AI agents promise to improve efficiency and automate decision-making, but many fall short once they are deployed in real operational environments. The common issue is not the model itself, but the complexity of the data and systems the agent is expected to navigate.
When agents are required to operate across multiple data sources and tools, inconsistency and ambiguity quickly emerge. Each additional system introduces new schemas, formats, and assumptions, increasing the likelihood of unreliable behavior and hallucinations. Instead of accelerating outcomes, complexity becomes a limiting factor.
A data lakehouse changes this dynamic by normalizing data before agents ever interact with it. Information from disparate systems is consolidated into a single, governed environment that presents a consistent view of reality. With fewer variables to interpret, agents can focus on classification, prioritization, and execution rather than data reconciliation.
This simplified foundation allows agents to operate more reliably and scale with confidence. Organizations benefit from improved efficiency, clearer decision-making, and better customer experiences, all driven by agents that work from the same trusted source of truth.
In 2026, AI will not replace MSP technicians. It will empower them by simplifying data, reducing noise, and enabling agents that deliver faster, smarter, and more consistent service.