Build enterprise-grade context systems that govern what AI agents perceive and act on. Learn to prevent failures such as stale knowledge, privacy leakage, and reasoning drift through structured visibility, policy enforcement, and measurable context health. This 3-hour, hands-on course is aligned to AEBOP T2.2.
AEI members receive 20% off with code MEM_C8_20.
Businesses deploy AI agents expecting reliable reasoning, but ungoverned context systems silently undermine trust and compliance. This module exposes how flat prompts, stale retrievals, and missing policy filters lead to real financial losses—from regulatory fines to operational collapse.
You'll analyze documented failures in life sciences, finance, and supply chain domains, extracting root causes that transcend industries. Through risk assessment frameworks and maturity benchmarking, you'll translate technical gaps into business impact, building the case for context engineering as a required discipline, not an optional enhancement.
Governed perception begins with architecture that treats context as a first-class system layer. You'll design the Context Fabric—a production blueprint separating compilation, routing, anchoring, and monitoring—to ensure visibility remains bounded, fresh, and policy-compliant throughout each reasoning cycle.
The module translates theory into enforceable patterns: layered windows with token budgets, role-aware routing matrices, and temporal anchors with TTL enforcement. You'll produce concrete schemas and integration specs that prevent data bleed, control cognitive load, and maintain audit trails, transforming context from a text buffer into a managed infrastructure component.
Implementation shifts from diagrams to deployable code. You'll assemble the core compiler with token-aware truncation, integrate policy engines for pre-inference enforcement, and establish immutable ledger storage for full replay capability. Each component is built against strict SLOs: 250ms compile latency, 500ms replay, and zero policy violations pre-inference.
Validation ensures reliability before production. You'll construct golden context tests that block schema drift in CI/CD, simulate failure modes like stale retrievals and budget overflows, and verify multi-tenant isolation. The result is a verified context system that integrates with existing knowledge bases, policy vaults, and agent frameworks, ready for staging deployment.
Most production AI failures aren’t caused by model errors; they’re caused by broken context. This module delivers the hard-won operational discipline required to detect and prevent catastrophic context failures before they trigger compliance breaches, security incidents, or business breakdowns. You’ll learn to instrument context health, catch silent failures in real time, and govern perception with the same rigor as uptime and latency.
Here, you’ll move from theory to battlefield-ready operations, implementing the Context Health Score (CHS), configuring P0 alerts for policy violations, and building a replayable audit trail that proves governance. Through proven anti-patterns and real incident case studies, you’ll learn how to transform failures into expertise, ensuring your context system remains aligned, fresh, and trustworthy under real-world pressure.