Your AI applications aren't purely "AI." They're a mix of traditional services and non-deterministic agent behavior running together. A product recommendation agent, for example, might be slow because of an inefficient method in your backend code or a poorly optimized database query. Or the agent itself could be stuck in a retry loop, pulling irrelevant context, or calling a model that adds unnecessary latency. The same input won't always produce the same response, and the root cause of a regression could live in any layer of the stack. For organizations that need to deliver secure, reliable, and trustworthy AI experiences, visibility has to span both worlds, unified in one place.

In this webinar, we'll walk through how modern engineering teams trace issues end-to-end across the full AI application stack. We'll start with Application Performance Monitoring, where distributed traces surface where time is actually being spent. From there, we'll follow a real-world debugging workflow down through code-level profiling to identify inefficient methods, into database query performance to catch slow or malformed queries, and finally into the agentic layer. There, LLM Observability lets you trace agent behavior, run structured experiments to validate prompt and model changes, and continuously evaluate for quality, security, and safety from pre-production through production.

You'll walk away with a practical playbook for pinpointing whether a problem is in your code, your infrastructure, or your agent, and resolving it faster regardless of where it lives.

Key Takeaways:

1. How to trace issues end-to-end across the full AI application stack

2. How to follow a real-world debugging workflow from infrastructure to agent

3. How to validate AI changes and evaluate quality continuously