According to technology services giant Capgemini, Artificial intelligence-powered agents will be able to work together and solve tasks in a so-called multi-agent AI system by 2025. Such a system would entail a collection of agents that work together to solve tasks in a distributed and collaborative way.
Pascal Brier the company’s chief innovation officer quoted to CNBC in an interview “That the firm is already seeing companies that are discussing those agent technologies.” He added that applications using multiple autonomous agents “is really what we should expect next year.”
Capgemini defines AI agents as technology designed to function independently, plan, reflect, pursue higher-level goals, and execute complex workflows with minimal or limited direct human oversight essentially, AI agents that work behind the scenes to complete tasks on your behalf. According to Brier while Europe lags behind, the US is further along the path realizing this technology.
In a new research report released Monday, called “Harnessing the Value of Generative AI,” Capgemini noted the vast majority of companies it surveyed (82%) plan to integrate AI agents within one to three years, while only 7% have no plans to integrate these agents. The research relied on a survey of more than 1,100 companies with revenues of $1 billion or more.
Brier said the so-called AI agents fall into two types: individual agents that carry out tasks on your behalf, and multi-agent technology or, “agents talking to agents.” For example, a marketing-focused AI agent that’s creating an ad campaign for an organization to run in Germany, could autonomously work with another agent in that same organization’s legal department to make sure that it’s legally sound.
Capgemini added “Unlike conventional AI systems that merely follow instructions, these agents “can understand, interpret, adapt, and act independently and, for certain tasks, are capable of replacing human workers.” Brier told CNBC that “The first major wave of AI in 2022, which Brier calls “V1,” was about “understanding what a prompt is, and understanding what an LLM large language model was.”
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