Preparing Infrastructure for the Next Phase of Agentic AI

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After years of exploring how AI can support agency operations and make better use of growing volumes of data, agencies are now preparing for the next phase: agentic AI. The infrastructure needed is changing along with it. Agentic AI, unlike regular AI capabilities, can coordinate multiple specialized agents to carry out complex, multistep workflows, which means agencies are no longer planning only for the compute required to run AI models. They also have to account for the orchestration between agents, the movement of data across environments, and the points where human judgment still belongs. That makes it important to start with the work itself—not the newest, shiniest technology. The following three strategies can help. Start With the Workflow, Not the Hardware Agentic AI is more sophisticated, but it also requires more orchestration and preparation. Consider a permitting process. One agent might evaluate municipal requirements while another examines state or federal statutes. A third agent—or a human—might then validate conflicting information before the process can move forward. The individual AI tasks are only part of the workload. The system must also coordinate those tasks, determine when one step depends on another, and move information between them. That changes infrastructure requirements. AI environments have often been designed around compute-intensive model workloads, with significant attention paid to accelerators. But agentic systems can introduce substantial CPU and networking requirements as agents execute workflows and interact with one another. Before …

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