With Gartner predicting that 40% of AI agent projects may be abandoned by 2027, the stakes for getting enterprise AI right have never been higher. In an authored piece on The New Stack — one of the most respected publications in the developer and DevOps community — Krazimo CEO Akhil Verghese breaks down why so many AI agent projects fail and provides a practical engineering framework for building ones that don’t.
The article draws on Verghese’s experience at Google and his work at Krazimo helping enterprises deploy reliable generative AI systems. He argues that most AI agent failures aren’t caused by limitations in the underlying models — they stem from poor engineering practices: lack of proper testing, over-reliance on non-deterministic one-shot approaches, and premature deployment without adequate validation.
Verghese’s prescription centers on three principles: building deterministic, modular workflows where each step can be tested independently; implementing rigorous evaluation frameworks that go beyond traditional unit tests; and adopting phased deployment strategies that include shadow launches and human-in-the-loop validation before full automation.
For engineering leaders evaluating AI agent projects, this article serves as both a diagnostic tool (identifying where your current approach may be vulnerable) and a playbook (providing specific techniques for building more reliable systems). The message is clear: with the right engineering discipline, AI agents can deliver transformative value — but cutting corners on reliability will likely land you in that 40% failure bucket.
Originally published on The New Stack. Krazimo specializes in building reliable, enterprise-grade AI agents and generative AI solutions.
Read the full article at The New Stack.