Who Wins the AI Agent Battle?

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Big tech has agents on the brain.At the World Economic Forum in Davos last week, OpenAI chief product officer Kevin Weil argued that 2025 was the year of the AI agent, the next evolution beyond the chatbot in which AI software goes out into the world and executes tasks on behalf of users. Meta CEO Mark Zuckerberg has predicted that the company will have an AI agent with the skills of a “mid-level” engineer by the end of the year.

These forecasts are quickly becoming reality. Two days after Weil’s comment, OpenAI released Operator, the company’s first publicly available agent. Operator works by accessing a remote web browser. You give it a task and virtually watch over its shoulder within the ChatGPT interface as it completes that task. It could, say, make a restaurant reservation or fix your code. OpenAI isn’t first to market: There are more than a dozen competitors offering a similar product. But OpenAI is the biggest game in town, boasting 300 million weekly active users.

If agents fulfill the promise that Silicon Valley has made, then we are in for a dramatic reinvention of both knowledge work and busy work over the next year. But first we need to answer a really important question: What is an AI agent? And from there we need to establish how these products will work—and which companies will dominate with them.

Defining agents

Here’s a boring, technical definition: An AI agent is a type of model architecture that enables a new kind of workflow.

The AI architecture that has underpinned ChatGPT takes a command, formulates a response, and returns it. Ask it something simple, like, “Does an umbrella block the rain?” and GPT-4o returns the answer, “Of course it does, dumbass.” The large language model answers the question using its own internal data—its training set and the prompt you’ve fed it. It’s a straightforward, linear workflow: Enter one prompt, receive on output.

By contrast, agentic workflows are loops—they can run many times in a row without needing a human involved for each step in the task. A language model will make a plan based on your prompt, utilize tools like a web browser to execute on that plan, ask itself if that answer is right, and close the loop by getting back to you. If you ask, “What is the weather in Boston for the next seven days, and will I need to pack an umbrella?” an agent would form a plan, use a web browsing tool to check the weather, and apply its existing corpus of knowledge to know that if it’s raining, you would need an umbrella. After that, it would check if its answers are right and finally say, “It’ll be raining (like it always does in Boston, you dumbass) so, yes, pack an umbrella.” Here, one input elicits multiple actions by the model. You’re not starting a call-and-response, you’re conducting an orchestra.

Agentic workflows are so powerful because there are multiple steps to accomplish the task, each of which you can optimize to be more performative. Perhaps it is faster and/or cheaper for one model to do the planning and smaller, specialized models handle each sub-task contained within the plan. Or maybe you build specialized tools to incorporate into the workflow. You get the idea.

With the release of Operator, two new dimensions of agents were thrown into sharp relief: