Agentic AI just proved it can fix federal procurement — now let’s scale it

The views expressed in this article are those of the authors and do not necessarily reflect the official policy or position of their employers. 

Every year, federal agencies evaluate thousands of vendor proposals worth billions of taxpayer dollars, and they do it with a process that is slow, inconsistent and an inefficient use of personnel time.

Contracting officers manually cross-reference dense submissions against hundreds of Federal Acquisition Regulation (FAR) clauses, Defense Federal Acquisition Regulation Supplement requirements and a growing web of executive orders. Critical gaps remain despite the diligent efforts of procurement teams. Timelines stretch from weeks into months. The cost is mission delay, wasted funding and eroded public trust.

This is not a theoretical problem; it is a daily operational reality. Even as the FAR overhaul aims to simplify compliance, procurement team burdens keep growing.

That’s why the ATARC Agentic AI Lab set out to answer a specific question: can a team of specialized AI agents — not a chatbot or search tool, but autonomous agents working in coordination — evaluate a federal proposal against real regulatory requirements and surface genuine compliance risks? We didn’t want a demo. We wanted proof.

We got it.

Our proof of concept deployed three specialized AI agents: a FAR compliance agent, an executive order agent and a technical evaluation agent against a real-world-modeled $8.5 million vendor proposal for a fictitious agency data modernization initiative. Each agent independently analyzed the submission from its domain, querying curated regulatory knowledge bases and generating detailed findings with precise FAR citations.

The results were striking. The agents identified gaps in small business subcontracting documentation, security framework specifics and cost justification. Where the proposal was strong, particularly its alignment with executive orders on AI policy, the agents recognized that too.

Here’s what matters most: humans never left the loop. The agents performed the analytical labor, document review, citation matching, cross-referencing across domains. Every final determination, every exception judgment, every award decision remained with the reviewer. The AI didn’t replace expertise. It multiplied it.

We also learned what doesn’t work yet. The system needs confidence scoring so reviewers know when to trust a finding. It needs context-aware interpretation for agency-specific deviations and acquisition strategy trade-offs no general model can anticipate. These are solvable engineering challenges, not fundamental limitations.

The broader lesson extends well beyond pre-award compliance review. The multi-agent architecture we validated — specialized agents with distinct knowledge domains, coordinating through a shared orchestration layer — is a reusable pattern. Grant evaluations at federal health agencies. Regulatory impact assessments at EPA. Small business compliance support that levels the playing field for new market entrants. The pattern scales because the problem scales.

Agencies should implement agentic AI tools against real procurement workloads and share findings openly. The Office of Federal Procurement Policy should issue guidance encouraging AI-assisted document review with clear human-in-the-loop standards.

We have the proof. In today’s budget-constrained environment, where every dollar must stretch further and mission delivery is harder than ever, agentic AI offers a genuine path to doing more with less. The question is no longer whether this technology can help. The question is whether we will move from pilot to production before that opportunity passes.

KJ Lian is the global worldwide public sector data and AI sales leader at Amazon Web Services. Anil Chaudhry is a senior advisor for AI at the Department of Transportation. Both Lian and Chaudhry are co-chairs of the ATARC Agentic Al Lab.