Monitoring an agent is not the same as monitoring a service. The question shifts from whether it is running to whether it is reasoning correctly — and that requires a different observability stack built around structured traces, quality metrics, and cost attribution.
Non-deterministic systems require evaluation strategies that traditional QA cannot provide. Closing the gap requires a golden dataset, trajectory analysis, an LLM-as-judge pipeline, and a feedback loop that runs before every deployment.
Most ‘my agent broke’ investigations don’t end at the model. They end in memory design, tool scope, orchestration logic, or missing observability. This post covers the plumbing that actually determines whether an agent works in production.
A deep-dive into every layer of a production-grade, fully open-source stack for self-hosting large language models — from the API gateway to the GPU compute plane.
Most teams use Claude Code like a smart autocomplete. The teams getting real leverage treat it as an engineering platform — with CLAUDE.md, permissions, hooks, sub-agents, skills, and CI/CD integration designed in from the start.
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