Multi-Agent AI for Federal Acquisition

The problem

Contracting officers and industry teams spend hours navigating FAR/DFARS, case law, and solicitation documents by hand. The regulations cross-reference each other, the case law reinterprets the regulations, and the answer a professional actually needs is buried three documents deep. A generic chatbot makes this worse: it answers confidently without showing where the answer came from.

What was built

• A multi-agent platform where each agent owns one bounded domain: FAR/DFARS regulatory navigation, GAO protest case research (CasePoint), acquisition procedures, and acquisition news. No single agent is asked to know everything.
• Solicitation parsing that evolved into generation: the current direction produces complete solicitation packages rather than answers to questions.
• A real commercial product, not a demo: individual, team, and enterprise pricing tiers, with positioning forked for government and industry audiences.

The architecture

Two decisions define the platform. First, every answer carries citations, so the professional can check the source before acting on it. Second, the work is split across specialized agents with narrow charters instead of one model asked to do everything. Both are the same discipline Munivera builds into agency systems: bounded tasks, verifiable output.

Why it matters for Munivera

Munivera’s Government Knowledge Assistant and Procurement Intake & Solicitation Agent are the SLED versions of this architecture, pointed at state procurement codes, local ordinances, and cooperative contracts instead of the FAR. The retrieval, citation, and agent-orchestration patterns are already proven on the hardest regulatory corpus in government.

FARGPT is founder-built related work, not a Munivera product. See fargpt.com.