Understanding Agentic Observability: Reading Traces to Build Reliable AI Agents

Discover how to look "under the hood" of your AI agents using traces in the Lyzr platform. This video demonstrates how agentic observability helps you understand why the same agent might produce different results by showing you the exact tool calls, context received, and model decisions. What you’ll learn: - How to Read Traces: Step-by-step walkthrough using an email summarizer agent demo. - Tool Call Analysis: See which arguments are passed to tools (like Composio) and what data is retrieved. - Performance Metrics: Monitor average latency, error rates, credit consumption, and token efficiency. - Debugging & Reliability: Learn how to identify exactly where an agent is breaking in production by reviewing captured logs and specific action costs. Build more reliable AI agents by digging deep into every action, input, and output with Lyzr Traces. 🔗 Important Links: Build with Architect: https://hubs.ly/Q043pWTs0 Build your own AI agent → https://hubs.ly/Q03wb5Md0 Explore our website → https://hubs.ly/Q03wbGVt0 Build agents for your company (Book a demo) → https://hubs.ly/Q03wbH0k0 Learn how to build agents with Lyzr Academy → https://hubs.ly/Q03wqxFR0