Blog
Articles about LLM observability, cost tracking, and building AI infrastructure.
Building Cascades and Guardrails into Your LLM Agent: From Demo to Production
From a naive triage agent to cascaded, guarded, and logged at every step. Twice, once in pydantic-ai and once in majordomo-llm, so you can see what each idiom gives you.
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Beyond the Agent Framework: Two Layers Every Production App Needs
Cascades for reliability, guardrails for governance. The two layers that turn an LLM demo into a production agent, illustrated with a customer support triage agent.
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Running a Live Model A/B Test with Majordomo Experiments
Replay tells you how a cheaper model performs on historical traffic. Experiments tell you how it performs on the traffic you're getting right now. Here's how to run one.
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How to Test a Model Switch Without Breaking Production
A new model drops and it's cheaper or faster than what you're running. But you don't switch — because you don't know what you'd be giving up. Here's how to find out before you commit.
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How to Track LLM Costs by Team and Feature
Your AI bill is growing fast, but your provider's dashboard won't tell you which team or feature is responsible. Here's how to get attribution at the request level.
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Your AI Stack Needs a Control Plane
You trace every database query and monitor every microservice — but your LLM calls run in total darkness. Here's how to fix that with Majordomo.