The term 'agentic' has emerged as a vital concept in the generative AI landscape, highlighting the collaborative capabilities of AI systems. Key industry players like Adobe and OpenAI are introducing tools that enable AI agents to work independently on complex tasks, diverging from traditional chatbots. Notification of a shift from merely generating responses to executing actions marks a significant evolution in AI applications for marketing and publishing. This shift necessitates understanding the operational differences between AI agents, agentic systems, and automated workflows.
Though the tech is clearly still in a larval stage, there's real implications for brands, agencies and publishers relating to it. But in order to see through chatbots masquerading as something more, it's important to understand the differences between agentic systems, AI agents and the various use cases already being applied at agencies, brands and publishers.
The world of generative AI, [of] ChatGPT, was about questions and and answers. Now we are in the world of action, said David Raichman, executive creative director at Ogilvy Paris, where the agency network operates a special AI unit.
Agents are AI models with a job. Agentic means those agents can work independently of humans for a certain length of time, said Wesley ter Haar, co-founder and chief AI officer at Monks.
The difference is one of scale and process. Agentic AI describes a situation where multiple AI agents work together to complete complex tasks, with minimal oversight or intervention from a human user.
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