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Learn about implementing observability in large language model workflows through a real-world case study of building agentic infrastructure at AlwaysCool.ai. Discover how a team evolved from simple GPT-based tools to production-ready AI microservices, encountering challenges with sync limits, cost control, and debugging visibility along the way. Explore the technical solutions implemented, including LangGraph for orchestrating multi-step flows, FastAPI for serving agents, and OpenTelemetry for comprehensive monitoring. Understand how metrics and traces are sent to Prometheus, Grafana, and LangSmith to achieve real-time visibility crucial for compliance workflows such as HACCP, CAPA, and FDA traceability. Gain insights into practical patterns, tooling decisions, and lessons learned from navigating production pressures in AI middleware development, with specific examples from automating nutritional analysis and FDA label validation processes.
