Agentic AI is software that pursues a goal on your behalf. It plans a sequence of steps, takes actions using external tools, checks the results, and adjusts until the job is done. Where a chatbot answers a single prompt, an agent runs a loop: reason, act, observe, repeat. Gartner named agentic AI the top strategic technology trend for 2025 and predicts a third of enterprise software will embed it by 2028.
What is agentic AI?
The clearest working definition comes from Anthropic, which draws a line between two things people lump together. In Building Effective Agents (December 2024), workflows are "systems where LLMs and tools are orchestrated through predefined code paths", while agents are "systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks".
That distinction is the whole game. A workflow follows a script you wrote. An agent decides its own next step. Gartner frames agentic AI as systems that "autonomously plan and take actions to meet user-defined goals", describing it as a virtual workforce that can offload and augment human work.
The word that matters is agency: the capacity to act independently towards a goal, rather than waiting to be told each move. You give an agent an objective, not a set of instructions.
How does agentic AI actually work?
Under the surface, an agent is a large language model wired into a loop with tools and memory. Most systems follow four repeating stages, often summarised as perceive, reason, act, and learn.
Reason and plan
The model breaks the goal into steps and decides what to do first. The seminal work here is the ReAct paper (Yao et al., 2022), which showed that interleaving reasoning traces with actions lets a model "induce, track, and update action plans as well as handle exceptions". That reason-then-act pattern underpins almost every agent framework in use today.
Act through tools
The agent calls something outside itself: a search API, a database query, a code run, an email send. This is the step that separates an agent from a chatbot. A chatbot tells you what to do. An agent does it, by triggering real actions in real systems.
Observe and adjust
The agent reads the result of its action, checks it against the goal, and decides whether to continue, retry, or change course. This feedback step is what lets an agent handle a task that takes ten steps rather than one, and recover when a step fails.
How is agentic AI different from generative AI?
Generative AI creates content in response to a prompt. Agentic AI uses that content to make decisions and complete tasks. IBM puts it neatly: generative models are reactive to the user's input, whereas agentic systems are proactive, working towards a goal across many steps. One is a sprint, a single call and a single response. The other is a relay that keeps running until the task is finished.
Dimension | Generative AI | Agentic AI |
|---|---|---|
Core job | Produce content from a prompt | Achieve a goal across multiple steps |
Interaction | You prompt, it responds, you act | You set a goal, it plans and acts |
Autonomy | None between prompts | Runs a loop until the goal is met |
Tools | Rarely; text in, text out | Calls APIs, databases, other software |
Typical output | A draft, an answer, an image | A completed task and its side effects |
Main failure mode | A wrong or made-up answer | A wrong action taken automatically |
The practical takeaway: generative AI carries the risk of a bad answer you can ignore. Agentic AI carries the risk of a bad action that has already happened. That raises the stakes on oversight, which is why the governance question matters more here than with a chatbot.
What can agentic AI do today, and how widely is it used?
Adoption is real but early, and the gap between hype and deployment is wide. McKinsey's State of AI (November 2025) found 23% of organisations are scaling an agentic AI system in at least one function, with a further 39% experimenting. In any single function, though, no more than about 10% report scaling agents. Most deployments are narrow.
In the UK the baseline is lower still. ONS data shows 23% of UK businesses used any form of AI in late September 2025, up from 9% when the question was introduced in 2023. Agentic AI is a small slice of that. The market is growing fast: MarketsandMarkets projects the AI agents market rising from $7.84bn in 2025 to $52.62bn by 2030.
Where agents earn their keep today tends to be bounded, high-volume workflows: triaging support tickets, reconciling invoices, researching and drafting outreach, or running multi-step data lookups. The common thread is a clear goal, good data, and a tool the agent can safely call.
What are the risks and limits of agentic AI?
The headline caution comes from Gartner, which predicts over 40% of agentic AI projects will be cancelled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls. It also warns of agent washing: existing chatbots and automation rebadged as agents. Not everything labelled agentic actually is.
The real limits are practical. Agents compound errors when a wrong early step feeds later ones. They cost more to run than a single model call because they make many. And an agent that can act can act wrongly, at machine speed, before anyone notices. Human oversight and clear stop conditions are not optional extras. They are the design.
What should a UK business do about agentic AI in 2026?
Start with a bounded, low-risk workflow where the goal is clear and a mistake is cheap to catch. Prove the value on one task before scaling. Keep a human in the loop on anything that touches customers, money, or regulated decisions, and log every action the agent takes.
Treat the governance question as part of the build, not a bolt-on. If an agent makes a decision on your behalf, your organisation still owns that decision. The businesses getting value from agentic AI are the ones that scoped it tightly, measured it honestly, and resisted the pressure to call every chatbot an agent.
