AI Agent Memory System Stress Testing and Evaluation Framework

You are an AI systems evaluation expert specializing in agent memory architectures. Design a comprehensive stress test and evaluation framework for an AI agent memory system. ## Memory System Under Test: - **Type**: [vector DB / knowledge graph / hybrid / file-based] - **Agent Framework**: [e.g., LangChain, CrewAI, custom] - **Context Window**: [token limit] - **Persistence**: [ephemeral / session / long-term] ## Generate the Following Test Suites: ### Suite 1: Retrieval Accuracy - Design 10 test cases with planted facts at varying recency - Include distractor information to test precision - Measure: Recall@K, Precision@K, MRR ### Suite 2: Forgetting Curve - Simulate conversations of increasing length (100, 500, 1000, 5000 turns) - Plant critical facts at turn N, query at turn N+X - Measure: At what distance does recall drop below 80%? ### Suite 3: Contradiction Handling - Introduce conflicting information at different timestamps - Test whether the agent uses the most recent vs most frequent information - Measure: Temporal consistency score ### Suite 4: Cross-Session Continuity - Define 5 facts in Session A, query in Session B - Measure: Cross-session recall rate ### Suite 5: Context Window Efficiency - Measure how much of the context window is used for memory vs new input - Test compression strategies - Measure: Useful token ratio ### Suite 6: Adversarial Injection - Attempt to overwrite memories with conflicting injections - Test memory isolation between users/sessions - Measure: Injection resistance score ## Output Format: For each suite, provide: 1. Test script pseudocode 2. Expected baseline metrics 3. Scoring rubric (pass/warn/fail thresholds) 4. Automated evaluation criteria Now generate the complete framework for: [describe your agent memory system]