AI Evaluation & Monitoring

The recent surge in AI evaluation and monitoring reflects an urgent need for robust methodologies. Videos like "How to Build AI Products That Work | LLM Evaluation Guide" by AI with Lena Hall and "The Hardest Problem in AI: Evaluation in 2025 with Ian Cairns" highlight critical frameworks for assessing LLM performance. These resources emphasize the necessity of use-case driven evaluations, revealing how establishing effective metrics not only enhances product reliability but also fortifies the security posture against supply-chain vulnerabilities. With increasing complexity in AI systems, practitioners must ensure that evaluation processes are both scalable and adaptable to evolving threats. Moreover, tools like Azure Cosmos DB for building evaluation pipelines and NVIDIA's NeMo for agent assessments showcase the industry's shift toward integrated observability solutions. As seen in "Gain Complete Visibility into AI Agents | AgentCore Observability | Amazon Web Services," comprehensive monitoring is essential for mitigating risks associated with AI deployments. The ongoing discourse around AI observability, as discussed in videos from AWS and Splunk, suggests that while these tools offer significant advantages, they also introduce new challenges—akin to a double-edged sword. The drive for escape velocity in AI development necessitates that engineers prioritize not just functionality, but also the security frameworks that protect against malicious exploitation and ensure resilient AI operations.