Insights

Insights

Insights into the issues that matter most, from Aptive’s leading industry experts, clinicians and leaders. Check out our case studies, featured content, news and award announcements.

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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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09.14.26

6. Multi-Model and Multi-Agent AI Workflows: Architecture, Risk and DevSecOps Controls

Multi-model and multi-agent AI architectures are now the default way serious enterprise and federal AI work gets built. That power comes with real tradeoffs: expanded attack surface, harder audit trails and governance gaps most teams underestimate. Article 6 of Aptive’s Agentic AI Governance Series lays out the risk taxonomy, a control baseline mapped to NIST 800-53 and the architecture patterns that hold up in regulated environments.

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09.21.26

7. Model Context Protocol (MCP) Servers in Enterprise AI Architecture

Security researchers have flagged real vulnerabilities in the MCP ecosystem, but nearly all of them trace back to public, third-party servers. In the last paper of Aptive's Agentic AI Governance Series, Ian Meinert separates the risks that actually carry over to self-hosted MCP from the ones that don't, and lays out what federal contractors still owe their agencies as proof they got it right.

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