Foundations
Learn the foundations.
Start with the core ideas behind autonomous systems, agent behavior, and architectural patterns before you scale up complexity.
Start with the intro articleAgentic Architect Lab by MLacademy
Build the judgment behind production AI agents.
Practice architecture decisions, understand agent failures, and learn to build secure, reliable systems.
Challenge preview
A purchase order needs manager approval before an agent can finish the workflow. What should control submission?
Review the scenario.
Choose the strongest design.
Reveal the explanation after you submit.
Featured challenge
Where should approval actually be enforced?
Try this free architecture challenge. No sign-up required.
Scenario
A purchase order needs manager approval before an agent can finish the workflow. What should control submission?
A procurement agent has prepared a $12,400 purchase order. Company policy requires manager approval for orders over $10,000. The agent can call an ERP submission tool, and the order may be edited before submission if pricing or quantity changes.
Which design best ensures the order cannot be submitted without valid approval under those constraints?
Learning paths
Choose a practical way in.
Start from the foundations, practice architecture choices, or go deeper into build-and-debug workflows.
Architecture practice
Practice architecture decisions.
Compare realistic tradeoffs across approvals, retries, tool usage, and shared-state scenarios in the practice collection.
Explore practice challengesBuild and debug
Build and debug agents.
Study public course materials and class archives to connect architecture decisions with implementation patterns and failure modes.
Browse the class archiveFeatured resources
Public materials you can use right now.
Explore articles, course materials, and published class materials to put these ideas into practice.
ArticleFoundational
Getting Started with Agentic Design
A substantive article introducing autonomy, goal-oriented behavior, and multi-agent architecture patterns.
Outcome: Understand the core vocabulary and system traits used throughout the lab.
Read the articleCourse overviewIntermediate
Agentic Design Patterns Course Overview
A guided overview of the primary course page, with learning goals, structure, and links to public supporting materials.
Outcome: See how architecture topics progress from fundamentals to capstone work.
Open the overviewPDFFoundational to intermediate
Course Introduction PDF
A downloadable introduction and syllabus resource for the agentic design course materials.
Outcome: Review the course framing, structure, and prerequisites at your own pace.
Open the PDFArchiveMixed
Past Class Archive
A public archive page for completed classes, slides, notebooks, homework, and other materials already published on the site.
Outcome: Find reusable class material from completed sessions in one place.
Browse the archive
Instructor
Learn with Venkatesh Tadinada.
Agentic Architect Lab draws from MLacademy's existing instruction and public materials to help developers and cloud architects build better judgment around real system decisions.
- Strategic AI consultant with a long track record in data and enterprise systems.
- Teaches through practical scenarios focused on design, debugging, security, and deployment.
- Shares course materials and class archives that connect architecture ideas to implementation choices.
Guided learning
Keep practicing beyond the homepage.
Browse the public course catalog and past class archive to continue studying agentic design concepts. Some courses are available now; others are listed as planned offerings rather than active enrollments.
FAQ
Who is this for?
Developers, cloud architects, and certification candidates who want to make better design decisions around AI agents and the systems that support them.
What should I know before I start?
A working knowledge of programming and software architecture is helpful. The public materials on this homepage are designed to be approachable even if you are still building experience with agentic systems.
What is free today?
The sample challenge and the selected public resources featured on this homepage are free to explore right now.
How does the learning approach work?
The lab combines scenario-based architecture practice with public course materials and class archives so you can connect abstract guidance to practical implementation choices.
Next step
Start with one architecture decision, then follow the path that fits your background.
The sample challenge and selected public resources are free. Use them to practice now, then continue into guided course materials and past classes when you're ready.