From Generative AI to Agentic AI: What's Actually Changing
The headline that generative AI is being "replaced" by agents is misleading, and it leads companies to make expensive mistakes. Here's what's actually changing when AI moves from giving answers to taking actions.
- agentic ai
- generative ai
- ai agents
Ask AI about this
From Generative AI to Agentic AI: What's Actually Changing
Two years ago, most of the AI conversations I had with leadership teams were about chatbots and copilots. Today, almost every one of them includes the word "agents."
That shift is real. But the headline that generative AI is being "replaced" is misleading, and it leads companies to make expensive mistakes. Here's how I explain what's actually changing.
What's the difference?
Generative AI produces something when you ask: an email draft, a summary, a piece of code. A person reads it and decides what to do next.
Agentic AI is given a goal and works towards it over several steps. It plans, uses tools, checks results, and takes actions in your systems: updating a CRM record, issuing a refund, filing a ticket, or running code.
The simplest way I put it: generative AI gives you answers. Agentic AI gets things done.
It's an evolution, not a replacement
Agents run on the same generative models. The language model is the reasoning engine at the centre of every agent. What's new is everything around it: access to tools, memory, permissions, and the freedom to act.
My observation: Generative AI isn't disappearing. It's becoming the engine inside something bigger. Teams that already understand generative AI well are the ones best placed to build agents.
Why the shift is happening now
Three things changed. Models became much better at reasoning and using tools. New standards made it far easier to connect AI to business systems and data. And most importantly, chat-based AI hit a ceiling on value.
Research from MIT and McKinsey in 2025 found that most companies saw little bottom-line impact from AI assistants. Helping people write faster rarely changes the P&L. Agents promise something different: completing the work itself.
My observation: The move to agents is driven less by technology hype and more by leaders asking, "Where's the return?"
Adoption is real, but uneven
Surveys in 2026 suggest that around half of organizations have some form of agent in production. But definitions vary widely. One study found that while most organizations use agents, only about 11% of their use cases made it into production in the past year. Another found that most "agents" in production are assistive workflows rather than truly autonomous systems.
Gartner predicts that more than 40% of agentic AI projects will be cancelled by the end of 2027 because of rising costs, unclear value, or weak risk controls. It also warns about "agent washing": vendors relabelling existing chatbots and automation as agents. By its estimate, only around 130 of the thousands of vendors claiming agentic capabilities actually deliver them.
My observation: When a vendor says "agent," I ask one question: what can it actually do without a human clicking a button? The answer is often "not much."
What changes when AI starts acting
The risk changes. A bad answer can be ignored. A bad action, like a wrong payment or a deleted record, may not be reversible.
Reliability matters more. Research shows agents that succeed on a task once often fail when asked to repeat it many times. In a workflow running thousands of times a day, consistency is everything.
Security changes. An agent with access to email, files, and systems is an attractive target. Hidden instructions in a document or webpage can redirect it.
Costs change. Agents use far more computing per task than a single chat response.
My observation: Agents need to be managed more like new employees than like software features: clear responsibilities, limited access, supervision, and a record of everything they do.
What could your agents do if someone tricked them?
That is what the AI Security engagement tests — every agent mapped for the data it can reach and the actions it can take, then attacked the way a real adversary would, with permissions cut back to what each task actually needs.
Where generative AI still wins
Not everything needs an agent. Drafting, summarizing, brainstorming, translation, and answering questions work well with generative AI, especially where a person reviews the output anyway. Adding autonomy to these tasks adds cost and risk without much benefit.
What I've seen work
- Start narrow. Pick well-defined, repeatable workflows where mistakes can be reversed.
- Limit access. Give each agent only the permissions it needs, and its own identity.
- Keep humans on the big decisions. Require approval for anything irreversible or high-value.
- Log everything and measure both success rate and consistency.
- Test vendor claims for real autonomy before you buy.
- Keep generative AI where it works, and don't force agents where they don't.
The shift from generative AI to agentic AI is the shift from AI that advises to AI that acts. That's where the real value is, and also where the real risk is. Companies that build the controls first will be the ones that scale.