Observability for Agents with OpenTelemetry, Aspire, and Application Insights

<p>A user reports that an AI application's response is bad. The dashboard, however, is green: 200 OK, latency within the expected percentile, clean logs. The complaint is

<h2> Introduction </h2> <p>Most LLM tutorials assume you start from nothing. A blank project. A clean architecture. No constraints. No legacy code. No deployment history. No

<p>Most developers who build AI agents make the same mistake: they spend weeks designing the orchestration layer, tuning the system prompt, and picking the right model — then hand

<p>Multi-agentic architectures can pose challenges from an observability perspective. Each agent within a workflow makes its calls to its own toolset and each agent may be

<p>You've deployed your fifth microservice. Requests flow through an API gateway, hit an order service, fan out to inventory and payment, and eventually land in a notification

<p>Your agent passed all the tests. It's been running in production for three weeks. And it has been quietly wrong the entire time.</p> <p>This is not a hypothetical. Galileo's

<p>Salesforce just flipped a switch at TDX 2026 that matters more than most announcements in recent memory: your Salesforce org is now an MCP server. Every Developer Edition org

<p>In this blogpost I am exploring how a framework that translates code into a graph fits within the observability stack. Intuitively, something that helps decompose a pipeline