Agent Memory Prototypes
An experimental framework for testing memory retrieval policies under multi-step, high-context agent tasks.
Problem
Agents performed well in short contexts but drifted over long execution windows. Relevant context was either missed or over-included, increasing latency and degrading decision quality.
Why it matters
Memory strategy is central to trustworthy autonomous systems. Poor retrieval leads to silent failure modes that are difficult to detect until business impact appears.
Approach
We benchmarked retrieval policies across recency, salience, and task-intent scoring. Evaluation emphasized reasoning consistency, correction behavior, and context-token efficiency.
Architecture
The prototype stack combined embedding stores, retrieval rankers, and a policy coordinator that selected memory slices before each agent planning step.
Tradeoffs
Heavier retrieval evaluation increased per-step latency. We accepted the overhead in exchange for improved trajectory consistency and better post-hoc interpretability.
Learnings
Agent quality improvements often come from context governance rather than model size. Retrieval policy design is a core systems problem, not a tuning afterthought.