Building Safe AI Agents: Best Practices

Building AI agents that are safe and reliable isn't just good practice—it's essential for maintaining trust and avoiding costly failures. ## The Safety Imperative As AI agents take on more critical tasks, the consequences of failures grow. A customer support agent that hallucinates could damage your brand. A data processing agent with security flaws could expose sensitive information. ## Key Safety Principles 1. **Defense in depth** - Multiple layers of protection 2. **Graceful degradation** - Fail safely when things go wrong 3. **Human in the loop** - Know when to escalate to humans 4. **Comprehensive logging** - Track everything for debugging and auditing ## Implementation Tips Start with the assumption that your AI will make mistakes, and build systems that can detect and recover from them.