The next phase of AI is agentic, and it starts with data architecture (www.techradar.com)

A significant shift in artificial intelligence is underway, moving from the era of larger models to a focus on agentic AI—smaller, purpose-built agents that collaborate and learn continuously rather than relying on a single, massive model. This new approach emphasizes a distributed intelligence architecture that allows agents to observe, reason, and react to real-time data, breaking away from the traditional question-answer cycles of generative AI. By operating in loops, these agents can adjust to changes in customer behavior and market dynamics, which promises to create more responsive and intelligent systems. For the AI/ML community, this transition highlights the critical importance of unified data architectures. Fragmented data can lead to contradictory decisions among agents, undermining their effectiveness. To truly leverage the potential of agentic systems, organizations must design their data models for continuous learning and interoperability from the outset, treating AI as an integral part of their infrastructure rather than an add-on. This represents not just a technological evolution but a conceptual shift—enabling teams to enhance system intelligence while retaining human oversight for strategic oversight and goal definition. The future of AI lies in creating coherent ecosystems that enable collaborative decision-making, ensuring that systems grow smarter over time rather than becoming rigid and brittle.