Maintain control without sacrificing innovation. True AI agents, where LLMs orchestrate workflow execution, can automate work once deemed impossible or impractical. To unlock this potential responsibly, AI agents need to be trusted, compliant, and built to mitigate risk. Guardrails help enable this, when they're focused, efficient and effective. OpenAI just released foundational guidance for building AI agent guardrails: 1\ Set up guardrails tailored to known risks in your use case, then layer in new ones as you uncover new vulnerabilities. 2\ Well-designed guardrails help manage, for instance, data privacy (e.g. prevent system prompt leaks) and reputational risks (e.g. enforce brand-aligned behavior). 3\ Guardrails are critical for any LLM-based deployment, but should be paired with robust authentication, access controls, and standard software security measures. 4\ Think of guardrails as a layered defense mechanism. Leveraging multiple, specialized guardrails together typically creates more resilient agents. P.S. I’ve created a graphic below that breaks it down further. The full OpenAI guide is linked in the comments. --- ♻️ Repost to help your network level up! 📌 Want the top 1% of agentic/gen AI for enterprises? Get on the list: https://lnkd.in/eHEzF_PF | 24 comments on LinkedIn