Let’s begin with the time period “agent” itself. Proper now, it’s being slapped on every thing from easy scripts to classy AI workflows. There’s no shared definition, which leaves loads of room for corporations to market primary automation as one thing rather more superior. That sort of “agentwashing” doesn’t simply confuse clients; it invitations disappointment. We don’t essentially want a inflexible normal, however we do want clearer expectations about what these techniques are alleged to do, how autonomously they function, and the way reliably they carry out.
And reliability is the following large problem. Most of at the moment’s brokers are powered by massive language fashions (LLMs), which generate probabilistic responses. These techniques are highly effective, however they’re additionally unpredictable. They’ll make issues up, go off monitor, or fail in refined methods—particularly once they’re requested to finish multistep duties, pulling in exterior instruments and chaining LLM responses collectively. A latest instance: Customers of Cursor, a well-liked AI programming assistant, had been informed by an automatic assist agent that they couldn’t use the software program on a couple of machine. There have been widespread complaints and reviews of customers canceling their subscriptions. But it surely turned out the coverage didn’t exist. The AI had invented it.
In enterprise settings, this type of mistake may create immense injury. We have to cease treating LLMs as standalone merchandise and begin constructing full techniques round them—techniques that account for uncertainty, monitor outputs, handle prices, and layer in guardrails for security and accuracy. These measures will help be certain that the output adheres to the necessities expressed by the person, obeys the corporate’s insurance policies relating to entry to info, respects privateness points, and so forth. Some corporations, together with AI21 (which I cofounded and which has obtained funding from Google), are already transferring in that course, wrapping language fashions in additional deliberate, structured architectures. Our newest launch, Maestro, is designed for enterprise reliability, combining LLMs with firm information, public info, and different instruments to make sure reliable outputs.
Nonetheless, even the neatest agent gained’t be helpful in a vacuum. For the agent mannequin to work, completely different brokers must cooperate (reserving your journey, checking the climate, submitting your expense report) with out fixed human supervision. That’s the place Google’s A2A protocol is available in. It’s meant to be a common language that lets brokers share what they will do and divide up duties. In precept, it’s an awesome thought.
In apply, A2A nonetheless falls brief. It defines how brokers discuss to one another, however not what they really imply. If one agent says it could present “wind circumstances,” one other has to guess whether or not that’s helpful for evaluating climate on a flight route. And not using a shared vocabulary or context, coordination turns into brittle. We’ve seen this drawback earlier than in distributed computing. Fixing it at scale is much from trivial.
