How to Build and Optimize AI Models for Real-World Applications

Building a production-ready AI agent requires solving six critical challenges: configuration management, state and memory design, security boundaries, testing, monitoring, and cost control. Unlike demos, real-world agents face unpredictable inputs, parallel sessions, and security threats like prompt injection, requiring automated eval gates, human approval checkpoints for risky actions, and continuous feedback loops that turn production failures into future test cases.