FEATURE
WHAT DO ANTHROPIC’ S AGENT EXPERIMENTS ACTUALLY TELL US?
Anthropic’ s recent multi-agent experiments made headlines after three AI agents began interfering with each other’ s work. But was this really a case of AI going rogue, or a problem with the environment humans created for it? Terry Dorsey, AI Architect for Modern Data Solutions at Denodo, argues that the experiments expose a bigger challenge for Enterprise AI: giving agents access to information does not mean they have the context to understand what matters.
Anthropic recently shared insights from experiments designed to better understand the characteristics and behaviours of multi-agent solutions. In many ways, the results reflect much of what we have already been experiencing, and perhaps anticipating, as we move deeper into this new technological space.
Some of the findings were fascinating. Others, at least initially, were a little discomforting. But what struck me most was how clearly they exposed one of the fundamental challenges of Agentic AI: communication.
And I mean communication in the broadest sense. Communication between agents. Communication of information and context. Communication of intent. Communication between people and machines. Underneath all of it is something deceptively familiar: language and our ability to establish a common understanding of what is being communicated.
This puts us in some known, yet strangely unknown, territory.
Language is how we communicate, right? Giving technology the ability to understand and use language should make things easier, not harder. Yet when we look at Anthropic’ s results, many of the challenges feel oh so familiar. They reflect issues we have already encountered with Generative AI:
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