AI Agent Memory Validation & Bias Detection System

source_data_type: "customer service interaction logs", validation_approach: "cross-reference verification" Original logs: Customer complained about delayed shipment on March 15, received partial refund of $50, expressed satisfaction with resolution, mentioned considering future orders. Compressed summary: Unhappy customer got refund for shipping issue in March. Reconstructed memory: Angry customer in March demanded full refund due to shipping problems, relationship damaged.

**MEMORY VALIDATION REPORT** **Source Integrity Score**: 85/100 - Clear factual foundation with specific dates, amounts, and outcomes documented. **Compression Fidelity Analysis**: - Information preservation rate: 40% - Critical details lost: Customer satisfaction with resolution, specific refund amount ($50 partial), future order interest - Introduced distortions: Emotional state simplified to "unhappy" without context **Reconstruction Reliability Assessment**: - Accuracy score: 25/100 - Fabricated elements detected: 3 ("angry" emotion, "full refund" amount, "relationship damaged" outcome) - Confidence intervals for reconstructed facts: Emotional state (20-30%), Resolution outcome (10-15%) **Bias Detection Results**: - Primary biases identified: Negativity bias, availability bias - Severity assessment: Negativity bias (HIGH), availability bias (MEDIUM) - Impact on decision-making: Could lead to defensive customer service approach, missed upselling opportunities **Validation Recommendations**: - Implement source log verification before customer re-engagement - Cross-reference customer satisfaction scores with complaint summaries - Require specific detail confirmation for high-stakes customer interactions - Set confidence threshold of 70% minimum for relationship status assessments