How boards should be measuring autonomous AI risk
A practical reporting model boards can use to govern autonomous agents without pretending to be technical.
Insights
Editorial perspective on identity-first AI, agent governance, machine trust, lifecycle continuity, and the operating practices required to deploy autonomous AI safely.
Now publishing
We are migrating selected Multiplier Partners analysis, frameworks, and field notes from internal advisory work to this hub. New insights will be published here in the cadence they deserve — not on a content calendar.
A practical reporting model boards can use to govern autonomous agents without pretending to be technical.
Working through realistic enterprise threat scenarios for autonomous agents — and the design patterns that actually defend against them.
A deep look at the model gateway, retrieval, agent runtime, identity, and audit as a single control surface.
Why traditional IAM falls short for autonomous AI agents, and what to put in place instead — credentials, crypto-keys, scoped delegation, and audit.
A pragmatic 12–24 month investment sequence that boards and operators both believe in — anchored in identity, governance, and continuity.
The patterns that produce real value from autonomous agents, and the patterns that quietly produce identity and compliance risk.
In the meantime
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