Conclusion and next steps

Congratulations! You’ve completed the AgentOps Observability with Red Hat AI workshop.

What you’ve learned

Core observability (Modules 1-4):

  • Explored a production-grade multi-agent mortgage lending system built with LangGraph

  • Understood the 3 pillars of observability and how they apply to agentic AI

  • Explored Grafana dashboards with agent-specific metrics and KPIs

  • Configured MLflow tracing for multi-agent workflows and MCP tool calls

Advanced quality assurance (Modules 5-6, if completed):

  • Implemented LLM evaluations for quality assurance

  • Automated evaluations with AI Pipelines for continuous quality monitoring

  • Caught a prompt regression before production by comparing evaluation results across prompt versions

You now have the practical skills to implement AgentOps observability at your organization.

Key takeaways

The most important concepts to remember:

  1. Agentic AI apps fail distributedly: Multi-agent systems require observability across the entire workflow, not just individual components. Traditional monitoring falls short.

  2. The 3 pillars work together: Metrics detect anomalies, traces locate problems, and logs explain root causes. Effective AgentOps requires all 3.

  3. Different personas, different needs: SRE/Platform Engineers focus on system health and SLOs; AI Developers focus on model quality and behavior. Both perspectives are essential for production AI.

  4. Tracing is the cornerstone: MLflow tracing provides the end-to-end visibility needed to understand multi-agent decision paths and diagnose distributed failures.

  5. Evaluations ensure quality: Observability tells you if systems work; evaluations tell you if they work well. Continuous evaluation prevents quality regression.

Fed Aura Capital’s transformation

Remember where we started? Fed Aura Capital was facing:

  • Blind spots in agent interactions

  • Hidden latency bottlenecks

  • Silent failures in MCP tools

  • No quality baseline

With the AgentOps observability stack you implemented, they now have:

Before After

Hours to diagnose issues

Minutes to pinpoint root cause

No visibility into agent decisions

Complete trace of every request

Reactive incident response

Proactive alerting and monitoring

Unknown quality levels

Baseline metrics with regression detection

Next steps

Ready to continue your AgentOps journey? Here are some recommended paths:

Apply to your own systems

  1. Start with tracing: If you only implement one thing, make it MLflow tracing—it provides the most value for debugging agent failures.

  2. Define your personas: Identify who needs observability data (SREs, AI developers) and what questions they need answered.

  3. Build incrementally: Start with basic metrics, add tracing, then implement evaluations. Don’t try to do everything at once.

  4. Practice incident response: Run game days with simulated failures to validate your observability stack before real incidents occur.

Recommended resources

Deepen your knowledge with these resources:

MLflow and tracing

Red Hat OpenShift AI

Multi-agent frameworks

Observability

Get certified

Validate your expertise with Red Hat certifications:

Share your feedback

Help us improve this workshop. Tell us what worked, what didn’t, and what topics you’d like to see covered next.

Contact us with your feedback!

Thank you!

You’ve taken an important step toward making your multi-agent AI systems observable, reliable, and maintainable.

Remember: Agentic AI apps don’t fail silently. They fail distributedly. But with proper observability, you can see everything, diagnose quickly, and maintain quality.

Keep building, keep learning, keep observing!