Agentic AI or AI agent? CIOs warned against agent marketing hype as vendors oversell capabilities

AI practitioners are beginning to distinguish between AI agents and agentic AI as two related but separate technologies, with experts warning CIOs against vendor confusion and marketing hype. Understanding this distinction is crucial for IT leaders to make informed purchasing decisions and avoid overpaying for “glorified chatbots” masquerading as advanced AI systems.

What you should know: AI agents and agentic AI serve different purposes and have distinct capabilities that CIOs need to understand.

• AI agents are tools designed for specific functions within IT systems, with predictable outcomes and limited learning ability.
• Agentic AI is an umbrella technology that uses agents and other AI tools to create autonomous systems capable of setting goals, learning over time, and reasoning across tasks.
• “Agentic AIs have the ability to set or reprioritize their goals, and they’re able to kind of dynamically reason through different kinds of domains,” says Numa Dhamani, head of machine learning at iVerify, a mobile security provider.

The big picture: CIOs can think of the relationship between these technologies using team metaphors to better understand their roles.

• Jim Olsen, CTO at ModelOp, a software governance provider, compares agents to individual players or employees while agentic AI represents the larger team.
• “Each member of the team brings both abilities, or tools, and expertise, or training, to an overall task, while agentic AI is the whole team working together to solve the problem,” Olsen explains.
• Louis Gutierrez from Constant Contact, an email marketing platform provider, describes agentic AI as “more like an orchestration layer — a system that supervises and coordinates multiple agents to tackle broader objectives.”

Why this matters: Vendor obfuscation and premature marketing of agentic AI capabilities pose significant risks for enterprise buyers.

• Many vendors are selling technology they describe as agents or agentic systems that don’t actually meet those definitions.
• “You’re probably just overpaying for glorified chatbots that are dressed up like an agent,” warns Dhamani.
• True agentic AI remains in early development stages, lacking the shared memory and tool orchestration capabilities needed for full autonomy.

Key risks to consider: Deploying autonomous AI systems introduces coordination complexity and potential security vulnerabilities.

• “If you are now starting to do things like tool calling and initiating actions, you’re starting to introduce a lot of coordination complexity, and you now have an increased blast radius,” Dhamani notes.
• Connecting agents through protocols like model context protocol (MCP) creates data exposure risks between different systems.
• Olsen warns about potential data leakage: “You have this tool that can only access this customer database, but has access to Social Security numbers, but then the agent sends it over to the one that has access to Slack and starts posting those numbers on a public Slack.”

What they’re saying: Industry experts emphasize the importance of vendor transparency and gradual implementation.

• “It’s worth clarifying whether you’re buying a true agentic system or just a workflow agent dressed up in buzzwords,” says Gutierrez.
• “The biggest danger in this space is obfuscation — it’s not always intentional, but it is common to oversell what the technology actually does.”
• Dhamani recommends starting small: “I would start with a very constrained use case that is low risk. Keep the agent in a read-only or suggest-only mode to start with, and then just gradually increase autonomy after it meets all your performance thresholds.”

Looking ahead: Both technologies are expected to evolve rapidly with improved specialization and reliability.

• Agents will likely shift from large-language models to small-language models trained for specific tasks.
• “We will start having what I believe will be truly expert agents, where you’ll use an SLM, highly trained on a specific thing,” predicts Olsen.
• Organizations should ensure their data is properly cleaned and prepared before implementing either technology.