AI Observability: How to Trace and Debug AI Systems With Confidence

AI-powered applications are reaching production fast. But once they’re live, many teams discover a hard truth: traditional observability tools were not built to explain AI behavior.

You can have healthy uptime, acceptable latency, and stable throughput and still have an AI system that is producing poor answers, calling the wrong tools, drifting off-task, or quietly driving up token costs.

That gap gets expensive quickly:

• Longer debugging cycles across dev, platform, and ops teams
• Limited visibility into prompts, retrieval, and tool orchestration
• Production incidents with no clear root cause
• Growing cost and risk as agent workflows become more complex

Join us for a webinar exploring why AI systems require a different observability model than traditional APM. We’ll break down what changes across the engineering lifecycle as AI moves from prototype to production:

• The difference between measuring system outputs and AI behavior
• Common failure scenarios in multi-step agent workflows
• Which production signals actually matter, from prompts to token usage
• How modern AI observability helps teams trace, diagnose, and fix issues faster

Claim your free spot to get a clear, engineering-first approach to AI observability and start debugging AI systems with confidence.