Vector Databases for LLM Systems: Foundations, Architectures, and Emerging Directions
A practical look at how vector databases power RAG systems, improve retrieval quality, and support real-world LLM applications.
A practical look at how vector databases power RAG systems, improve retrieval quality, and support real-world LLM applications.
Overview
A technical deep-dive into building observability into production AI agents. This session covers distributed tracing for multi-step agent workflows, structured logging for agent reasoning steps, metrics collection (latency, cost, success rates), and real-time monitoring dashboards. We examine integration with observability platforms (Datadog, Grafana, LangSmith), implementing custom instrumentation, tracking token usage and costs, and debugging agent failures in production. Topics include analyzing agent decision paths, identifying bottlenecks in agentic workflows, alerting on anomalous behavior, and building replay systems for reproducing agent interactions.
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