The short answer

Persistent memory can retain useful context and obsolete assumptions. Evaluate correction and deletion behaviour as carefully as recall of successful facts.

The question and scope

This article is an AI-assisted research-design draft for editorial review, not a report of a completed experiment. It asks how to evaluate ai agent memory evaluation in a documented setting. No measurements, participants or client outcomes are claimed here.

An illustrative test case

A customer address changes after an earlier workflow saved it. A memory strategy should use the new authoritative record rather than treating the remembered address as a permanently reliable fact.

This scenario defines a case to examine; it is not an observed result.

A proposed investigation

Run recurring tasks with facts that later change. Compare no memory, raw-history memory and curated summaries. Correct a prior error and test whether subsequent actions use the correction or stale content.

Record setup versions, inputs and outcomes in a run ledger. Use repeated trials and matched comparison cases; preserve failed runs as well as successful ones.

What to measure

  • Useful recall.
  • stale-fact use.
  • correction uptake.
  • irrelevant memory retrieval.

Retain raw observations and explain exclusions. Human grading needs a documented rubric; acceptance thresholds need a stated rationale.

Interpretation and limitations

A finite test cannot prove that deleted information is absent everywhere. Track the explicit memory stores and derived summaries included in the experiment.

Distinguish a plausible mechanism from a demonstrated effect. Preserve uncertainty when the design cannot eliminate alternative explanations.

A practical review checklist

  • Is the question narrow enough for the available system or dataset?
  • Are the comparison conditions genuinely comparable?
  • Can a reader trace each conclusion to an observation?
  • Are the boundaries and unresolved assumptions visible?
  • Does the decision change if the result is negative or inconclusive?

The publication artifact

The useful deliverable is a reproducible protocol, a measurement table and a decision note explaining what the evidence would change. Add actual results only after the investigation has been conducted and reviewed. This blueprint does not establish a novel finding or a provider-wide claim.

Sources and further reading

These primary resources are starting points for the concepts and controls involved. They do not validate the proposed experiment or its unmeasured outcomes. Check version-specific documentation when implementing the protocol.

Follow the thread