We have developed a multi-agent chatbot using LangGraph within the Databricks environment. The solution is functional, but we are facing challenges related to performance observability and end-to-end optimization.
We need guidance in the following areas:
Tracing and Logging Enablement
How to implement effective distributed tracing and structured logging across LangGraph agents, Databricks components, and external model calls to identify bottlenecks.
Vector Index Optimization
Best practices for optimizing our vector index (index type selection, parameters, retrieval tuning) to improve retrieval accuracy and reduce latency.
Gemini External Model API Optimization
Recommendations on improving performance and cost efficiency of Gemini API calls, including batching, streaming, prompt optimization, and retry patterns.
Response Latency Analysis & Architecture Review
We are experiencing higher-than-expected response latency. We need help validating whether our current architecture and implementation approach is optimal, and identifying improvements if not.
Looking for expert insights, recommended configurations, code samples, or architectural guidance to help us tune the system for lower latency, better observability, and more efficient multi-agent performance.
