Production Ready AI Automations Monitoring, Evals, Cost Caps & Fail Safes Agent Ops Playbook 1

Description

Turn AI agents and automations into something you can trust in production. In this video, we walk through a practical playbook for shipping *production-ready* AI automations: ✅ Monitoring (quality, latency, cost, safety) ✅ Evals (offline regression + online drift) ✅ Cost caps (token budgets, routing, caching) ✅ Fail-safes (timeouts, retries, circuit breakers, human-in-the-loop) Who this is for: - Founders and operators scaling AI workflows - Teams shipping LLM features, copilots, or agentic systems - Anyone tired of “it worked in the demo” surprises Chapters: 0:00 Production-ready AI automations (overview) 0:14 What “production-ready” actually means 0:28 Reference architecture that scales 0:48 Monitoring: what to track 1:08 Dashboards & high-signal alerts 1:25 Evals: offline vs online 1:42 Build an eval suite fast 2:02 LLM-as-judge + human calibration 2:22 Cost caps & anomaly detection 2:41 Fail-safes: circuit breakers & fallbacks 3:02 Human-in-the-loop escalation 3:20 Incident playbook (detect → triage → mitigate → fix → postmortem) 3:39 Pre-ship checklist 3:58 Governance basics (lightweight risk loop) 4:15 Templates + next video #AIAutomation #LLMOps #AgenticAI #MLOps #Observability #OpenTelemetry