The lack of a standard description of agentic AI may lead to CIOs buying the wrong AI tool or paying more for a product that doesn’t work as advertised. Agentic AI has replaced generative AI at the top of the technology hype cycle, but there’s one major problem: A standard definition of an AI agent doesn’t yet exist. With dozens, if not hundreds, of vendors touting their agentic AI products, a lack of definition could lead to confusion as CIOs and other IT leaders seek to purchase and deploy the emerging technology.
Some AI experts define agentic AI as a tool that can make autonomous decisions within the enterprise, learn from past experiences, and adapt its responses, whereas others suggest that any AI with some decision-making functionality qualifies as agentic. In most cases, vendors aren’t yet offering truly agentic AI with real autonomy, some critics say, but are instead pitching simpler AI chatbots, assistants, or add-ons to large language models (LLMs) as agentic AI. Many so-called agents are just LLM wrappers or “glorified LLM workflows,” says Zach Bartholomew, VP of product at Perigon, provider of an AI-powered and context-based search tool.
Click here to read insights from Ilia Badeev, Head of Data Science at Trevolution, a leading travel software and services provider.
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