What you'll learn in Designing Multi-Agent AI Systems
What you'll leave with
- 1A fully functioning multi-agent AI system you built yourself
- 2Agent design patterns for reliable coordination
- 3Certificate of completion (4.5 stars, 4 reviews)
- 4Code templates and design frameworks
Course Curriculum
Week 1Multi-Agent Foundations
Multi-Agent Foundations
- What is an agent and what is a multi-agent system (5 modules)
- Single-agent vs multi-agent workflows (5 modules)
- Real-world examples of multi-agent AI (5 modules)
- Core coordination patterns and communication methods (5 modules)
- Common challenges in multi-agent systems (5 modules)
- Hands-On / Outcome + Assignment + Resources
Week 2Agent Architecture Design
Agent Architecture Design
- Defining agent roles: executor, planner, reviewer, supervisor (6 modules)
- Delegation and handoffs between agents (5 modules)
- Preventing coordination breakdowns (5 modules)
- Handling context and short-term memory (5 modules)
- Safe planning and basic decision-making logic (5 modules)
- Hands-On / Outcome + Assignment + Resources
Week 3Tool Integration & Memory
Tool Integration & Memory
- Integrating external tools safely
- Long-term memory and state management
- Multi-step reasoning patterns
- Testing agent reliability
Week 4Production Deployment
Production Deployment
- Evaluate and debug agent behavior
- Scale multi-agent systems safely
- Capstone: Build your multi-agent system
- Final showcase and certification
Who Designing Multi-Agent AI Systems is designed for
Prerequisites
Your Instructors
Aki Wijesundara, PhD
AI Founder | Educator | Google AI Accelerator Alum
Aki Wijesundara is an AI leader with a PhD in Machine Learning and extensive experience mentoring startups at Google's AI Accelerator. With a career spanning both research and applied AI, Aki has taught 5,000+ students worldwide how to design and deploy production-ready AI systems. He has worked across cutting-edge areas of applied AI, from LangChain and RAG pipelines to observability and large-scale deployment. As a researcher and educator, Aki bridges the gap between theory and practice, making complex systems approachable and actionable for engineers, founders, and product leaders.
Manu Jayawardana
Exited AI Founder | Co-Founder, Snapdrum | Co-Founder & CEO, Krybe
Manu Jayawardana is a serial entrepreneur with multiple AI startup successes. He exited Rise AI, a fintech app with over 35,000 users, to a private investor. He co-founded Snapdrum, delivering enterprise AI systems and scaling paid acquisition engines that drove 5,000+ premium customers. He is also Co-Founder & CEO of Krybe, a London-based Voice AI startup serving 1,000+ users and part of the NVIDIA Inception ecosystem. With a background in quant finance and AI engineering, he operates at the intersection of AI, distribution, and execution.
What Students Say
“The AI training approach is outstanding. Our team learned to build practical AI solutions that we could implement immediately in our educational platform. The hands-on methodology made complex AI concepts accessible to our entire development team.”
Kavi T.
CEO of Tilli Kids / Stanford PhD
“I sent my team through this training for upskilling, and the results have been remarkable. Within weeks, they became much more efficient at building automations and deploying AI agents at work. This program bridges the gap between theory and practice and it's had a real impact on our productivity.”
Aamir Faaiz
CEO of Bayseian
Frequently Asked Questions about Designing Multi-Agent AI Systems
What will I learn in Designing Multi-Agent AI Systems?
Understand what agents are and how they work together in multi-agent systems. Define agent roles: executor, planner, reviewer, supervisor. Manage context, memory, and interactions so agents reason reliably. Integrate tools safely and make agents perform real actions. Detect and fix coordination breakdowns before they affect the whole system. Build and ship a fully functioning multi-agent AI system
Who is Designing Multi-Agent AI Systems designed for?
Engineers building AI features or platforms. Product managers owning AI initiatives. Founders designing AI-powered products
What are the prerequisites for Designing Multi-Agent AI Systems?
Basic familiarity with AI concepts and tools. No advanced coding required
How long does Designing Multi-Agent AI Systems take?
4 weeks. Format: Cohort-based.
What will I leave with after completing Designing Multi-Agent AI Systems?
A fully functioning multi-agent AI system you built yourself. Agent design patterns for reliable coordination. Certificate of completion (4.5 stars, 4 reviews). Code templates and design frameworks
Is Designing Multi-Agent AI Systems available online?
Yes, Designing Multi-Agent AI Systems is delivered entirely online as a cohort-based.
Who teaches Designing Multi-Agent AI Systems?
Aki Wijesundara, PhD — AI Founder | Educator | Google AI Accelerator Alum. Manu Jayawardana — Exited AI Founder | Co-Founder, Snapdrum | Co-Founder & CEO, Krybe
How do I enroll in Designing Multi-Agent AI Systems?
You can enroll via Maven at https://maven.com/theaiinternship/designing-multi-agent-ai-systems. Click the "Enroll on Maven" button on this page.
Topics covered
Corporate Training & Team Upskilling
Train your entire team on Designing Multi-Agent AI Systems. We offer corporate group training, custom cohorts, and enterprise licensing. Trusted by teams at Google, Meta, Oracle, and more.