Why most agentic AI strategies fail in I&O
By Chandra Mukhyala, Sr Director Analyst at Gartner
The infrastructure and operations (I&O) landscape is undergoing a fundamental shift driven by the rapid maturation of large language models (LLMs). Capable of sophisticated reasoning, contextual analysis, and planning, these models enable organizations to move beyond deterministic, script-based automation toward autonomous, agentic operations. In this new model, AI agents act as software entities that leverage LLM reasoning to independently interpret system state, formulate action plans, and execute changes across infrastructure tools without continuous human intervention.
Enterprises are adopting this model to significantly reduce the mean time to resolution (MTTR) of incidents, increase engineering productivity, strengthen security through machine-speed response, and optimize infrastructure to reduce the total cost of ownership (TCO). For many I&O leaders, agentic AI represents a pathway to scale operations in environments whose complexity now exceeds what human-centric processes can reliably manage.
However, this transition introduces a class of...
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