By: Paul Perez, SVP & Senior Technology Fellow, Office of the CTO & Dell Federal
Always-on agents change the economics of AI. Federal agencies need workload placement strategies that account for token demand, inference costs, data control, and mission requirements.
Key takeaways
- Agentic AI can deliver major productivity gains, but persistent agents can consume far more tokens than standard chat interactions.
- According to Signal65 research, agentic workloads can use 4x to 15x more tokens than standard chat interactions, and autonomous agents may drive up to 1,000x more inference demand than reasoning AI.
- A token-smart infrastructure strategy helps agencies decide where workloads should run based on mission sensitivity, data gravity, latency, governance, and cost predictability.
Agentic AI – AI systems that retrieve information, reason through multi-step tasks, call tools, and generate outputs autonomously over time – introduces a new layer of infrastructure planning because agents run continuously.
Agents work across tasks and workflows. They can retrieve information, reason through steps, call tools, coordinate actions, and generate outputs over time. As federal agencies embed agents into mission workflows, token demand becomes tied to operational activity and to the infrastructure decisions that support it.
How do agentic AI agents drive token demand?
Tokenomics – the economics of how AI systems create and consume tokens – is a practical planning issue for agencies moving agentic AI into production. Each activity consumes tokens, and token use scales with the frequency, duration, and complexity of the work they support. That makes placement a core infrastructure decision, not a downstream cost consideration.
Where should agencies run agentic AI workloads?
Workload placement matters because AI cost, performance, security, and governance are all connected. Agencies need to decide where each workload should run based on the data it uses, the latency it requires, the sensitivity of the mission, and the predictability of demand.
Cloud can be useful for testing, temporary capacity, or specialized needs. It gives teams flexibility as they explore new models, validate use cases, and manage variable workloads. But always-on agents can quickly increase token use and costs, especially when workloads are persistent and high volume.
For those workloads, agencies may need more control over where inference happens. Bringing AI closer to governed mission data can reduce unnecessary data movement, strengthen control, and improve cost predictability. It can also help teams align infrastructure decisions with mission sensitivity, data gravity, compliance needs, and long-term cost predictability.
How can agencies predict and control AI costs?
Cost predictability becomes clearer when agencies look beyond experimentation and model the cost of persistent operations. Signal65 found that agentic workloads can use 4x to 15x more tokens than standard chat interactions, and autonomous agents may drive up to 1,000x more inference demand than reasoning AI.
In a modeled two-year analysis conducted by Signal65, Dell AI Factory with NVIDIA infrastructure reduced the cost of persistent AI agent deployments by 28% to 90%+ compared to cloud-based AI application programming interfaces.
For federal agencies, token demand should be part of infrastructure planning from the start. Teams need to understand how often agents will run, how many steps they will take, which models they will use, and where inference should happen before selecting cloud, on-premises or hybrid deployment.
How do agencies scale AI responsibly?
A token-smart strategy supports both cost control and responsible scale. It helps agencies decide which workloads belong in cloud environments, which should run closer to mission data, and which require a hybrid approach.
It also reinforces the need for governance. As AI moves into production, agencies must govern model selection, inference operations, agent behavior, token consumption, and workload utilization. Clear policies and monitoring help teams keep AI aligned with mission requirements.
The Dell AI Factory with NVIDIA provides agencies a platform for operationalizing AI across Dell infrastructure and services, integrated with NVIDIA accelerated computing, NVIDIA networking, NVIDIA AI Enterprise software, and NVIDIA NIM inference microservices. Paired with the NVIDIA AI Factory for Government reference design, it supports a repeatable approach to scaling agentic AI responsibly, securely, and efficiently.
Agentic AI can help agencies save time, improve services, and accelerate decisions. Tokenomics helps ensure those gains are supported by infrastructure choices that keep performance, governance, and economics aligned. Learn more: https://www.delltechnologies.com/assetlink/doc/en-us/meritalk-dell-nvidia-ai-moves-missions-forward-ebook-dl2bqz-original.pdf.