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Behind every breakthrough in Veterans' health care is a team keeping the science moving. Through VA's Field Enterprise Research Support Services (FERSS), Aptive's embedded specialists support research at VA medical centers and central offices nationwide, spanning data analytics, biostatistics, research administration and training. Meet the people making it possible.
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Aptive CMO Dr. Adi Gundlapalli has briefed members of White House Task Forces, led federal responses to COVID-19, Mpox and Marburg, and still sees patients every Tuesday. He sat down to talk about his career, why informatics is both a science and a daily practice, and what it will take for federal health modernization to hold up in the real world.
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VA is deploying a single Federal Electronic Health Record across 1,380 sites of care, the largest integrated health system in the nation. Aptive is helping standardize the operating model behind that shift, using Scaled Agile Framework (SAFe) to align workflows, cut resolution times and keep pace with a program that changes constantly.
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Federal programs are deploying agentic AI faster than governance frameworks can follow. This seven-paper series, written from the practitioner's side, covers what actually has to work when multi-agent AI moves from pilot to production: security architecture, trust boundaries, governance artifacts, infrastructure and the evidence trail that gets a system through ATO.
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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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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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