An AI agent is a smart digital assistant that can understand what you want, figure out the best way to do it, and use different tools to get the job done—kind of like a really capable, always-learning intern for your computer.

AI agents are leveling up from basic task-doers to something a lot more capable.

They started out as single-purpose agents (see slide above)—good at one thing, but not much else.

Think of them like early smartphone apps that did one job and stayed in their lane.

Then came platform-level agents (see slide), which got a little more flexible, working across different tools but still stuck inside walled gardens.

The endgame, though, is universal agents that can move between platforms, handle a range of tasks, and actually make independent decisions instead of just following orders.

At the time of publishing this, AI agents are sitting somewhere between Siloed, Single-Purpose Agents and Platform-Level Agents on that chart.

They’re more capable than basic, task-specific bots, but they’re not quite at the point where they can seamlessly operate across multiple platforms with real autonomy.

The biggest shift happening now is that AI agents are starting to go beyond just responding to commands. They’re learning to string multiple steps together, take action across different platforms, and even make basic decisions on their own.

What’s pushing this shift is smarter AI models with access to better tools, making it possible to tackle more complex workflows.

Instead of just automating routine tasks, these agents are inching closer to true decision-making, changing how we interact with technology and reshaping everything from productivity to creativity.

At their core, AI agents are built to understand intent, plan with context, take action using the right tools, and keep improving through iteration.

That last part is key—continuous learning is what’s turning AI agents from passive assistants into active problem-solvers, setting the stage for a future where they don’t just respond to commands but anticipate what’s next.