If 2024 was the year of models that could see and hear, 2026 is the year of models that do things. The phrase repeated in every announcement is agentic AI, and it is worth understanding what actually sits behind it.
The difference in one sentence
An ordinary model answers a question. An agent is given a goal, decides for itself which steps to take, uses tools to carry them out, and checks the result.
In practice: ask an ordinary model how to fix a bug and you get an explanation. Give the same task to an agent and it will open the file, edit it, run the tests, see that they still fail, and try again.
What made the shift possible
Three things converged. First, models got better at following long chains of instructions without losing the thread. Second, context windows grew to a million tokens, so an agent can hold an entire project in view. Third, the way models call external tools became standardised.
Newer development tools go further still, running several sub-agents in parallel and keeping a history of actions so work can resume after an interruption.
Where the limits are
Agents do well on tasks with a clear success criterion. A test either passes or it does not. A field is either extracted or it is not. There the agent can correct itself, because it knows when it got something wrong.
On tasks with no measurable criterion – strategy, judgement calls, anything where the answer is a matter of opinion – the agent has nothing to learn from mid-run. That still needs a person.
The security side
This is the part mentioned in passing in the announcements, and it is the expensive one. An agent with access to your files, database, or email is a new attack surface. A malicious instruction hidden inside a document the agent processes can steer it into doing something you never asked for.
Basic hygiene: give the agent the smallest possible set of permissions, log everything it does, and never let it take irreversible actions without confirmation. Deleting, sending mail, and spending money always go through a human.
How to start
Do not build an autonomous system on day one. Take one task with a clear success criterion, let the agent work on it under your supervision for a week, and only then think about widening its remit. Teams that skip this step usually end up with a system nobody trusts and therefore nobody uses.