Five Primitives for Governing Autonomous AI Agents at Runtime
What happened
New research introduces five primitives for governing autonomous AI agents at runtime, addressing the shortcomings of traditional control models in enterprise deployments. Traditional models fail because AI agents are ephemeral, their actions are model-selected and unpredictable, and their populations are dynamic.
Why it matters
Organizations deploying autonomous AI agents must shift from traditional control models to runtime governance, implementing specific primitives like identity and attestation to manage ephemeral agents and unpredictable actions effectively, as current frameworks are insufficient.
Topics
- Autonomous AI Agents
- AI Governance
- Runtime Security
- Policy Enforcement
Articles in this trend
- Five Primitives for Governing Autonomous AI Agents at Runtime — Artificial Intelligence
- Does Your “Agent Governance” End Up Governing Everything But The Agent Itself? — Featured Blogs - Forrester
- When agents act on their own, governance has to live in the data layer — VentureBeat
- A Contract-Centered Architecture for Scalable and Manageable Agentic Runtimes — Artificial Intelligence