ver the past couple of years, the world has marveled at the capabilities and possibilities unleashed by generative AI (gen AI). Foundation models such as large language models (LLMs) can perform impressive feats, extracting insights and generating content across numerous mediums, such as text, audio, images, and video. But the next stage of gen AI is likely to be more transformative.
We are beginning an evolution from knowledge-based, gen-AI-powered tools—say, chatbots that answer questions and generate content—to gen AI–enabled “agents” that use foundation models to execute complex, multistep workflows across a digital world. In short, the technology is moving from thought to action.
Agentic systems traditionally have been difficult to implement, requiring laborious, rule-based programming or highly specific training of machine-learning models. Gen AI changes that. When agentic systems are built using foundation models (which have been trained on extremely large and varied unstructured data sets) rather than predefined rules, they have the potential to adapt to different scenarios in the same way that LLMs can respond intelligibly to prompts on which they have not been explicitly trained. Furthermore, using natural language rather than programming code, a human user could direct a gen AI–enabled agent system to accomplish a complex workflow. A multiagent system could then interpret and organize this workflow into actionable tasks, assign work to specialized agents, execute these refined tasks using a digital ecosystem of tools, and collaborate with other agents and humans to iteratively improve the quality of its actions.
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