Keeping a live AI system working
Launch is not the finish line. AI systems decay rather than crash, which is why standard monitoring never catches it. What breaks after go-live, and what operating one properly involves.
Everything we've written on this
LLMOps: how to run LLM apps and AI agents in production
LLMOps is how you keep LLM apps and agents working after launch: versioned prompts, regression evals, guardrails, tracing, cost budgets and model upgrades.
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The security checklist to run before real users reach your AI-built app
Vibe coding security checklist for Lovable, Bolt, Replit, Cursor and Claude Code apps: 13 checks from Supabase RLS to AI spend caps, each with a 10-minute test.
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AI agent observability tools: what to capture, and which tool fits your team
AI agent observability tools compared: Langfuse, LangSmith, Phoenix, Helicone, Braintrust, Weave, Datadog and OpenTelemetry, priced on 23 Oct 2026.
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Why AI systems break after launch — and what "operating" one means
AI systems don't crash. They decay while dashboards stay green. Five ways a live system degrades, and what AI system maintenance actually is.
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What an AI operations runbook actually contains
Seven entries, each a question someone asks at an inconvenient hour. The runbook that turns a live AI system into something a person can be on call for.
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How to hand over an AI system — to a new vendor or in-house
AI system handover: the inventory to collect, the test that proves it worked, and the four things that always go missing.
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