Agentic artificial intelligence (AI), a new breed of autonomous, connected AI systems capable of making decisions with minimal human oversight, is driving intense interest across corporate landscapes. Promising to automate complex workflows, anticipate security threats, and manage huge logistical networks, agentic AI’s disruptive potential is reflected in growing enterprise investments. A Capgemini survey projects that half of business executives will deploy AI agents by 2025, a five-fold jump from current levels, while Gartner anticipates a third of enterprise software will include agentic AI by 2028.
But Matt McLarty, chief technology officer at Boomi, cautions that despite agentic AI’s promise, many organizations risk missteps by pursuing complex implementations too soon. Drawing parallels to the early promise and subsequent stagnation seen with Blockchain technology, McLarty suggests the focus should remain on solving actual business problems rather than chasing technological hype. The advice: start small, iterate, and target “low-hanging fruit.” Early investments should prioritize foundational worker agents—narrow, task-specific AI systems capable of instantly modernizing rote automation or exception handling within enterprise workflows. These targeted agents, enabled with advanced language processing, can streamline operations without introducing unnecessary cost or complexity.
Looking ahead, the true power of agentic AI will hinge on interoperability. Organizations are urged to prepare their data and applications for plug-and-play connectivity with AI agents. Frameworks like model context protocol (MCP) offer a way to connect AI models seamlessly to both internal and external information sources, future-proofing organizations as multi-agent capabilities take hold. While some third-party solutions, such as Amazon’s recently released multi-agent tools, offer a head start, bespoke in-house integration will often be needed for large enterprises to unlock the full potential of connected agentic systems. McLarty’s overarching counsel is to keep the focus squarely on tangible business outcomes, ensuring agentic AI serves as a value-driver from the outset rather than a source of unnecessary complexity.
