Mar 28
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📊 AGENTOPS OBSERVABILITY — Monitor • Trace • Optimize AI Agents As AI agents become more autonomous and multi-step, observability is no longer optional it’s critical. Just like DevOps has monitoring and logging, AI systems now need AgentOps a framework to monitor, trace, and optimize AI agent workflows. Here’s what matters in AgentOps: 🔍 Distributed Tracing — Track every AI request as a trace 🧩 Spans — Each tool call, API call, or LLM call is a span 🔗 OpenTelemetry — Industry standard for traces, metrics, and logs 📊 Metrics — Token usage, latency, errors, API calls ⚖️ Sampling — Control observability cost with probabilistic sampling 🔄 Context Propagation — Maintain trace across services 🛠️ Prometheus — Metrics monitoring and alerting 💡 Final Takeaway: Tracing + Metrics + AI Monitoring = Reliable & Scalable AI Systems Use cases: ✔ Debug LLM workflows ✔ Monitor AI agents in production ✔ Optimize performance and cost AI is not just about models anymore — it’s about operating AI reliably at scale. #AgentOps #Observability #OpenTelemetry #Prometheus #AIEngineering #LLMOps #MLOps #DevOps #AI ✨ The ThinkLab by Saurabh