The way humans interact and collaborate with AI is taking a dramatic leap forward with agentic AI. Think: AI-powered agents that can plan your next trip overseas and make all the travel arrangements; humanlike bots that act as virtual caregivers for the elderly; or AI-powered supply-chain specialists that can optimize inventories on the fly in response to fluctuations in real-time demand. These are just some of the possibilities opened up by the coming era of agentic AI.

While previous AI assistants were rules-based and had limited ability to act independently, agentic AI will be empowered to do more on our behalf. But what, exactly, is agentic AI? “You can define agentic AI with one word: proactiveness,” said Enver Cetin, an AI expert at global Experience Engineering firm Ciklum, whom I interviewed. “It refers to AI systems and models that can act autonomously to achieve goals without the need for constant human guidance. The agentic AI system understands what the goal or vision of the user is and the context to the problem they are trying to solve.”

To achieve this level of autonomous decision-making and action, agentic AI relies on a complex ensemble of different machine learning, natural language processing, and automation technologies. While agentic AI systems harness the creative abilities of generative AI models such as ChatGPT, they differ in several ways. First, they are focused on making decisions rather than on creating content. Second, they do not rely on human prompts, but rather are set to optimize particular goals or objectives, such as maximizing sales, customer satisfaction scores, or efficiency in supply-chain processes. And third, unlike generative AI, they can also carry out complex sequences of activities, independently searching databases or triggering workflows to complete activities.

The Benefits of Working with Agentic AI

With their supercharged reasoning and execution capabilities, agentic AI systems promise to transform many aspects of human-machine collaboration, especially in areas of work that were previously insulated from AI-led automation, such as proactively managing complex IT systems to pre-empt outages; dynamically re-configuring supply chains in response to geopolitical or weather disruptions; or engaging in realistic interactions with patients or customers to resolve issues. Three of the main benefits will be greater workforce specialization, greater informational trustworthiness, and enhanced innovation.

Greater specialization 

The importance of workforce specialization — the “division of labor” — has been understood since Adam Smith’s celebrated pin-factory visit in the opening paragraphs of The Wealth of Nations. Smith observed how one worker “draws out the wire, another straights [sic] it, a third cuts it; a fourth points it…” such that “the important business of making a pin is, in this manner, divided into about eighteen distinct operations.” Specialization brings greater efficiency, learning by doing, and innovation — but can be difficult to implement as businesses run up against workforce shortages and mismatches between roles and available human skills. Because agentic models are explicitly designed to carry out very granular tasks, they enable much greater specialization of roles compared with previous broad-brush automation systems. What’s more, multiple agentic roles can be created rapidly. In knowledge work, for example, agents can be created for information retrieval, analysis, workflow generation, and employee assistance — all working in tandem. Some AI agents will also work “behind the scenes”, orchestrating the work of other agents, just as human managers do for their teams.

Innovation

With their enhanced judgement and powers of execution, Agentic AI systems are ideal for experimentation and innovation. For example, ChemCrow, an AI-powered chemistry agent, has been used to plan and synthesize a new insect repellent as well as to create novel organic compounds. Multi-agent AI models can also scan and analyse vast research spaces — such as scientific articles and databases — in a fraction of the time it would take teams of human scientists and researchers. SciAgents — a multi-agent model developed by researchers at MIT — includes not only robot scientists to develop research plans, but a Critic Agent to review these and suggest improvements. Working together, the team of AI agents was able to identify a novel bio-material combining silk and dandelion-based pigments that had better mechanical and optical properties compared with similar materials, with a reduced energy input to boot.