Let’s start with the term “agent” itself. Right now, it’s being slapped on everything from simple scripts to sophisticated AI workflows. There’s no shared definition, which leaves plenty of room for companies to market basic automation as something much more advanced. That kind of “agentwashing” doesn’t just confuse customers; it invites disappointment. We don’t necessarily need a rigid standard, but we do need clearer expectations about what these systems are supposed to do, how autonomously they operate, and how reliably they perform.
And reliability is the next big challenge. Most of today’s agents are powered by large language models (LLMs), which generate probabilistic responses. These systems are powerful, but they’re also unpredictable. They can make things up, go off track, or fail in subtle ways—especially when they’re asked to complete multistep tasks, pulling in external tools and chaining LLM responses together. A recent example: Users of Cursor, a popular AI programming assistant, were told by an automated support agent that they couldn’t use the software on more than one device. There were widespread complaints and reports of users canceling their subscriptions. But it turned out the policy didn’t exist. The AI had invented it.
In enterprise settings, this kind of mistake could create immense damage. We need to stop treating LLMs as standalone products and start building complete systems around them—systems that account for uncertainty, monitor outputs, manage costs, and layer in guardrails for safety and accuracy. These measures can help ensure that the output adheres to the requirements expressed by the user, obeys the company’s policies regarding access to information, respects privacy issues, and so on. Some companies, including AI21 (which I cofounded and which has received funding from Google), are already moving in that direction, wrapping language models in more deliberate, structured architectures. Our latest launch, Maestro, is designed for enterprise reliability, combining LLMs with company data, public information, and other tools to ensure dependable outputs.
Still, even the smartest agent won’t be useful in a vacuum. For the agent model to work, different agents need to cooperate (booking your travel, checking the weather, submitting your expense report) without constant human supervision. That’s where Google’s A2A protocol comes in. It’s meant to be a universal language that lets agents share what they can do and divide up tasks. In principle, it’s a great idea.
In practice, A2A still falls short. It defines how agents talk to each other, but not what they actually mean. If one agent says it can provide “wind conditions,” another has to guess whether that’s useful for evaluating weather on a flight route. Without a shared vocabulary or context, coordination becomes brittle. We’ve seen this problem before in distributed computing. Solving it at scale is far from trivial.
