While AI is likely to reach global investment $1.5 trillion In 2025, Less than half of business leaders Confidence in their organization’s ability to maintain service continuity, security and cost control during unexpected events. With the deep complexity introduced by the lack of trust, agentic AI’s autonomous decision-making and interactions with critical infrastructure, digital resilience needs to be reimagined.
Organizations are turning to the concept of data fabric. It is an integrated architecture that connects and governs information across all business layers. By breaking down silos and enabling real-time access to enterprise-wide data, a data fabric can empower both human teams and agentic AI systems to sense threats, prevent problems before they happen, recover before they do, and maintain operations.
Machine data: A cornerstone of agentic AI and digital resilience
Earlier AI models relied heavily on human-generated data such as text, audio, and video, but agentic AI demands deeper insight into an organization’s machine data: the logs, metrics, and other telemetry generated by devices, servers, systems, and applications.
To put agent AI to use in driving digital flexibility, it must have seamless, real-time access to its data flows. Without comprehensive integration of machine data, organizations risk limiting AI capabilities, missing critical anomalies, or introducing errors. As Kamal Hathi, senior vice president and general manager of Splunk, a Cisco company, emphasizes, agentic AI systems rely on machine data to understand context, simulate outcomes, and continuously adapt. This makes machine data monitoring a cornerstone of digital flexibility.
“We often describe machine data as the heartbeat of the modern enterprise,” Hathi says. “Agent AI systems are powered by this critical pulse, requiring real-time access to information. It is important that these intelligent agents work directly on complex streams of machine data and that the AI ​​itself is trained using the same data stream.”
Few organizations are currently achieving the level of machine data integration required to fully enable agentic systems. Not only does this reduce the scope of potential use cases for agentic AI, but worse, it can also result in data inconsistencies and errors in results or actions. Natural language processing (NLP) models developed prior to the development of generative pre-trained transformers (GPT) suffered from linguistic ambiguity, bias and inconsistencies. Similar mistakes can be made with agentic AI if organizations move forward without providing models with basic fluency in machine data.
For many companies, keeping up with the rapid pace at which AI is advancing has been a huge challenge. “In some ways, the pace of this innovation is starting to hurt us, because it creates risks that we’re not prepared for,” Hathi says. “The problem is that with the evolution of agentic AI, relying on traditional LLMs trained on human text, audio, video, or print data is when you need your system to be secure, flexible, and always available.”
Designing the data fabric for resiliency
To address these shortcomings and build digital resilience, technology leaders must pivot to what Hethi describes as the design of a data fabric, better suited to the demands of agent AI. It involves bringing together dispersed assets from security, IT, business operations, and the network to form an integrated architecture that connects disparate data sources, breaks down silos, and enables real-time analysis and risk management.