Learn to design, build, and scale production-grade agentic AI systems for enterprise environments.
A 24-course professional curriculum for engineers, architects, and leaders, organized as a progressive learning journey across the full agentic AI system lifecycle.
Courses are available to both AEI members and non-members.
AEI members receive exclusive discounts, access to AEBOP standards, and participation in a global professional Agentic Engineering community.
Enterprise agentic AI training designed for real-world deployment
Most AI training helps teams experiment.
AEI courses are designed to help organizations move from experimentation to dependable, scalable agentic AI systems—with the rigor required for enterprise deployment.
Lower AI risk. Faster production. Confident scale.
24 courses organized as a progressive learning journey. Each course stands alone, yet compounds when taken together.
A fast-track introduction to the discipline, core terminology, cross-industry patterns, and the full practice map of Agentic Engineering.
Required for all AEI certifications.
Build the essential technical and governance foundations required for any production-grade agentic system.
Execution model, behavior controls, runtime policies, and safety.
Security-by-design, identity, threat modeling, and agent-level defense.
Telemetry, reasoning logs, ROC dashboards, and cognitive observability.
Structured, enforceable communication patterns for agent-system interaction.
Governance frameworks, guardrails, controls, and compliance.
Deep dive into the components that enable robust reasoning, adaptation, and cognitive performance.
Knowledge structures, grounding, and domain modeling.
Adaptive context pipelines for high-fidelity agent performance.
Short-term, long-term, and episodic memory architectures.
Chain-of-thought, trees, planning, verification, and reasoning controls.
Model selection, tuning, and integration for agentic cognition.
Graph-based orchestration, multi-agent coordination, workflow automation.
Architect full-scale agentic systems ready for enterprise and regulatory environments.
Blueprinting full agentic systems with reliability and scalability.
Human–agent interaction, cognitive UX design, and trust signaling.
APIs, connectors, and enterprise integration patterns.
Cognition loops, meta-cognition, self-evaluation, and self-correction.
Explainability, certainty scoring, alignment strategies, and trust fabrics.
Operational discipline for deploying, monitoring, and scaling agents.
Deployment, monitoring, incident response, and operational reliability.
Testing, benchmarking, and behavioral validation for cognitive systems.
A complete SDLC tailored for agentic systems—from ideation to deprecation.
For senior leaders, PMs, and executives driving AI-native transformation.
Product strategy, roadmaps, and value realization for agentic systems.
Roles, org models, and capability structures for AI-native teams.
The L0–L5 maturity journey and how to build an agentic operating system.
Enterprise-wide transformation models for scaling agentic systems.
Testimonials
“After graduating, I spent six months applying for AI roles without traction. AEI’s courses helped me move beyond theory and understand how agentic systems operate in production. I secured an AI Agent Developer role shortly after and finally felt job ready.”
“We were close to missing a critical delivery milestone. AEI’s training gave me the system-level patterns and validation approaches to stabilize our agentic pipeline. We met the deadline and avoided a rollback that would have put my role at risk.”
“I had built many AI components, but AEI helped me design agentic systems end-to-end. The architectural clarity changed how I review designs and guide teams. It’s the first training that matched the complexity I deal with daily.”
“Operating agentic AI in production was chaotic before AEI. The courses gave me concrete patterns for monitoring, validation, and failure handling. Incidents dropped, and on-call stopped feeling like guesswork.”
“AEI helped me shift from building features to owning systems. I was promoted to AI Tech Lead and now use AEI frameworks to align engineers and architects. I’m pursuing AEI certifications to formalize what we’ve put into practice.”
“What stood out was the focus on lifecycle and control. AEI gave us a shared language across engineering, architecture, and leadership. That alignment accelerated decisions and reduced friction across teams.”