agent evals explains how task cases expose failures that successful API responses do not. Review tool selection and evidence as well as the final text. A plausible recommendation produced through an impermissible data read is still an unacceptable run.
Operational checks should also cover interruption and recovery. If a source system becomes unavailable, the product needs to report the incomplete task and preserve enough context for a permitted retry. An agent should not keep exploring indefinitely because the interface has no defined stopping state.
Choose the behaviour your workflow actually needs
Use a fixed conversational flow when the path is known and users benefit from asking questions in natural language. Consider agent behaviour when the next useful step depends on uncertain information and the available choices can be bounded and evaluated.
A form or conventional screen may remain the appropriate interface for structured review. Chat can help gather an explanation, while a proposal screen shows the intended changes and evidence clearly. The application does not need to force every part of the task into the same interaction style.
The question of when an agent is the wrong solution follows from this choice. Define the task, permitted choices, and authority first. Then select the interaction and mechanism that make those responsibilities understandable to users and maintainable by the team.
Frequently asked questions
Can a chatbot use tools without being an agent?
Yes. A chatbot can invoke tools through a predefined workflow, such as retrieving a selected record or submitting a fixed search. Tool use alone does not establish agent behaviour. The relevant question is whether the system chooses among permitted steps based on the situation or follows a route already specified by application logic.
Does an AI agent have to use a chat interface?
No. An agent can support a form, prepare items for an approval queue, or analyse an event behind an existing application screen. Chat is an interaction choice. Select the interface that helps users inspect evidence, understand the proposed effect, and recognise the current task state rather than assuming conversation is required.
Does an agent need the ability to change records?
No. An agent can be useful while reading permitted evidence and preparing proposals. In CodeDTX's Propose–Decide–Execute pattern, a named human decides and a separate execution layer performs approved work. The proposing agent has no outward write tool, so its ability to choose analytical steps does not grant authority to change external systems.
How should we assess a supplier's agent demonstration?
Ask to see an ambiguous request, an unavailable source, and a proposal that should be rejected. Inspect how the workflow selects tools, preserves evidence, and reports actual completion. A convincing conversation is insufficient evidence of the surrounding product controls; the demonstration should also show the decision boundary and the path for investigation or recovery.
