AI systems fail gradually and partially, not in clear binary states like traditional software. The model gives a plausible answer missing crucial context, latency spikes but stays under timeout limits, outputs degrade invisibly. Your error handling must match this complexity.
Traditional monitoring catches when systems are down but misses when AI is confidently wrong. You need different metrics for systems that fail quietly - tracking output quality, user satisfaction, and model drift alongside uptime. Learn how to build ai observability monitoring that catches problems before users complain.
In business, a reliable AI agent that works consistently beats a brilliant one that fails unpredictably. Engineering practices for building AI agents that companies can actually trust in production.
Most AI roadmaps focus on capabilities and features when they should focus on reliability and failure modes. With 95% of AI pilots never making it to production and 73% of enterprise deployments failing, your roadmap must prioritize reliable agent patterns over impressive demos. Start with constraints, measure operational health, and plan for continuous iteration.
Most Claude deployments fail at the same point - when complexity exceeds what prompt engineering can handle. The ones that work treat conversation design as infrastructure, not an afterthought. Success comes from systematic patterns for system prompts, context management, error handling, and scaling that survive production reality.
Traditional monitoring tells you if your LLM is running. It does not tell you if it is delivering garbage to users. Quality degradation is silent, and traditional metrics miss it completely. Here is how to monitor what actually matters for LLM applications in production.
Automated RAG evaluation metrics do not predict which systems people trust and use daily. Precision scores and faithfulness ratings miss what matters - user behavior and task completion. Here is how to build evaluation frameworks that measure real success in production AI systems.