Learn Agentic Knowledge Engineering, the discipline of engineering knowledge as a live, policy-bound service. In this 3-hour, hands-on course, move beyond static repositories to build a self-healing knowledge fabric. Apply AEBOP T2.1 patterns, code, and trust metrics to eliminate knowledge drift and make truth observable and auditable in production.
AEI members receive 20% off with code MEM_C7_20.
This module diagnoses the core failure mode of autonomous systems: knowledge drift. We move beyond bugs to examine how stale facts, unverified sources, and policy misalignment silently corrupt agent reasoning. You'll learn to identify the four operational challenges—Drift, Opacity, Fragmentation, and Latency—and their direct business impact.
We then establish the Agentic Knowledge Engineering (AKE) framework as the solution. You'll understand the fundamental mindset shift: treating knowledge not as an archive but as a control surface and runtime service. We introduce the Knowledge Chain of Custody as the non-negotiable lifecycle for verifiable truth, setting the foundation for all subsequent engineering work.
This module provides the concrete, incremental roadmap for implementation. We introduce the AKE Maturity Ladder, a five-level model that guides teams from chaotic, unstructured data (L0) to a self-healing knowledge mesh (L5). You'll learn to assess your current state, identify the most common stall points, and plot your progression by replacing assumption with evidence at each stage.
We then translate maturity into action with the Field Build Sequence: six executable steps to production. You'll walk through creating a source registry, automating signed ingestion, deploying retrieval contracts that return evidence, implementing continuous validation, integrating an immutable Trust Ledger, and activating drift auto-repair. This is your step-by-step construction guide.
Here, we move from blueprint to build. We cover essential design patterns for resilience, including the Policy-Bound Retrieval Loop and Schema-as-Contract. You'll learn to architect a system where verification is baked into every layer, preventing common failure modes like retrieval bypass and schema drift.
We then deliver the practitioner's toolbox: the 12 Core Practices that act as operational controls, and the complete Implementation Playbook. You get production-ready code templates for ingestion, signing, retrieval, and drift detection, alongside a vetted tool stack (Vault, OpenTelemetry, ledger databases). This module is about executable specifics, not theory.
The final module focuses on the ongoing discipline of operating and measuring your knowledge fabric. We define the Metrics Stack for trust telemetry—shifting from monitoring system uptime to tracking truth uptime. You'll learn to instrument and alert on key signals like provenance coverage, freshness lag, and drift rate, integrating them into your existing observability dashboards.
We solidify your expertise by analyzing common Anti-Patterns and real-world Field Lessons. By studying how knowledge systems fail in production—and the precise engineering fixes—you'll learn to diagnose issues early and reinforce system boundaries. The outcome is the ability to run knowledge as a reliable, auditable, and self-correcting service.