AI Agent Memory Architecture

Problem

Agents that treat every past interaction as equally relevant drown in context: how can an agent retain useful context while selecting what actually matters?

Context

How can an agent retain useful context without treating every past interaction as equally relevant?

Constraints

• Proposed components must stay clearly separated from implemented ones.
• Any retrieval mechanism must be evaluable — selection quality has to be measurable, not asserted.

Architecture

The work examines a layered memory design: working memory for the immediate task, semantic retrieval over embeddings and vector stores for recall, graph relationships for structured context, and a consolidation step that decides what persists.

• Working memoryHolds the immediate task context the agent reasons over.
• Semantic retrievalEmbeddings and vector retrieval surface relevant past content.
• Graph relationshipsEntity and relation structure connects context beyond similarity search.
• ConsolidationMemory consolidation decides what persists and in what form.
• Retrieval evaluationMeasures whether selection actually returns what the task needs.

Implementation

Architecture and experiments are in progress. Proposed components are clearly separated from implemented ones, and no component is presented as complete.

AI / ML role

Machine learning is central to the design under study: embeddings for representation, vector retrieval for recall, and consolidation strategies for managing what persists. No trained models or measured retrieval results are reported here.

Evaluation

No evaluation has been run. Retrieval quality, consolidation effectiveness, and scaling behavior remain unmeasured; the evaluation component exists in the design precisely because selection must be measured rather than assumed.

Results

• Question and scope fixed

  The retention-versus-relevance question and the five-component scope (embeddings, vector retrieval, consolidation, graphs, evaluation) define what any future implementation must answer.

• Evaluation designed in, not bolted on

  Retrieval evaluation is a first-class component of the architecture, so selection quality will be measured from the start rather than asserted after the fact.

Limitations

This is an architecture study: no working memory system is demonstrated and no measurements exist.

The hardest questions — what to keep, what to forget, and how selection degrades with scale — are identified but unanswered.

Findings from any future implementation will be conditional on the embedding models, stores, and workloads used.

Lessons

Treating evaluation as an architectural component forces the design to be measurable before it is built.

Separating proposed from implemented components keeps an exploration honest while it is still in progress.