Give an agent memory and it gets more personal. Whether it gets more correct is a separate question that almost nobody tests.

Retrieval systems optimize for semantic relevance to the user. That is the wrong target. A user's savings goal is essential context for one question and active distraction for another, and no similarity score will tell you which situation you are in.

Bring a laptop. We run one seeded memory set through a financial agent across two contrasting decisions and watch the same stored fact improve one answer and degrade the other. Then we demonstrate the counterfactual harness: delete one
memory, rerun, and measure what changed. That single technique answers a question most memory stacks cannot, which is whether a specific memory helped a
specific decision.

Attendees leave with the repository, a seeded dataset, and a memory metadata schema to finish at home.

Python and an API key required. A hosted fallback environment is available for anyone whose setup does not cooperate.

Key Takeaways:
- Why relevance to the user is not relevance to the decision
- The counterfactual removal technique, demonstrated live
- A memory metadata schema covering freshness, decision scope, and sensitivity
- Working code to continue with after the session