Apr 24
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🚀 How do you monitor, debug, and scale AI agents in production? In this video, we break down **observability in Amazon Bedrock AgentCore** — including logs, metrics, traces, and distributed tracing for AI systems. --- 💡 What you’ll learn: • What observability means for AI agents :contentReference[oaicite:0]{index=0} • Built-in metrics from AgentCore (runtime, memory, gateway, tools) • How to enable CloudWatch Transaction Search • Using AWS Distro for OpenTelemetry (ADOT) • Capturing traces, spans, and custom metrics • Instrumenting agent code with OpenTelemetry • Configuring log destinations (CloudWatch, S3, Firehose) • Distributed tracing with headers (traceparent, X-Amzn-Trace-Id) • Session-level observability and debugging • Best practices for monitoring AI systems --- 🧠 Key Insight: AI systems are not black boxes. They generate: • Logs • Metrics • Traces 👉 Observability makes AI systems debuggable and production-ready. --- 📌 Core Concepts: 🔹 CloudWatch Observability Central dashboard for logs, metrics, and traces 🔹 OpenTelemetry (ADOT) Standard for tracing and instrumentation 🔹 Distributed Tracing Track requests across services and agents 🔹 Custom Metrics Add application-level insights --- ⚡ Why this matters: Without observability: → Hard to debug ❌ → No visibility ❌ → Poor reliability ❌ With observability: → Full visibility into AI behavior ✅ → Faster debugging ✅ → Better performance tuning ✅ --- 🏗️ Real-world use cases: • Debugging AI agent failures • Monitoring production AI systems • Performance optimization • Distributed system tracing • Enterprise AI platforms --- 🔗 Topics covered: AgentCore Runtime, observability, OpenTelemetry, CloudWatch, AI monitoring, GenAI architecture --- #AWS #AmazonBedrock #AgentCore #Observability #OpenTelemetry #CloudWatch #GenAI #LLM #AIEngineering