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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.

We specialize in applying technology, creativity and human-centered services to optimize mission delivery and improve experiences for millions of people who count on government services every day.

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Employees: 300+ nationwide

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09.04.26

5. The Orchestrator Capability Manifest: Governing Tool Access, Prompt Integrity and Model Authorization in Multi-Agent AI Systems

Multi-agent AI systems create an authorization problem most security frameworks were never built to address. Article 5 in Aptive's Agentic AI Governance Series introduces the Orchestrator Capability Manifest, a runtime-enforced policy document that governs which tools each AI agent can use, which prompt version it runs, which model it's pinned to and when a human has to review its output before it moves downstream.

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09.01.26

4. Multi-Model and Multi-Agent AI Systems: Infrastructure Implementation Guide

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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08.31.26

Tammy Czarnecki Named Leading for IMPACT Award Winner by FORUM

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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08.25.26

3. Organizational AI Governance: Frameworks, Artifacts and Implementation Guidance

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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08.18.26

2. Advanced Operational Maturity for Multi-Agent AI Systems

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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