Even when an agent holds the right permissions, "should it take this action?" is a separate question 🛡️ Securing AI Agents: Lessons From Real-World Deployment Uber has shared the design and lessons of "Uber Secure AI," its defense-in-depth framework for protecting its own AI agents. Authorization answers who may act, but agents also need a layer that checks whether an action fits the request. Three highlights: 📜 Register a behavioral contract The AI Registry records each agent's persona, permitted intents, constraints, and risk tier. Argus derives actual capabilities from code and prompts and checks them against what was declared. ⚡ Decide at runtime with small models AI Guard evaluates proposed actions and enforces its verdict before they run. Because it sits in the execution path, it uses small specialized models hosted locally and running in parallel, not remote general-purpose LLMs. 🎯 Limiting intents and constraints worked best Restricting agents to explicit intents and constraints greatly reduced their ability to act on manipulated instructions, at the cost of some flexibility. Easy onboarding is treated as part of the control so developers don't bypass it. It's also useful that the authors say plainly that guardrails reduce risk but don't eliminate it. No quantitative results are given. #AIAgents# #AISecurity#