We\u2019re witnessing a pivotal shift in artificial intelligence, where the sweeping scale of huge, all-purpose AI is giving way to the precision and finesse of purpose-built autonomous agents. This is not just a technical advancement. It\u2019s a reimagining of how machines can augment a worker’s potential.<\/p>\n\n\n\n
Whether that\u2019s helping sales reps nurture leads, brainstorming campaign ideas for product marketers, or deflecting customer service calls, these purpose-built agents<\/a> are focused on one specific job, and doing it exceedingly well. Even better, they boast a superpower most GPT-based tools can\u2019t match: the ability to take action and get real work done. <\/p>\n\n\n\n \u201cLLMs are extremely powerful, but they are part of a much more complex and nuanced AI architecture,\u201d said Itai Asseo, senior director of incubation and brand strategy, AI research at Salesforce. \u201cSometimes you need a big multipurpose LLM, but in many cases a specialized agent will be a much more efficient solution.\u201d <\/p>\n\n\n\n Business leaders clearly see the potential. A Capgemini survey<\/a> found 82% of large companies plan to implement agents by 2027, heralding a new way of working that demands a new technological approach. <\/p>\n\n\n\n If you\u2019ve ever used generative AI to help draft an email or brainstorm a campaign idea, you\u2019ve probably been impressed by the technology\u2019s potential, while simultaneously feeling frustrated by some of the hard limitations of GPT-based tools in the workplace.<\/p>\n\n\n\n They\u2019re trained on \u201cgeneral\u201d data, so they don\u2019t know your business or customers, and can\u2019t generate outputs that reflect your day-to-day reality. They can\u2019t tell you, for example, about open sales opportunities or provide year-to-date campaign performance. <\/p>\n\n\n\n Forward-thinking organizations are already beginning to bridge that knowledge shortfall with an innovative new type of data platform<\/a> that aggregates and harmonizes data, and connects the dots between every data point. But for AI to truly be effective in an enterprise setting, a second requirement must be met: it needs to have the ability to take action on your behalf. <\/p>\n\n\n\n Agents deliver this capability by combining the language and reasoning powers of LLMs with large action models<\/a> (LAMs). A LAM is a type of language model that specializes in \u201cfunction-calling,\u201d which is the ability to execute actions in other systems and apps. LAMs are trained on datasets specifically curated for performing tasks, enabling agents to autonomously trigger a wide range of actions. <\/p>\n\n\n\nAutonomous agents will revolutionise work<\/h2>\n\n\n\n