End-to-End Observability for AI Agents — OpenTelemetry, MCP, Semantic Search, Next.js & Docker

Enhancing AI Agent Monitoring: A Comprehensive Guide with OpenTelemetry, MCP, Semantic Search, Next.js & Docker

Understanding and maintaining complex AI-driven applications can be challenging without proper observability tools. In this guide, we explore a comprehensive approach to building transparent, debuggable, and reliable AI agent ecosystems.

Key Components Covered:

• Implementing a complete OpenTelemetry integration spanning traces, logs, and metrics to ensure full visibility into your system’s behavior.
• Developing a custom Model Context Protocol (MCP) server to streamline communication and context management within your AI pipeline.
• Incorporating Semantic Search capabilities using Qdrant, with a modern front-end built on Next.js and orchestration managed via .NET and Docker containers.

This setup aims to transform your AI workflows from opaque processes into fully observable pipelines—reducing guesswork and fostering trust in your AI solutions.

For an in-depth walkthrough, including step-by-step setup instructions and detailed notes, visit our comprehensive tutorial at → https://go.fabswill.com/otelmcpandmore.

We’re keen to hear your thoughts on telemetry and tracing patterns for AI-centric platforms. Share your ideas and feedback to help shape more robust, transparent AI systems.