This 3-hour hands-on course equips engineers to design, implement, and operate governed AI systems in production. Learn disciplined model selection, schema-based contracts, telemetry, and cost-performance engineering to turn experimental models into auditable infrastructure using the AME framework and enterprise patterns.
AEI members receive 20% off with code MEM_C11_20.
This module exposes the predictable failure patterns that plague production AI systems: cost runaways, silent hallucinations, and unobserved model planes. You'll learn to diagnose why prototype success doesn't translate to production reliability and how undisciplined model use creates systemic risk.
We introduce the Agentic Model Engineering (AME) Maturity Ladder—a field-tested framework for assessing your current state (L0-L4). You'll identify where your system sits, recognize field signals of immaturity, and establish a target state for governed, reliable cognition that withstands enterprise pressure.
Design begins with intentional model selection. You'll master the enterprise model palette, learning to choose between reasoning and utility models, large and small architectures, and specialized adapters. We cover the global landscape including DeepSeek, Qwen, and Ernie, focusing on fit-for-purpose selection, not hype.
Then you'll architect cognitive roles (Planner, Executor, Critic, Validator) with enforceable schema contracts using Pydantic/JSONSchema. Learn to isolate risk through role boundaries, define measurable confidence thresholds, and design resilient model meshes that prevent cascading failures in production environments.
Implementation turns design into running code. We walk through the six-step field pattern for model integration: from task intent definition to Trust Ledger registration. You'll implement telemetry instrumentation with OpenTelemetry, enforce schema validation, and configure multi-tier fallback chains that include human escalation.
Next, you'll engineer adaptive routing systems with confidence gating and circuit breakers. Master cost-performance engineering by tracking cost-per-successful-decision (not tokens) and implementing policy-aware routing that automatically adjusts for latency SLOs, budget limits, and data sensitivity requirements.
Operational discipline separates experimental systems from infrastructure. You'll establish governance workflows: model promotion gates with two-person review, weekly chaos testing, and biweekly cost-performance reviews. Learn to implement the Metrics Ladder for automated drift detection and correction.
Finally, internalize lessons from enterprise failures and successes. Analyze anti-patterns like uncalibrated confidence and untested fallbacks through real case studies. Establish continuous improvement loops that measure, diagnose, and correct model performance, transforming reactive firefighting into proactive reliability engineering.