Gartner recently projected that 40% of AI agent projects will be abandoned by 2027. Many enterprises are already discovering the reasons firsthand: costs escalating without return, outputs that require more manual rework than expected and initiatives launched based on hype rather than measurable value.
These aren’t failures of AI. They’re failures of systems.
The issue isn’t that large language models (LLMs) lack capability. AI agents, as they’re currently deployed, lack structure, validation, and alignment. They receive a prompt, produce output, but rarely deliver measurable outcomes in alignment with business goals.
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