Opentelemetry

Most AI agent tutorials end at “hello world.” You build a single agent with one or two tools, it answers a few questions, and that is it. The gap between that tutorial and a production system with multiple agents, authentication, observability, and a real frontend is enormous.

When a user asks “Where is my order?”, the request travels through 3 services, 2 LLM calls, and 4 database queries. The orchestrator receives the message, calls the LLM to classify intent, delegates to the order management agent via A2A protocol, which calls its own LLM, executes a tool that queries the database, and returns a response that the orchestrator synthesizes into a human-readable answer.

The infrastructure overhead in microservices projects is real. Setting up databases, wiring service discovery, getting distributed tracing working, managing connection strings across environments—this work hits before you’ve written a line of business logic. On more than one project, I’ve watched the first sprint disappear into Docker Compose files and appsettings drift.

If you’ve ever built a microservices architecture, you know the pain points all too well: spending hours setting up PostgreSQL locally, wrestling with Redis configurations, debugging why RabbitMQ won’t connect, managing connection strings across multiple services, and let’s not even talk about implementing distributed tracing manually.