Calling a Model Is Easy. Running an AI System Is Not
Why a successful model call says little about whether an AI feature is reliable, secure, observable, affordable, and ready to operate in production.
Observability
Measure retrieval misses separately from model failures
Give retrieval its own outcome and telemetry so missing or irrelevant context is not diagnosed as a model problem.
Make logs structured before adding dashboards
Capture queryable fields first so dashboards are built on data, not text search.
Observability for Agents with OpenTelemetry, Aspire, and Application Insights
How to make Microsoft Agent Framework behavior visible in production with OpenTelemetry traces, token usage, model calls, tool calls, Aspire local debugging, and Application Insights monitoring.
Prefer health checks that test dependencies deliberately
Health checks should say something useful about readiness without turning every probe into production load.
Use OpenTelemetry before you need production debugging
Add traces, metrics, and logs while the system is still easy to reason about.
Use the Aspire Dashboard Standalone
Stop setting up complex observability stacks for local dev. Learn how to run the Aspire Dashboard as a standalone, lightweight OTLP viewer for any language.
Debugging LLM Timeouts in .NET
A repeatable local setup for timeout triage in .NET LLM workloads using Aspire, OpenTelemetry, and Ollama.