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As enterprises deepen their reliance on complex digital infrastructures, digital resilience is no longer optional – it’s a strategic imperative. The emergence of agentic AI, characterized by its autonomous planning and execution, is simultaneously accelerating innovation and amplifying vulnerabilities. While AI investment is booming, many leaders express concern about maintaining service continuity amidst unforeseen disruptions. Agentic AI, with its ability to operate at unprecedented speed and scale, exacerbates existing data inconsistencies and security gaps.

A data fabric architecture is gaining traction as a key solution. By connecting disparate information sources across the organization, a data fabric creates real-time data accessibility for both humans and AI. This enhanced access facilitates proactive risk management, accelerates recovery processes, and breaks down traditional data silos. Critically, agentic AI’s effectiveness hinges on seamless access to machine data – logs, metrics, and telemetry – enabling it to understand context, simulate potential outcomes, and dynamically adapt.

However, insufficient machine data integration remains a significant hurdle for many organizations, thereby limiting AI capabilities and potentially introducing errors. Traditional AI models, primarily trained on human-generated data, may struggle to process the complexities of system-level data. Forward-thinking organizations are redesigning their data architectures, implementing data fabric solutions that integrate security, IT, and business operations data for real-time analysis. These unified systems enhance visibility, accelerate decision-making, and improve resilience to disruptions.

This transformation necessitates breaking down departmental silos and adopting federated data architectures, where autonomous data sources collaborate without centralized merging. Organizations also require platforms capable of handling both structured and unstructured data, complemented by AI-powered tools that identify data relationships, detect errors, and leverage NLP for data categorization. Ultimately, agentic AI should be viewed as a form of assistive intelligence, demanding human oversight to mitigate potential failures and security risks. Note: The original reporting was provided by Insights, the custom content studio of MIT Technology Review.