AgentOps for Real: Evals, Tracing, and Regression Tests for AI Agents

Brian Haydin

Shipping an AI agent without observability is like deploying a distributed system with no logging. Learn how to implement evals, tracing, and regression testing so your agents don’t quietly degrade into chaos.

AI agents behave probabilistically — and that breaks traditional testing assumptions.

So how do you:

• Detect drift?
• Prevent silent regressions?
• Prove your system still works after model updates?
• Debug weird edge-case behavior?

In this session, we’ll walk through a practical AgentOps stack:

• Structured evaluation datasets
• Golden test prompts
• Scoring strategies (semantic similarity, rule-based, hybrid)
• Trace capture and replay
• Logging intermediate reasoning steps
• Monitoring production agents
• Designing feedback loops

We’ll implement examples using Azure-based tooling and .NET, including how to structure regression tests for multi-step tool-using agents.

You’ll leave with a repeatable pattern for treating agents like real software — not magic.