Fewer Hours, Same Scope: Turning AI Agent Spend into Compliant Federal Proposals and Rates

AI agents are landing on federal programs faster than pricing and compliance models can keep up — and the savings they create can quietly turn into questioned costs when the spend isn’t classified, allocated, and documented correctly.

During this interactive discussion, we will show you how to turn AI token spend into compliant federal proposals and rates that hold up under audit. Join Brian Sweeney and Eric Poppe as they unpack the complexities of:

• Allowability Under FAR Part 31: Whether AI token spend is allowable, and where it belongs in your rate structure.

• Standing Up TokenFinOps: The controls, ownership, and visibility needed to govern AI agent spend.

• Translation Into Proposal Terms: How to turn AI-driven efficiencies into labor categories, hours, and scope.

• Allocation and Documentation: A defensible approach for AI costs shared across multiple contracts, ready for DCAA scrutiny.

• Security and Data Sovereignty: How FedRAMP, ITAR, and restricted foreign models affect compliant AI choices.

Learning objectives:

1. Stand up a basic TokenFinOps practice — the controls, ownership, and visibility needed to govern AI agent spend rather than just watch it accumulate.

2. Determine whether AI token spend is allowable under FAR Part 31, and where it belongs in the rate structure (direct vs. indirect, G&A vs. overhead).

3. Translate AI-driven efficiencies into proposal and pricing terms the government already understands — labor categories, hours, and scope — instead of unfamiliar “AI” line items.

4. Build a defensible allocation and documentation approach for AI costs shared across multiple contracts, ready for DCAA scrutiny.

5. Factor in security and data-sovereignty constraints — FedRAMP, ITAR, and restricted foreign models — that can make the cheapest AI option noncompliant for federal work. 

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