Generative AI
A model that produces new content in response to a prompt. It answers, and stops.
- Output: text, images, code, audio, video
- Started by a person’s prompt
- One response per request
- A person decides what to do with it
Guide · Agent harness
Agentic AI vs generative AI: from a draft on the screen to work done in your systems.
Generative AI produces content when asked. Agentic AI uses that same generative model to pursue a goal across many steps — reading, calling tools, checking and acting.
Every agent today runs on a generative model; what makes it agentic is the loop, the tools and the permission to act that are built around that model. This guide defines both precisely, shows the loop step by step, works one example through both, and sets out the risks that appear once AI can act.
Updated · 13 sources
Produces contentCompletes the task
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- Text or image generatorOne prompt in, one piece of content out.
- Chat assistantHolds a conversation; a person acts on what it says.
- Assistant with retrievalAnswers from your documents, with citations.
- Agent with approvalsPlans steps and calls tools; a person confirms changes.
- Autonomous agentWorks through a goal on its own, within granted tools.
What is the difference between agentic AI and generative AI?
Generative AI is AI that creates new content — text, images, code, audio — from a prompt. Agentic AI is a system that pursues a goal on someone’s behalf: it plans steps, calls tools and APIs, checks the results and keeps going until the task is done. In agentic AI vs generative AI, the generative model is the engine and the agent is the vehicle built around it: agentic AI adds a loop, tools, memory and the permission to act.
Side by side
Agentic AI vs generative AI, compared dimension by dimension.
NIST’s Generative AI Profile (AI 600-1, July 2024), quoting a 2023 US executive order, defines generative AI as models that “generate derived synthetic content”. Google Cloud defines AI agents as software that uses AI “to pursue goals and complete tasks on behalf of users”. The rows below follow from those two definitions.
Agentic AI
A system that uses a generative model to decide and take the next step toward a goal, many times over.
- Output: a finished task and a record of it
- Started by a goal, an event or a schedule
- Many model calls and tool calls per task
- Acts in systems, within permissions
A responseUnit of workA task
Reactive: waits for a promptInitiativeProactive within its goal: chooses next steps
Optional; mostly noneToolsEssential: search, databases, APIs, other agents
The conversation, if anyStatePlan, progress and memory across steps
A wrong or invented answerFailure modeA wrong action, repeated or compounded
The reader of the outputWho checksBuilt-in checks, approvals and a run record
One model callCost profileMany calls; grows with the task
Mechanics
How agentic AI turns a generative model into a worker.
A generative model on its own predicts the next piece of content. An agent wraps it in a loop: the model proposes a step, software runs it, the result goes back to the model, and the loop continues until a stopping rule is met. ReAct, published in October 2022, showed that interleaving reasoning with actions this way outperformed imitation- and reinforcement-learning methods on interactive tasks.
The agent loop
- GoalA request, an event or a schedule says what done looks like.
- PlanThe model proposes the next step from the goal and what it knows.
- ActSoftware runs the chosen tool: a search, a query, an API call.
- ObserveThe result is added to the model’s context.
- CheckRules, tests or a person confirm the step before the next one.
Back to the plan with what the last step returned — until the goal is met, a person must decide, or a stopping rule ends the run.
The model never runs the tool itself. In function calling, the model returns the name and arguments of a tool; the application executes it and sends back the result. That is why what the application allows decides what an agent can do.
A short history
From generative AI to agentic AI in eight years.
The generative model came first; the agent pattern followed once models could reliably choose tools and a standard appeared for reaching them.
- June 2017The TransformerThe architecture behind today’s large language models is published.
- October 2022ReActResearchers show models that alternate reasoning with actions solve interactive tasks better.
- November 2022ChatGPTGenerative AI reaches the public as a conversational assistant.
- November 2024Model Context ProtocolAn open standard for connecting AI applications to tools and data.
- March 2025Agent toolkitsOpenAI releases the Responses API and its Agents SDK, for building agents with built-in tools and hand-offs.
- August 2025Peak expectationsGartner names AI agents one of the two fastest-advancing technologies on its 2025 Hype Cycle for AI, at the Peak of Inflated Expectations.
One request, two systems
The same supplier email, handled by generative AI and by agentic AI.
A supplier emails: “Please pay our next invoice to the new bank account below.” It is a routine request and a well-known fraud pattern.
Generative AI
- ReadsThe email the person pasted in.
- ProducesA summary and a polite draft reply.
- Leaves to a personChecking the supplier, the account, the history, and updating the record.
- RiskThe draft sounds right whether or not the request is genuine.
- Triggered
The email arrives in the payables mailbox.
- Looks up
Finds the supplier and its bank details on record.
- Compares
Sees a changed account and a sender domain that differs by one letter.
- Proposes
Holds the next payment and opens a call-back check.
- Records
Keeps each step and the reviewer’s decision with the run.
Both used the same kind of model to read the email. The agent differs because it was given tools to look things up, a rule that payment changes wait for a person, and a record of what it did.
What the analysts report
Agentic AI adoption: fast interest, uneven results.
The forecasts are large and the warnings are blunt. Read them together: most organisations are experimenting, and many projects will be stopped before they pay off.
Agent washing. Gartner’s term for chatbots, assistants and robotic process automation rebranded as agents. The test is simple: does the model choose and take steps toward a goal, or does it only generate a reply?
Controls
What agentic AI adds to the generative AI risk list.
Generative AI risks are about content: wrong, invented or harmful output. Once AI acts, the risks are about actions. Each one needs a control that sits outside the model.
Controls an agent needs beyond those for generative AI
- Tool permissions per agentAn agent reaches only the tools granted to it, checked on every call.
- Approval before changePayments, emails and record updates wait for a person where you choose.
- Untrusted contentText inside a document or web page can try to redirect an agent; treat it as data.
- Stopping rulesStep and spend limits per run, so a loop cannot run up cost unseen.
- A run recordEvery step, tool call and decision kept, so an action can be explained later.
- Measured before releaseTest the whole task against known cases, not single answers.
Decision guide
Agentic AI vs generative AI: which one a task needs.
Most work needs both: a generative step to read or write, inside an agent or a workflow that decides what happens next. Ask what should happen after the model has spoken.
After the model produces its output, what happens?
A person reads it and decidesGenerative AI is enough: drafting, summarising, translating, answering questions with citations.
The same fixed steps, every timePut a generative step inside a workflow: the order is fixed and the model reads or classifies.Read more →
It depends on what it findsUse an agent: it chooses the next tool from the result, within permissions and approvals.Read more →
On Botminds
How Botminds runs generative and agentic work on one harness.
On Botminds, the same agent can answer in a moment or plan and act over many steps. The agent harness decides what the model sees and what it may touch, whichever way it runs.
Botminds agent harnesseverything around the model, for every agent
Fast and thinkinga quick answer, or a planned, checked run; Auto picks per message
Autonomous runsagents that work through a task on their own
Tools by grantchecked before every call; read-only basics by default
Approvalsapprove, reject or edit, on the tools you choose
Structured outputtyped fields against a schema for the next step
Run recordrequest, tool calls, approvals, answer and cost
See the agent harness for the full design, and agent varieties in the Academy for when to use each kind of agent.
Questions people ask
Agentic AI vs generative AI: common questions.
The technology behind itAgent harnessThe runtime around every agent→What is the difference between agentic AI and generative AI?
In agentic AI vs generative AI, generative AI creates content from a prompt and stops. Agentic AI uses a generative model inside a loop to pursue a goal: it plans steps, calls tools, checks results and acts in systems until the task is done.
Is ChatGPT generative AI or agentic AI?
ChatGPT began in November 2022 as a generative AI assistant. Features that let it browse, run code or take actions with tools add agentic behaviour on top of the same generative model. Whether a given use is agentic depends on whether the model chooses and takes steps toward a goal.
Is agentic AI a type of generative AI?
Agentic AI is built on generative AI. The language model inside an agent is generative; the agent adds the loop, the tools, memory and permission to act. You can have generative AI with no agent, but today’s agents all use a generative model.
What are examples of agentic AI?
An agent that triages supplier emails and holds suspicious payment changes for review; one that reconciles invoices against purchase orders; a research agent that searches, reads and cites sources; a support agent that looks up an order and issues a refund within policy.
What is agentic AI vs generative AI vs predictive AI?
Predictive AI estimates a value or class from data — a fraud score, a demand forecast. Generative AI creates new content. Agentic AI acts toward a goal, and may use both: a prediction to decide, generation to write.
Is agentic AI riskier than generative AI?
Its risks are different. Generative AI can produce a wrong answer; an agent can take a wrong action, and repeat it. Agents therefore need tool permissions, approvals before changes, stopping rules and a record of every step.
What is agent washing?
Agent washing is Gartner’s term for rebranding chatbots, assistants or robotic process automation as agentic AI without real agentic capability. Gartner estimated in 2025 that only about 130 of thousands of vendors offered genuine agentic AI.
Do I need agentic AI if generative AI already works?
Not always. If a person reads the output and decides, generative AI is enough. Agentic AI pays off when the next step depends on what the model finds and the work crosses several systems.
Sources
- NIST AI 600-1: Generative AI Profile (July 2024)nvlpubs.nist.gov
- Google Cloud: What are AI agents?cloud.google.com
- OpenAI: A practical guide to building agentscdn.openai.com
- IBM Think: Agentic AI vs generative AIibm.com
- Vaswani et al.: Attention Is All You Need (2017)arxiv.org
- Yao et al.: ReAct, reasoning and acting in language models (2022)arxiv.org
- OpenAI: Introducing ChatGPT (November 2022)openai.com
- Anthropic: Introducing the Model Context Protocol (November 2024)anthropic.com
- OpenAI: New tools for building agents (March 2025)openai.com
- OpenAI: Function calling guidedevelopers.openai.com
- Gartner: over 40% of agentic AI projects will be canceled by end of 2027 (June 2025)gartner.com
- Gartner: Hype Cycle identifies top AI innovations in 2025 (August 2025)gartner.com
- McKinsey: The state of AI in 2025mckinsey.com
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