Agentic AI vs Generative AI: Definitions and Differences

Agentic AI vs Generative AI: Definitions and Differences

Generative AI produces content such as text, images, audio and code in response to a prompt. Agentic AI places a generative model inside a loop with tools, so the system can plan steps, act in other software and check its results with limited supervision. Agentic AI is built on generative AI. The practical difference is who decides the next step: a person or your code, or the model.

What is generative AI?

Generative AI is the class of models that create new content from patterns learned in training data. NIST's Generative AI Profile, published in July 2024, uses the definition from US Executive Order 14110: "the class of AI models that emulate the structure and characteristics of input data in order to generate derived synthetic content. This can include images, videos, audio, text, and other digital content."

IBM's explainer, published February 11, 2025 and updated August 10, 2026, gives the working version: "generative AI creates new content such as text, images, video, audio and software code."

The pattern is one request and one response. A person, or the application around the model, reads the output and decides what happens next.

What is agentic AI?

Agentic AI is a system in which a generative model controls a multi-step task and takes actions through tools. A precise public definition comes from NIST's Center for AI Standards and Innovation, in a request for information published in the Federal Register on January 8, 2026:

"AI agent systems are capable of planning and taking autonomous actions that impact real-world systems or environments. AI agent systems consist of at least one generative AI model and scaffolding software that equips the model with tools to take a range of discretionary actions."

Vendor definitions agree on the core.

OpenAI's guide also marks the boundary. Applications that integrate language models "but don’t use them to control workflow execution", such as simple chatbots and classifiers, "are not agents."

Agentic AI vs generative AI: what is the difference?

The difference is control and action. A generative system returns content and stops. An agentic system keeps going: it chooses steps, calls tools, reads the results and decides whether the task is complete.

Generative AI Agentic AI
Purpose Create or transform content Complete a task or reach a goal
Who decides the next step The user or the application code The model, inside limits set by the developer
Unit of work One prompt, one response A loop of plan, act, check, repeat
Access to other systems None required Tools, APIs, databases, files
Output Text, image, audio, video, code Content, a decision, or an action taken in another system
Human role Prompts and reviews each output Sets the goal, approves high-risk actions, reviews results
Relative token use Baseline About 4× a chat interaction, by Anthropic's measurement
Main added risk Inaccurate content Wrong or unauthorized actions, compounding errors
Example Draft a reply to a customer email Read the email, look up the order, issue the refund, send the reply

IBM summarizes the autonomy row in one sentence: generative AI "is primarily reactive, while agentic AI can proactively work toward a goal." The token figure comes from Anthropic's engineering post of June 13, 2025: "agents typically use about 4× more tokens than chat interactions, and multi-agent systems use about 15× more tokens than chats."

The two are layers of one stack. In NIST's wording, an agent system contains "at least one generative AI model". Remove the tools and the loop, and what remains is generative AI.

Agentic AI vs AI agents: are they the same thing?

In most business and policy writing they mean the same thing. Some sources use "AI agent" for one system and "agentic AI" for the broader approach or for several agents working together.

NIST treats the terms as synonyms: "Other terms used to refer to AI agent systems include AI agents and agentic AI." Google Cloud separates them: "AI agents are the building blocks of agentic AI." IBM takes the same line, describing AI agents as "one component of the broader concept of agentic AI."

An academic review draws a sharper line. Sapkota et al., first posted May 15, 2025, characterize AI agents as "modular systems driven and enabled by LLMs and LIMs for task-specific automation" and agentic AI as a shift "marked by multi-agent collaboration, dynamic task decomposition, persistent memory, and coordinated autonomy."

The sources do not agree on the vocabulary. In a specification or a contract, state what the system may do: which tools it can call, which actions need approval and when it stops. That description matters more than the label.

Agents vs workflows: what are agentic workflows?

A workflow follows steps the developer fixed in code. An agent chooses its own steps. Anthropic drew this line in "Building Effective AI Agents", published December 19, 2024:

  • "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, maintaining control over how they accomplish tasks."

Anthropic groups both under "agentic systems". The term "agentic workflow" is used more loosely elsewhere. IBM's definition, published March 7, 2025 and updated July 8, 2026, reads: "Agentic workflows are AI-driven processes where autonomous AI agents make decisions, take actions and coordinate tasks with minimal human intervention."

Andrew Ng set out the idea early. In a letter published March 20, 2024, he contrasted one-pass generation with an iterative loop and named four design patterns: reflection, tool use, planning and multi-agent collaboration. He reported that on the HumanEval coding benchmark, "GPT-3.5 (zero shot) was 48.1% correct. GPT-4 (zero shot) does better at 67.0%", while "wrapped in an agent loop, GPT-3.5 achieves up to 95.1%."

A useful reading is a scale of autonomy. A single model call sits at one end. Fixed workflows with several model calls sit in the middle. Agents that plan their own path sit at the other end.

What are concrete examples of each?

Generative AI examples end with content a person uses. Agentic examples end with a completed task.

Generative AI

  • Summarizing a contract or a meeting transcript.
  • Drafting marketing copy, product descriptions or email replies.
  • Generating images, voice or video from a text description.
  • Suggesting code as a developer types.
  • Answering questions from company documents with retrieval. NIST's request for information places "AI chatbots or retrieval-augmented generation systems that are not orchestrated to act autonomously" outside its scope for agent systems.

Workflows

  • Routing: Anthropic's example is "Directing different types of customer service queries (general questions, refund requests, technical support) into different downstream processes, prompts, and tools."
  • A document pipeline that extracts fields, validates them against rules and drafts a summary in a fixed order.

Agents

  • OpenAI's examples of agent tasks: "resolving a customer service issue, booking a restaurant reservation, committing a code change, or generating a report."
  • A coding agent that edits files, runs the tests and fixes what fails.
  • A research agent that plans searches, reads sources and writes a cited report.
  • A phone agent that answers a call, checks a calendar and books the appointment.

How do you decide which one a project needs?

Choose the least autonomous design that completes the task. Anthropic's advice is to find "the simplest solution possible", and it adds: "This might mean not building agentic systems at all." For many applications, it says, "optimizing single LLM calls with retrieval and in-context examples is usually enough."

If the project looks like this Start with
The output is content and a person takes the next step Generative AI: a single model call, with retrieval if it needs your data
The steps are known in advance and rarely change A workflow with model calls at fixed points
The steps cannot be predicted and the system must act in other software An agent with tools, limits and human approval for risky actions
Rules cover every case reliably Conventional software with no language model

OpenAI's guide lists three signs that a process suits an agent: complex decision-making that involves judgment and exceptions, rules that have become difficult to maintain, and heavy reliance on unstructured data. It adds a caution: "Before committing to building an agent, validate that your use case can meet these criteria clearly. Otherwise, a deterministic solution may suffice."

IBM gives the short version: "If the goal is to create or transform content, summarize information or assist users with knowledge-based tasks, generative AI is often the right choice. If the objective involves completing multi-step workflows, making decisions or interacting with other systems, agentic AI might provide greater value."

What does agentic AI add in cost and risk?

It adds token cost, new failure modes and a security surface that content generation does not have.

  • Cost. Each step of the loop is another model call. Anthropic's figure is about 4× the tokens of a chat interaction for a single agent.
  • Compounding errors. Anthropic warns that "The autonomous nature of agents means higher costs, and the potential for compounding errors", and recommends "extensive testing in sandboxed environments, along with the appropriate guardrails."
  • Security. NIST notes that agent systems "may be susceptible to hijacking, backdoor attacks, and other exploits." A system that can act can be tricked into acting.
  • Integration work. MIT Sloan reported on February 18, 2026 on research by Kate Kellogg and colleagues into a clinical AI agent, in which "80% of the work was consumed by unglamourous tasks associated with data engineering, stakeholder alignment, governance, and workflow integration."

These costs are acceptable when the task is valuable and hard to automate another way. They are a poor trade for a task that a single prompt already handles.

Key takeaways

  • Generative AI creates content from a prompt. Agentic AI uses a generative model to plan and take actions through tools.
  • NIST's January 2026 definition: agent systems "consist of at least one generative AI model and scaffolding software that equips the model with tools".
  • "AI agent" and "agentic AI" are used as synonyms by NIST. Google Cloud and IBM treat agents as components of agentic AI.
  • Anthropic separates workflows, which follow predefined code paths, from agents, which direct their own process.
  • Agents use about 4× the tokens of a chat interaction by Anthropic's measurement, and add action and security risk.
  • Pick the least autonomous design that completes the task.

Frequently asked questions

Is agentic AI a type of generative AI?

Agentic AI is built on generative AI. Google Cloud calls it "a subset of generative AI", and NIST's definition says an agent system contains at least one generative model plus software that gives it tools. The generative model produces the plans and tool calls. The surrounding software executes them.

What is the difference between an AI agent and agentic AI?

Often there is none, and NIST lists the terms as alternatives. Where writers do separate them, an AI agent is a single system that pursues a task with tools, and agentic AI is the broader approach, which can include several agents coordinated together.

What are agentic workflows?

They are multi-step processes in which a language model does more than answer once: it plans, uses tools, reviews its own output or hands work to other agents. Andrew Ng's four patterns from March 2024 are reflection, tool use, planning and multi-agent collaboration.

Is a RAG chatbot agentic AI?

Usually it is generative AI with retrieval. It finds passages and writes an answer in a fixed sequence. It becomes agentic when the model decides which searches to run, judges the results and repeats until it has enough, or when it can take actions beyond answering.

Does every AI project need agents?

Most do not need them at the start. Anthropic, which builds agent tooling, recommends starting with simple prompts and adding "multi-step agentic systems only when simpler solutions fall short."

Easital Technologies Ltd. builds both kinds of system: generative AI features inside software products, and AI agents, agentic workflows and tool-calling systems that act on a user's behalf. One example is Calldone, voice agents that answer business phone calls and book appointments, built by Easital for a client in the United States. Related pages: generative AI development, AI agent development, AI automation services and the Calldone case study.

Sources

All sources were opened and checked on October 3, 2026.

  1. NIST, "Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile" (NIST AI 600-1), July 2024. https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf
  2. NIST Center for AI Standards and Innovation, "Request for Information Regarding Security Considerations for Artificial Intelligence Agents", Federal Register, January 8, 2026. https://www.federalregister.gov/documents/2026/01/08/2026-00206/request-for-information-regarding-security-considerations-for-artificial-intelligence-agents
  3. IBM, "Agentic AI vs. generative AI", IBM Think, published February 11, 2025, updated August 10, 2026. https://www.ibm.com/think/topics/agentic-ai-vs-generative-ai
  4. OpenAI, "A practical guide to building agents", April 2025. https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf
  5. Amazon Web Services, "What is Agentic AI?", undated, opened October 3, 2026. https://aws.amazon.com/what-is/agentic-ai/
  6. Google Cloud, "What is agentic AI?", undated, opened October 3, 2026. https://cloud.google.com/discover/what-is-agentic-ai
  7. Anthropic, "Building Effective AI Agents", December 19, 2024. https://www.anthropic.com/engineering/building-effective-agents
  8. Anthropic, "How we built our multi-agent research system", June 13, 2025. https://www.anthropic.com/engineering/multi-agent-research-system
  9. Sapkota, Roumeliotis, Karkee, "AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges", arXiv 2505.10468, May 15, 2025. https://arxiv.org/abs/2505.10468
  10. IBM, "What are agentic workflows?", IBM Think, published March 7, 2025, updated July 8, 2026. https://www.ibm.com/think/topics/agentic-workflows
  11. Andrew Ng, "Four AI Agent Strategies That Improve GPT-4 and GPT-3.5 Performance", The Batch, DeepLearning.AI, March 20, 2024. https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/
  12. MIT Sloan School of Management, "Agentic AI, explained", February 18, 2026. https://mitsloan.mit.edu/ideas-made-to-matter/agentic-ai-explained

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