Let’s begin with the time period “agent” itself. Proper now, it’s being slapped on every thing from easy scripts to stylish AI workflows. There’s no shared definition, which leaves loads of room for firms to market fundamental automation as one thing far more superior. That sort of “agentwashing” doesn’t simply confuse prospects; it invitations disappointment. We don’t essentially want a inflexible normal, however we do want clearer expectations about what these methods are purported to do, how autonomously they function, and the way reliably they carry out.
And reliability is the subsequent massive problem. Most of immediately’s brokers are powered by giant language fashions (LLMs), which generate probabilistic responses. These methods are highly effective, however they’re additionally unpredictable. They’ll make issues up, go off monitor, or fail in delicate methods—particularly once they’re requested to finish multistep duties, pulling in exterior instruments and chaining LLM responses collectively. A current instance: Customers of Cursor, a well-liked AI programming assistant, had been advised by an automatic help agent that they couldn’t use the software program on a couple of system. There have been widespread complaints and reviews of customers cancelling their subscriptions. But it surely turned out the policy didn’t exist. The AI had invented it.
In enterprise settings, this type of mistake might create immense harm. We have to cease treating LLMs as standalone merchandise and begin constructing full methods round them—methods that account for uncertainty, monitor outputs, handle prices, and layer in guardrails for security and accuracy. These measures can assist make sure that the output adheres to the necessities expressed by the person, obeys the corporate’s insurance policies relating to entry to data, respects privateness points, and so forth. Some firms, together with AI21 (which I cofounded and which has acquired funding from Google), are already shifting in that route, wrapping language fashions in additional deliberate, structured architectures. Our newest launch, Maestro, is designed for enterprise reliability, combining LLMs with firm knowledge, public data, and different instruments to make sure reliable outputs.
Nonetheless, even the neatest agent received’t be helpful in a vacuum. For the agent mannequin to work, totally different brokers have to 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 important concept.
In observe, A2A nonetheless falls brief. It defines how brokers speak to one another, however not what they really imply. If one agent says it may possibly present “wind situations,” 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 downside earlier than in distributed computing. Fixing it at scale is much from trivial.
