Let’s begin with the time period “agent” itself. Proper now, it’s being slapped on every part from easy scripts to stylish AI workflows. There’s no shared definition, which leaves loads of room for corporations to market primary automation as one thing far more superior. That type of “agentwashing” doesn’t simply confuse clients; it invitations disappointment. We don’t essentially want a inflexible customary, however we do want clearer expectations about what these techniques are imagined to do, how autonomously they function, and the way reliably they carry out.
And reliability is the subsequent large problem. Most of at this time’s brokers are powered by giant language fashions (LLMs), which generate probabilistic responses. These techniques are highly effective, however they’re additionally unpredictable. They will 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 current instance: Customers of Cursor, a preferred AI programming assistant, had been instructed by an automatic help agent that they couldn’t use the software program on multiple gadget. There have been widespread complaints and experiences of customers canceling their subscriptions. However it turned out the coverage 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 techniques round them—techniques that account for uncertainty, monitor outputs, handle prices, and layer in guardrails for security and accuracy. These measures may help make sure that the output adheres to the necessities expressed by the person, obeys the corporate’s insurance policies concerning entry to data, respects privateness points, and so forth. Some corporations, together with AI21 (which I cofounded and which has obtained 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 information, public data, 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’ll do and divide up duties. In precept, it’s an amazing 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 possibly present “wind circumstances,” one other has to guess whether or not that’s helpful for evaluating climate on a flight route. With out 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.
