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 deciding where to invest, agencies should map both the workflow and the data flow. Where will each agent perform its work? What information will it need, and where does that information reside? Which steps can occur simultaneously, which depend on one another, and where is human review required?
Those questions are especially important in hybrid environments, where data and workloads may span cloud and on-premises systems. An architecture optimized for one generation of AI can create new bottlenecks when multiple agents must coordinate across distributed environments.
Mapping the workflow can also reveal where agentic AI will and will not provide value. An existing 12-step process, for example, might contain five steps that agents can accelerate while others lack sufficiently accessible or reliable data to make automation worthwhile. The goal is not to insert an agent into every step, but to understand the process well enough to determine where autonomy can improve it and what infrastructure those agents will require.
Prioritize Resources With Observability
With more agents operating simultaneously, agencies also need a way to determine which workloads receive infrastructure resources first. Those priorities can reflect mission importance, deadlines, or operational requirements. A financial reporting workload at the end of the month, for example, might temporarily receive priority over a less time-sensitive optimization workload.
The underlying concept isn’t new. High-performance computing environments already use scheduling systems to prioritize workloads based on factors such as urgency and deadlines. As agentic AI develops, agencies must consider how similar principles might help automate resource prioritization more broadly rather than assuming there is already a mature playbook for doing so.
That depends on visibility. IT teams already collect telemetry such as performance metrics, storage information, and logs. In an agentic environment, that information may need to be readable not only by IT teams but also by the systems monitoring and coordinating autonomous workflows.
Consider what happens when an agent encounters a latency spike or another performance issue in the middle of a workflow. Instead of simply stopping, could it use performance or security telemetry to help determine what went wrong? Depending on the policies in place, the system could then recommend reprioritizing or moving the workload or escalating the issue for human review.
Design Around Data Movement, Not Rip-and-Replace
Agentic AI also puts new pressure on data architecture. Agents may depend on information residing across multiple clouds, local systems, and external data sources. Simply having access to that information does not mean an agent can retrieve it quickly enough—or in the appropriate form—to complete a workflow efficiently.
Agencies should map those dependencies to identify potential bottlenecks, from data cleansing and outdated external sources to latency when retrieving information from remote environments. Because agents often depend on one another, a delay in one step can affect the entire workflow. Understanding dependencies can also inform decisions about where data should reside and whether compute, networking, and other infrastructure resources align with agentic workloads.
But identifying a bottleneck should not automatically trigger the need for a new technology purchase. Agencies should first establish the “as-is”: What infrastructure, platforms, data sources, code, and capabilities are already available? What can be reused or consolidated? And where would a new investment fill a genuine gap?
That assessment matters because few agencies can (or should) replace their infrastructure every time the technology landscape changes. A stepwise approach allows them to target the most consequential bottlenecks, build on existing investments, and consider total cost of ownership as agentic AI evolves.
The infrastructure required for agentic AI will continue to change as the technology matures. Agencies don’t need to predict every requirement today. They do need a clear picture of their workflows, data, and existing environment so they can modernize where the mission actually demands it.