Summarization is not reasoning: How hybrid AI fixes failing AIOps

Agentic AI represents the next evolution of IT operations

by · TechRadar

Opinion By Casey Kindiger published 4 May 2026

(Image credit: Getty Images)

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While many AIOps platforms promise automation and intelligence, most still rely on conditional logic, scripted workflows, dashboards, or copilot-style summaries. These approaches can improve visibility, but they often fall short of delivering true autonomy.

The reason is simple: they lack enterprise memory, cross-domain reasoning, and governed execution.

Casey Kindiger

Founder and CEO of Grokstream, LLC.

At the same time, there’s growing excitement around large language model (LLM)-driven agents. While powerful, LLMs alone are not enough to deliver autonomous operations.

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True agentic systems require a combination of classical machine learning and generative AI, working together to provide predictive, causal intelligence and reliable outcomes.

Agentic AI represents the next evolution of IT operations. But getting there requires more than adopting new tools. It requires building the right foundation. Below are five critical steps organizations should take to move toward safe, scalable, and self-driving operations.

1. Start with a unified data foundation

Modern IT environments are complex and fragmented. Data is spread across monitoring tools, logs, metrics, IT service management systems, and more. Each system offers a partial view, but none provides the full picture.

For AI to operate effectively, it needs access to a unified and continuously refined data layer. This means ingesting data from across the environment, normalizing it, enriching it with context, and making it usable in real time.

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