Engineer bounded, observable, and recoverable reasoning loops for production agentic systems. Move beyond reactive AI to governed cognition using PDAR architectures, supervisor patterns, and traceable execution. This 3-hour practitioner course includes hands-on labs to design, test, and recover reasoning under failure. Based on AEBOP T2.4 standards.
AEI members receive 20% off with code MEM_C10_20.
Production AI fails silently when reasoning is implicit. This module teaches you to identify runaway cognition, silent drift, and trust erosion—the most common sources of agent failure in enterprise environments. You’ll learn to diagnose gaps in reasoning control using real incident patterns.
Through the Reasoning Maturity Ladder, you’ll assess your system’s current level and define measurable signals for improvement. This isn’t about intelligence—it’s about safety. By the end, you’ll translate theoretical risks into actionable engineering requirements: bounded, measurable, and observable reasoning loops.
Move from diagnosis to design with proven reasoning architectures. We cover the core PDAR loop (Plan-Decide-Act-Reflect) and its essential variants: governance envelopes, validation patterns, reflection integration, and bounded execution. Each pattern includes guardrails, rollback logic, and verification signals.
You’ll learn to select and combine patterns based on use case and risk level. This module provides a complete library of reasoning design patterns drawn from finance, healthcare, and industrial deployments—enabling you to architect systems that fail safely, recover predictably, and remain auditable under pressure.
Implementation separates theory from production reality. Here you’ll work with production-ready Python templates for bounded PDAR loops, supervisor-executor contracts, and trace emission. Every line of code is built with guardrails, validation, and observability integrated from the start.
You’ll build a complete deployment pipeline including CI/CD integration for reasoning replay tests, drift detection, and cost-aware throttling. The module culminates in a field rollout checklist that ensures your reasoning system meets enterprise requirements before first deployment—transforming design patterns into running, governed cognition.
Reasoning systems require operational discipline, not just deployment. This module teaches weekly cognitive ops rituals, reasoning standards maintenance, and post-incident analysis specific to reasoning failures. You’ll learn to manage versioning, upgrades, and continuous improvement through structured reflection.
Through cross-industry case studies and anti-pattern analysis, you’ll internalize how high-performing teams sustain reasoning reliability. The module closes with a pre-release safety scan and the core field insight: autonomy without visibility isn’t intelligence—it’s luck. You’ll leave ready to operate reasoning systems that are safe at scale.