Conversational AI
Understands a request in natural language and replies — by chat, voice or email — keeping track of the conversation.
- Output is a reply
- One turn in, one turn out
- Reads from knowledge it is given
- A person carries out the result
Guide · Conversational AI
Conversational AI vs agentic AI: a system that answers, and one that gets it done.
Conversational AI understands a request and replies. Agentic AI plans steps, uses tools and changes things in other systems — often through the same chat box.
The two are often sold as rivals, and often confused on purpose. They sit on one scale: how much a system is allowed to do after it understands what you asked. This guide places common systems on that scale, shows the loop that makes software agentic, compares the same request handled both ways, and gives a way to decide how far along the scale a given job should go.
Updated · 6 sources
AnswersActs
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- Rule-based chatbotScripted replies to recognised phrases.
- Intent-based assistantClassifies the request, fills slots, runs one fixed flow.
- LLM chat assistantUnderstands free text and writes its own replies.
- Grounded assistantSearches your sources and cites them.
- Tool-using agentCalls tools, reads results, decides the next step.
- Autonomous agentWorks toward a goal over many steps with little supervision.
What is the difference between conversational AI vs agentic AI?
Conversational AI is software people talk to in natural language: it understands the request, keeps track of the conversation and replies. Agentic AI is software that pursues a goal: it plans steps, calls tools, reads the results and decides what to do next. In conversational AI vs agentic AI, the first is about the dialogue and the second is about the doing — and an enterprise assistant usually needs both.
Side by side
Conversational AI vs agentic AI, compared dimension by dimension.
The clearest test is what happens after the system understands the request. A conversational system answers. An agentic system decides on an action, takes it, and checks what happened.
Agentic AI
Takes a goal, breaks it into steps, uses tools to carry them out and adjusts from what each step returns.
- Output is a finished task
- Many steps per request
- Reads and writes other systems
- A person approves or reviews
Understand, then replyCore loopPlan, act, observe, repeat
Rarely, or for lookupsUses toolsCentral to how it works
The conversationState it keepsThe conversation, the plan and tool results
A wrong or ungrounded answerTypical failureA wrong action in a real system
Grounding, citations, read permissionsControls neededAll of those, plus tool grants and approvals
One or two model callsCost per requestMany model and tool calls
How it works
What makes a system agentic: the plan-act-observe loop.
Anthropic draws the line this way: a workflow runs language models through predefined code paths, while an agent lets the model “dynamically direct” its own process and tool use. The ReAct paper (2022) showed the pattern underneath — reasoning interleaved with actions.
One agent working on one request
- GoalThe request, restated as an outcome to reach.
- PlanThe model proposes the next step from what it knows.
- ActIt calls a tool: search, query, update, send.
- ObserveThe tool result comes back into its context.
- CheckDone, blocked, or another step needed?
Until the goal is met, a person must decide, or the agent gives up and says why.
An agent is conversational AI with more layers
01
Conversationchat, email, voice, a messaging app
conversational02
Understanding and reasoninga language model reads the request and plans
both03
Context and memorywho is asking, what was said, what is on screen
both04
Tools and actionsthe systems the agent may read and change
agentic05
Guardrails and approvalswhich tools wait for a person, what is refused
agentic06
Recordevery step, call and decision, kept for review
agenticThe same request, twice
One refund request, handled by conversational AI and by agentic AI.
A customer writes that a delivery arrived damaged. Both systems understand the message equally well. What differs is who does the work afterwards.
Conversational AI
You
My order 7781 arrived with a cracked screen. I want a refund.
Agent
Sorry about that. Damaged items can be refunded within 30 days: open Orders, choose 7781, select “Report a problem” and attach a photo. A refund is usually issued within five working days.
Agentic AI
You
My order 7781 arrived with a cracked screen. I want a refund.
Agent
I found order 7781, delivered on Tuesday and within the 30-day window. I’ve opened a damage case with your photo. The refund of $349 needs a team member’s approval — I’ve sent it and will confirm here when it is issued.
What the second one needed. Read access to orders, permission to open a case, a refund tool that waits for a person’s approval, and a record of each step. The language model is the same; the tools, grants and checks around it are what make it agentic.
Expectations and reality
What analysts predict for agentic AI, and what they warn about.
Forecasts for agentic AI are large, and so are the warnings. Gartner’s figures below are predictions, not measurements — useful as a sense of direction and of the risk of buying the label rather than the capability.
Questions that separate an agent from a relabelled chatbot
- Which tools can it call?Name the systems it reads and the ones it changes.
- Who decides the next step?The model, from results — or a fixed script behind a chat box?
- What waits for a person?Which actions need approval, and where does the approval appear?
- Whose permissions does it use?The person asking, or one shared service account?
- What is recorded?Every call, argument and decision, retrievable for review.
- How is it tested?A set of known cases with expected outcomes, run before every change.
Decision guide
Choosing between conversational AI and agentic AI for a job.
Anthropic’s advice is to find the simplest solution that works and add autonomy only when it pays for itself, because agents trade latency and cost for task performance. Two questions place most jobs.
High cost of a mistake
Grounded answers, citedPolicy questions, contract lookups. Conversational AI with sources and read permissions.conversational
Agent with approvalsRefunds, payments, record changes. Agentic AI where the risky tools wait for a person.agentic + people
Low cost of a mistakeSelf-service answersOpening hours, how-to questions. A conversational assistant is enough.conversational
Autonomous agentTagging, routing, drafting. Agentic AI that acts alone and is reviewed in samples.agentic
Answer a questionFinish a taskAfter the system understands the request, what should happen?
A person reads an answerConversational AI. Invest in grounding, citations and per-person read permissions.
One fixed procedure runsA workflow: the steps are known, so code them and let a model fill in the judgement calls.
The steps depend on what it findsAgentic AI, with granted tools, approvals on the risky ones and a full record.
On Botminds
How Botminds combines conversational AI and agentic AI in one agent.
On Botminds every conversation runs on a full agent. The same agent answers from your data with sources, and acts through tools you grant — with the actions you choose waiting for a person inside the thread.
- Message
Typed, dictated, or with files attached.
- Context
Who is asking, the page they are on, the conversation so far.
- Search
The agent’s sources, within what this person may read.
- Act
Granted tools; the ones you chose wait for a person.
- Answer
In the person’s language, with its sources, kept with the conversation.
One agent, every doorthe same knowledge, tools and record wherever people reach it
Every screenan assistant beside any page that knows the selection
Email and Teamsa mailbox of its own and a presence in Teams
Approvals in the threadapprove, reject or edit a waiting action
Sources shownthe documents and pages each answer used
An APIOpenAI-compatible endpoints for your own apps
On the recordevery turn, tool call and approval kept with the run
Read more in conversational AI on Botminds, and see how a waiting action works in approvals inside a flow.
Questions people ask
Conversational AI vs agentic AI: common questions.
The technology behind itConversational AIAgents in every app, inbox and channel→Conversational AI vs agentic AI — which is better?
Neither in general. In conversational AI vs agentic AI, conversational systems are simpler, cheaper and safer when a person only needs an answer; agentic systems are worth their extra cost and controls when the job is to finish a task across systems.
Is ChatGPT conversational AI or agentic AI?
A chat assistant answering from its training and the files you give it is conversational AI. When the same assistant is allowed to browse, run code or call tools and decide its next step from the results, it is behaving agentically. The label depends on what it is allowed to do.
What is the difference between conversational AI, generative AI and agentic AI?
Generative AI is the underlying capability to produce text, images or code. Conversational AI applies it to dialogue. Agentic AI applies it to pursuing goals through tools and multiple steps. Most agentic systems are generative and conversational too.
Is a chatbot an AI agent?
Usually not. A chatbot replies; an agent decides on actions, calls tools and adjusts from the results. Gartner warns of “agent washing” — chatbots and assistants rebranded as agents without substantial agentic capability.
What are examples of agentic AI?
An agent that investigates why an invoice is unmatched and drafts the supplier note; one that triages incoming emails into cases and routes them; one that answers a customer and issues a refund after a team member approves it.
Is agentic AI riskier than conversational AI?
Yes, because its mistakes are actions, not just words. That is why agentic systems need tool grants per agent, approvals on risky actions, the asking person’s permissions and a full record of every step.
Can conversational AI become agentic?
Yes — by giving it tools, the permissions to use them and a loop that lets the model act on results. The conversation stays the same; what changes is what the system may do after it understands the request.
Sources
- Anthropic: Building effective agents (December 2024)anthropic.com
- Yao et al.: ReAct, reasoning and acting in language models (2022)arxiv.org
- Sumers et al.: Cognitive Architectures for Language Agents (CoALA, 2023)arxiv.org
- IBM: What is conversational AI?ibm.com
- Gartner: Agentic AI will autonomously resolve 80% of common customer service issues by 2029 (March 2025)gartner.com
- Gartner: Over 40% of agentic AI projects will be canceled by end of 2027 (June 2025)gartner.com
Read next
Conversational AI vs chatbotScripted replies, matched intents, and models that understand free text→Agentic AI vs generative AIHow AI that writes becomes AI that acts, and what that adds→AI agents in Microsoft TeamsHow agents reach Teams, the ways to build them, and how admins govern them→Generative UI vs composable UIInterfaces a model writes at run time, and interfaces assembled from parts→All guidesEvery explainer, by topic→
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