Part 13 — Evaluating and Observing Memory Systems

This section is about measuring whether memory actually works in production — retrieval quality, latency and cost, observability, drift detection, and regression testing.

What you will learn

  • Precision, recall, and relevance metrics for memory retrieval
  • Benchmarking long-horizon conversational memory and consistency
  • Tracing memories from extraction to retrieval
  • Dashboards, A/B tests, and monitoring growth, cost, and signal-to-noise

Chapters in this section

There are 16 chapters in this part. Open any chapter to read it on its own, or work through them in order.

  1. 1 Why Memory Systems Need Their Own Evaluation Framework
  2. 2 Precision, Recall, and Relevance in Memory Retrieval
  3. 3 Benchmarking Long-Term Conversational Memory
  4. 4 Evaluating Memory Consistency Over Long Sessions
  5. 5 Measuring Hallucination Reduction from Grounded Memory
  6. 6 Latency Budgets for Memory-Augmented Agents
  7. 7 Cost Modeling for Memory Pipelines and Storage
  8. 8 Observability: Tracing a Memory From Extraction to Retrieval
  9. 9 Run-Level Debugging in Asynchronous Memory Pipelines
  10. 10 Detecting Memory Pollution and Drift in Production
  11. 11 A/B Testing Memory Configurations
  12. 12 Human Evaluation of Agent Personalization Quality
  13. 13 Regression Testing for Memory Pipeline Changes
  14. 14 Monitoring Memory Growth and Storage Costs Over Time
  15. 15 Signal-to-Noise Ratio in Long-Lived Memory Stores
  16. 16 Building Dashboards for Memory System Health