How to evaluate AI agent memory
An article published on June 26, 2026 examines agent memory systems and notes that existing evaluations often treat memory as a black box.
Source: Survey examina a avaliação de sistemas de memória para agentes (arxiv.org). Text prepared with AI from this source.
What happened and what to do
Published on June 26, 2026, the article examines the evolution of memory for language-model agents: persistent storage, retrieval, updating, consolidation, and lifecycle governance. It observes that existing evaluations often rely on end-to-end task metrics and treat memory as a black box.
A company developing or adopting agents can assess its benchmarks to identify which memory capabilities they measure and which they omit. A project could define specific tests for retrieval and updating, track results in a dashboard, and establish lifecycle governance criteria for memory.
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Wendelmaques can diagnose how agent memory is evaluated, define a testing scope, and implement evaluation and monitoring pipelines and dashboards to track results and guide operations.
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