Troubleshooting and Monitoring Applications
in watsonx.ai
Enabling enterprise developers to optimize and analyze AI performance via automated tracing and observability.
OVERVIEW
We don't need more applications; we need better visibility.
Watsonx.ai has made it easier for developers to build AI solutions.
But as these solutions grew more sophisticated, it also became harder to understand and troubleshoot. Developers lacked visibility into how their applications executed, making it difficult to diagnose failures, track usage and costs, and effectively manage their expanding portfolio of AI applications.
Existing platforms weren’t designed to meet these unique needs, leaving developers without a clear understanding of what was happening behind the scenes.
I led the end-to-end design of a new observability and tracing experience that transformed complex AI behavior into clear, actionable insights.
ROLE
Product Designer
UX Designer
TIMELINE
6 months, 2025
SKILLS
Product design
UX design
TEAM
2 researchers
3 developers
CHALLENGE
How can we provide granular visibility into generative and agentic AI applications?
Granular visibility provides a detailed view of how an application runs, helping developers understand its behavior, identify issues, optimize performance, and reduce costs.
The challenge was integrating this capability into the watsonx.ai ecosystem in a way that scaled with evolving AI workflows and remained approachable for our users.
GLOSSARY
Observability: seeing what’s happening inside an AI application in real-time to understand how well it’s working and why it’s behaving a certain way.
Tracing: one aspect of observability; it captures step-by-step records (traces) of what an application does as it runs.
Trace: a complete record of one AI interaction: what a user asked, what the model did, and what happened next.
Watsonx.ai: IBM’s enterprise AI development studio that helps developers create, customize, test, and launch AI applications – all in one place.