As organizations race to ship AI features and deploy

agentic workflows, many find themselves operating with

limited visibility into what their systems are actually doing.

Traditional monitoring tools often fall short when faced

with the unique challenges of AI, such as model drift,

hallucinations, and data quality issues. This lack of

reliability and trust are two primary barriers preventing

teams from scaling AI to production.

Join experts from Red Hat and Dynatrace to learn why

end-to-end AI observability is critical for transforming AI

experiments and technical KPIs into measurable business

outcomes. Topics to be covered in this moderated

discussion include:

• The state of enterprise AI: Why traditional tools fail

and how AI observability provides the trust needed to

scale.

• The power of open standards: How OpenTelemetry

and open source formats allow customers to retain

control of their data, while standardizing signals

across the entire stack for accurate root cause

analysis.

• Key considerations for AI observability: How Red Hat

OpenShift AI seamlessly delivers rich telemetry to

Dynatrace for advanced signal analytics and real-time

dependency mapping.

• Proving ROI: Real-world examples of customers

building trusted, scalable, and cost-optimized AI

systems.