Explainer
Generative AI creates content. Agentic AI plans and takes actions to reach a goal. Here is the difference in plain English, with examples, the risks that come with agents, and how to decide where each belongs.
By Christopher Fagan, Managing Partner, PEF Consulting Partners
Generative AI produces new content, such as text, images, code or summaries, in response to a prompt. Agentic AI uses models like these to pursue a goal: it plans steps, calls tools and systems, checks the results and acts, often with limited human input. Gartner describes agentic AI as systems that autonomously plan and take actions to meet user defined goals.
| Generative AI | Agentic AI | |
|---|---|---|
| What it does | Creates content in response to a prompt | Plans and carries out multistep tasks towards a goal |
| Typical output | A draft, an answer, an image, some code | A completed task: a ticket resolved, a record updated, a report sent |
| The human role | Reviews and uses the output | Sets the goal, approves key actions and monitors results |
| Main risks | Inaccurate or invented content, data leakage, intellectual property | All of those, plus actions in the wrong place, runaway costs and unclear accountability |
| Examples | Drafting an email, summarising a contract | Handling an expense claim end to end, reconciling accounts and chasing missing invoices |
An AI agent is a single piece of software that acts for a user, such as a Copilot agent that answers HR questions and books leave. Agentic AI is the broader approach, often with several agents working together. In practice the terms are used loosely.
Gartner predicted in June 2025 that over 40% of agentic AI projects will be cancelled by the end of 2027 because of escalating costs, unclear business value or inadequate risk controls. The pattern is familiar from earlier waves of automation: projects that start from the technology rather than a measurable process problem.
NIST describes AI agent systems as capable of planning and taking autonomous actions that affect real world systems, which is why governance matters more than with generative tools. The NIST AI risk management framework and a practical AI governance framework give you the structure.
Related reading: Copilot agents explained, the free AI readiness check, the AI roadmap template, AI readiness, governance and implementation.
AI that pursues a goal by planning steps, using tools and systems, checking results and taking actions, rather than only producing content in response to a prompt.
ChatGPT is primarily a generative AI assistant, but it now includes agent features that can browse, use tools and complete tasks, so it can act agentically when those features are used.
An agent that processes an expense claim from receipt to payment, one that triages support tickets and resolves the simple ones, or one that reconciles accounts and chases missing invoices.
It can be, with the right controls: limited permissions, human approval for significant actions, full logging, monitoring and clear accountability for what the agent does.
If you are planning your first agents, start with one measurable process and build the controls in from day one. We can help you choose it and set it up safely.