Aptive partners with federal agencies to achieve their missions through improved performance, streamlined operations and enhanced service delivery. Based in Alexandria, Virginia, we support more than a dozen agencies including Veterans Affairs, Transportation, Health and Human Services, Defense, Homeland Security and the National Science Foundation.
The first three articles in Aptive's agentic AI governance series defined the architecture. Article four defines the infrastructure that enforces it: container isolation, sandboxed code execution, zero trust network zones, CI/CD security gates and an AI incident response plan built around CISA's 72-hour reporting window.
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Aptive congratulates Tammy Czarnecki, executive vice president for health services, on being named a winner of FORUM's Leading for IMPACT, Women in Leadership award.
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Most AI governance failures aren't technical. They're organizational. In Article 3 of Aptive's Agentic AI Governance series, Ian Meinert breaks down the nine artifacts that turn NIST AI RMF requirements into working governance: risk tolerance statements, accountability matrices, impact assessments, bias evaluation frameworks and more. If leadership hasn't defined what risk the organization accepts, who owns what and what happens when something goes wrong, the technical controls aren't enough.
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Passing an ATO review is not the same as staying compliant. This article maps the five gaps between a deployed multi-agent AI system and an operationally mature one: measurement, model risk, data governance, human oversight and continuous assurance, plus the evidence model auditors actually expect to see.
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Agentic AI systems make decisions across multiple steps with limited human review at each one. The NIST AI Risk Management Framework is the federal government's reference point for governing them, and it's the standard agentic AI programs will be measured against. This is article one of a seven-part series on what it takes to govern these systems well.
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