Three terms get used as if they meant the same thing: generative AI, AI agents and agentic AI. They are related, but they are not interchangeable, and the difference decides what you can safely hand over to software. This guide gives a one-line answer for each, then shows a real agent run step by step so the difference stops being abstract.
The short answer
Generative AI is a model that creates new content from a prompt: it writes the email, summarises the PDF, drafts the product description. One request in, one piece of content out.
An AI agent is a system built around such a model. It receives a goal, decides which tool to call (search the catalogue, check availability, create a booking), reads the result and decides again, until the goal is reached or it hands over to a person.
Agentic AI is the umbrella term for systems that behave this way. JetBrains puts it neatly: "Agentic AI describes how a system behaves; an AI agent is what the system is." In practice, when a vendor says "agentic AI", they mean software built from one or more AI agents.
- Aspect: What it doesChatbot: Answers questions in a conversationGenerative AI: Creates content from a promptWorkflow with an LLM: Runs fixed steps, with a model inside some of themAI agent: Pursues a goal, choosing its own steps
- Aspect: Who decides the next stepChatbot: A script or the model, inside the chatGenerative AI: The userWorkflow with an LLM: The developer, in codeAI agent: The model, within limits you set
- Aspect: Touches your systemsChatbot: Rarely; usually read-onlyGenerative AI: NoWorkflow with an LLM: Yes, at fixed pointsAI agent: Yes, through the tools you give it
- Aspect: Typical exampleChatbot: FAQ bot on a websiteGenerative AI: Writing a product descriptionWorkflow with an LLM: Classify a ticket, then route itAI agent: Rebook a cancelled reservation end to end
- Aspect: Main riskChatbot: A wrong answerGenerative AI: A wrong or bland textWorkflow with an LLM: A broken stepAI agent: A wrong action (money, data, a customer)
| Aspect | Chatbot | Generative AI | Workflow with an LLM | AI agent |
|---|---|---|---|---|
| What it does | Answers questions in a conversation | Creates content from a prompt | Runs fixed steps, with a model inside some of them | Pursues a goal, choosing its own steps |
| Who decides the next step | A script or the model, inside the chat | The user | The developer, in code | The model, within limits you set |
| Touches your systems | Rarely; usually read-only | No | Yes, at fixed points | Yes, through the tools you give it |
| Typical example | FAQ bot on a website | Writing a product description | Classify a ticket, then route it | Rebook a cancelled reservation end to end |
| Main risk | A wrong answer | A wrong or bland text | A broken step | A wrong action (money, data, a customer) |
What one agent run looks like
Most articles stop at definitions. Here is what actually happens when an agent handles a request. A guest asks an assistant: "Book a table for 4 at Ada Bistro tonight, around 8." (Ada Bistro is a fictional restaurant.) The agent has three tools: check_availability, create_booking and send_confirmation.
user Book a table for 4 at Ada Bistro tonight, around 8.
model → call check_availability { date: "2026-10-30", party: 4, around: "20:00" }
tool ← { slots: ["19:30", "20:45"] } # 20:00 is full
model 20:00 is taken. 19:30 or 20:45? # asks, because the goal changed
user 20:45 is fine.
model → call create_booking { date: "2026-10-30", time: "20:45", party: 4 }
tool ← { booking_id: "AB-1042", status: "confirmed" }
model → call send_confirmation { booking_id: "AB-1042", channel: "email" }
tool ← { sent: true }
model Booked: Ada Bistro, tonight 20:45, table for 4. Confirmation sent.Three things in this trace make it agentic. The model chose which tool to call and in what order. It read the result and changed course when 20:00 was full. And it stopped to ask instead of guessing, because the booking would commit the customer to something. A chatbot would have answered "Here is the booking link". Generative AI would have written a nice sentence about Ada Bistro.
Red Hat describes the mechanical difference well: "While generative AI typically performs inference once to create content … agentic AI often runs the inference loop repeatedly." Every loop costs tokens and time, which is why agents are slower and more expensive than a single call.
The spectrum: from chatbot to multi-agent system
It helps to see these as steps on one ladder rather than separate categories. Each step gives the model more control and adds more risk.
- Single model call. A prompt, maybe with retrieved documents (RAG). Answers questions, drafts text.
- Workflow. Your code runs fixed steps; a model handles some of them (classify, extract, draft). Predictable and cheap.
- Agent. The model decides the steps and calls tools in a loop, within the permissions you give it.
- Multi-agent system. Several agents with separate roles (research, drafting, checking) hand work to each other, usually under an orchestrator.
Anthropic draws the line between steps 2 and 3 clearly: "Workflows are systems where LLMs and tools are orchestrated through predefined code paths. Agents, on the other hand, are systems where LLMs dynamically direct their own processes and tool usage." We use the same line at Semroi, because it is the one that matters when something goes wrong: in a workflow you can point at the step that failed; in an agent you read the trace.
Why the definitions online disagree
If you read three of the top-ranking articles you will find three definitions of agentic AI. They are not wrong, they describe different levels:
- Agentic AI as a behaviour (IBM, JetBrains): any system that plans, uses tools and adapts. One agent can be agentic.
- Agentic AI as an orchestration layer (several vendor blogs): agentic AI means many agents coordinated together; a single agent is "just" an AI agent.
- Workflow vs agent (Anthropic): the useful question is who controls the steps, your code or the model.
An academic survey, "AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges" (arXiv:2505.10468), follows the second view: single task-specific agents on one side, collaborative multi-agent systems on the other. For buying decisions the third view is the most useful, because it tells you how predictable the system will be.
Agentic AI vs generative AI
Generative AI is a capability; agentic AI is a way of using it. Every AI agent today runs on a generative model, so the two are not competitors. The differences show up in four places:
- Aspect: OutputGenerative AI: Content: text, image, codeAgentic AI: An outcome: a booking made, a ticket closed, a report filed
- Aspect: ControlGenerative AI: The user prompts each stepAgentic AI: The system plans steps toward a goal
- Aspect: AccessGenerative AI: Needs no access to your systemsAgentic AI: Needs tools, accounts and permissions
- Aspect: Cost and speedGenerative AI: One model callAgentic AI: Many calls per task, plus tool latency
| Aspect | Generative AI | Agentic AI |
|---|---|---|
| Output | Content: text, image, code | An outcome: a booking made, a ticket closed, a report filed |
| Control | The user prompts each step | The system plans steps toward a goal |
| Access | Needs no access to your systems | Needs tools, accounts and permissions |
| Cost and speed | One model call | Many calls per task, plus tool latency |
A useful rule: if the job ends with a person copying the output somewhere, you need generative AI. If the job ends with the software doing it, you are looking at an agent.
AI agent vs chatbot
A chatbot talks; an agent acts. Modern chatbots run on the same language models as agents, so the difference is not intelligence, it is access. Elementum makes the stakes concrete: "A chatbot that gives an incorrect answer creates a customer service issue. An agent that executes an incorrect transaction can create immediate financial loss." That is why an agent needs things a chatbot does not: scoped permissions, confirmations before irreversible actions, logs of every tool call and tests before launch.
For a sector example, see our guide to AI agents in hotels, where the line between answering questions and changing reservations is the whole design problem.
How to tell if a product is really agentic
Gartner warned in June 2025 that "many vendors are contributing to the hype by engaging in 'agent washing'" and estimated that "only about 130 of the thousands of agentic AI vendors are real." Five questions separate a real agent from a relabelled chatbot:
- Which actions can it take? Ask for the list of tools. If the answer is "it answers questions", it is a chatbot.
- Who decides the order of steps? If every path is drawn in a flow builder, it is a workflow (which may be exactly what you need).
- What happens when a step fails? A real agent reads the error and retries, picks another route or escalates.
- Can I see the trace? You should be able to read every tool call, input and result of a run.
- How was it tested? Ask for the evaluation set and the pass rate on real requests, not a demo.
Do you need an agent at all?
Often not. Anthropic's advice is to find "the simplest solution possible" and notes that "optimizing single LLM calls with retrieval and in-context examples is usually enough." Gartner predicts that over 40% of agentic AI projects will be cancelled by the end of 2027 "due to escalating costs, unclear business value or inadequate risk controls", and adds that "many use cases positioned as agentic today don't require agentic implementations."
If you can draw the process as a flowchart that rarely changes, build a workflow. Reach for an agent when the steps depend on what the software finds along the way.
An agent earns its cost when the task has many possible paths, depends on messy inputs (emails, free text, documents) and ends in an action across several systems. Rebooking a cancelled stay, triaging a request for quotation, or reordering a customer's usual items are good examples. Answering opening hours is not.
Examples by sector
- Sector: E-commerceGenerative AI: Writes product descriptionsAI agent: Reorders a customer's last purchase, checks stock, applies the right discount
- Sector: HospitalityGenerative AI: Drafts replies to reviewsAI agent: Changes a reservation, updates the PMS, sends the confirmation
- Sector: ClinicsGenerative AI: Summarises a patient leafletAI agent: Moves an appointment to a free slot and notifies the doctor
- Sector: B2B SaaSGenerative AI: Drafts release notesAI agent: Reads a support ticket, checks the account, applies a fix or escalates
| Sector | Generative AI | AI agent |
|---|---|---|
| E-commerce | Writes product descriptions | Reorders a customer's last purchase, checks stock, applies the right discount |
| Hospitality | Drafts replies to reviews | Changes a reservation, updates the PMS, sends the confirmation |
| Clinics | Summarises a patient leaflet | Moves an appointment to a free slot and notifies the doctor |
| B2B SaaS | Drafts release notes | Reads a support ticket, checks the account, applies a fix or escalates |
All four agent examples share one requirement: the business must expose those actions to the agent safely. Increasingly that is done through an MCP server, which lets assistants such as ChatGPT, Claude and Gemini call your tools under the customer's own account. If you want to build one, our step-by-step guide on how to build an AI agent covers the process and the costs.
Frequently asked questions
What is the difference between agentic AI and AI agents?
An AI agent is a system: a model with tools that pursues a goal. Agentic AI describes the behaviour (planning, tool use, adapting) and is often used for the whole field or for systems made of several agents. Every AI agent is agentic AI; not every agentic system is a single agent.
Is ChatGPT generative AI or agentic AI?
Both, depending on how you use it. Answering a question or writing a text is generative AI. When ChatGPT uses tools on your behalf, such as browsing, running code or calling an app connected to your account, it is acting as an agent.
Is a chatbot an AI agent?
Not by itself. A chatbot answers in a conversation. It becomes an agent when it can call tools that change something, such as creating a booking or updating a record, and decides on its own when to call them.
Can a chatbot be upgraded into an AI agent?
Yes, by giving it tools, permissions and a loop that lets it act on results. That upgrade also needs what a chatbot never needed: scoped access, confirmations before irreversible actions, logging and tests on real requests.
Are agentic AI systems fully autonomous?
Rarely, and they should not be by default. Well-built agents act within the permissions they are given and ask a person before actions that cost money, change a customer's data or cannot be undone.
What are the risks of agentic AI?
Wrong actions rather than wrong words: a booking for the wrong date, a refund to the wrong account, data shown to the wrong person. Prompt injection through content the agent reads is the other major risk. Scoped permissions, confirmations, logs and evaluation sets keep them manageable.
What is agentic AI in simple terms?
Software that does not just answer but gets something done: it decides the steps, uses tools such as your booking or order system, checks the results and stops to ask a person when it should.
Sources
- Anthropic: Building effective agents
- Gartner: over 40% of agentic AI projects will be canceled by end of 2027 (25 June 2025)
- Sapkota et al.: AI Agents vs. Agentic AI, a conceptual taxonomy (arXiv:2505.10468)
- Red Hat: Agentic AI vs. generative AI
- JetBrains: Agentic AI vs AI agents
- Elementum: AI agents vs chatbots
- AI agents
- Agentic AI
