C009: Agentic Memory Engineering

This module confronts the harsh reality of memory failures in production AI systems. Through real case studies from healthcare, finance, and customer support, you'll learn to identify the three critical failure axes: drift, leakage, and compliance violations. We'll analyze why traditional approaches to memory fail in agentic systems and quantify the business impact of uncontrolled memory.

You'll then master the Agentic Memory Engineering framework and its maturity ladder (L0-L4). Through guided self-assessment, you'll pinpoint your organization's current maturity level and define a target state aligned with your risk profile and use case requirements. The module concludes with a practical roadmap for achieving quick wins and establishing measurable improvement goals.

This module delivers the architectural blueprints needed for enterprise-scale memory systems. We start with the Policy-Aware Memory Pipeline reference architecture, breaking down each component from ingestion through retrieval and auditing. You'll learn how to integrate memory systems with existing context engineering, AgentOps, and governance frameworks, with specific patterns for both centralized and federated deployments.

We then dive into six essential design patterns proven in production environments. Each pattern includes clear implementation criteria, trade-off analysis, and integration guidance. You'll learn when to apply scoped session isolation versus typed memory structures, how to implement hybrid retrieval for compliance, and establish compression-decay loops for cost control. These patterns form the building blocks for any compliant memory system.

This hands-on module transforms theory into immediately deployable code and practices. We begin with the seven core implementation practices that separate experimental from production-ready memory systems. You'll implement metadata tagging, TTL enforcement, early policy binding, and audit logging with our provided templates. The "4R Rule" (Record, Retain, Recall, Retire) becomes your implementation checklist.

We then provide production-ready code templates for memory schemas, write/retrieve/expiry operations, and security patterns including namespace isolation and redaction. The module culminates with a comprehensive testing framework featuring forgetting tests for CI/CD pipelines, isolation validation for multi-tenant systems, drift detection automation, and load testing protocols. You'll leave with a complete validation suite for memory systems.

This module delivers critical field wisdom by analyzing real production failures and anti-patterns from agentic memory deployments. This module synthesizes documented case studies of healthcare data leakage, financial compliance drift, and customer support system decay into actionable patterns that practitioners must recognize and avoid. Through an anti-pattern table and tool stack analysis, teams learn to identify governance gaps before they cause compliance violations or trust erosion.

The module culminates in a hands-on lab where practitioners conduct an anti-pattern audit and design remediation for a realistic scenario. This practical exercise reinforces that memory engineering success depends on governance rigor—implementing isolation boundaries, enforcing intentional forgetting, and maintaining continuous correction cycles rather than chasing technical sophistication.