Five checks for agent memory before using it in production

This article was prepared with AI assistance and reviewed by its author.

A memory feature needs an acceptance test, not just a successful retrieval demo.

Start with one small task whose answer you can check. Record its inputs, expected answer and permissions before enabling memory. Then try five cases:

1. Recall: save a useful project fact, start a new session and check whether the agent retrieves the right source.
2. Correction: replace that fact with a newer decision. Check that the agent uses the correction and exposes the old note as superseded.
3. Missing context: ask about an unstored detail. The agent should expose the gap instead of treating a guess as remembered evidence.
4. Isolation: repeat the question in a separate test workspace. Check that another workspace's private notes do not appear.
5. Deletion: remove the test note and check retrieval again, including any application cache involved in the workflow.

Run the same task without memory as a baseline. Keep a small results table with the case, expected result, retrieved evidence, actual answer and pass or failure. Repeat after changing extraction, retrieval or retention behavior.

This is a proposed evaluation procedure, not a benchmark result. It tests whether memory helps the particular workflow while preserving correction, permissions and deletion controls.

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