Comparing two agent versions using traces is the most rigorous way to evaluate whether a change improved, degraded, or had no effect on agent behavior. The process is: same inputs, both versions, side-by-side trace comparison at the changed step.
Why You Need Traces for Agent Comparison
Without traces, agent comparison is impressionistic. “Version B’s outputs seem better” is an observation, not evidence. With traces, you can point to specific steps where the reasoning changed, specific tool calls that now return better data, and specific decision points where the updated prompt produced a more appropriate branch. The trace turns a subjective improvement into a documented one.
This matters especially when you’re making changes that affect student-facing behavior. “I think the new version is better” is not a sufficient basis for deploying a change that affects how your campus AI responds to students. “The trace shows that version B correctly identifies the student’s course level in step 3, where version A was defaulting to beginner in 40% of cases” is.
How to Run a Trace Comparison
Prepare three to five test inputs that represent typical real-world cases — include at least one edge case. Run both agent versions against each input and save the traces. For each trace pair, start at the step where you made your change and read both traces forward from that point. Note differences in: what reasoning the agent applied, which tools it called and with what parameters, which branches it took at decision points, and the final output it produced. Summarize your findings in a simple table: input, version A outcome, version B outcome, which is better and why.
What This Means for Educators
Building a habit of trace-based comparison before deploying agent updates is how you develop a well-tested, continuously improving campus AI system — rather than one that changes unpredictably every time you adjust a prompt. It takes 20 extra minutes. It prevents the kind of regression that takes hours to diagnose after the fact.
The Simple Rule
Same test inputs. Both versions. Compare traces at the changed step. Document what you found before you deploy.