Designing Multi-Agent AI Systems

4.5 (4)

Dr. Aki Wijesundara

AI Founder | Google AI Accelerator Alum

Manu Jayawardana

Exited AI Founder | Founder, TAI Labs

Build multi-agent AI systems that actually work, not just demo

Many aspiring AI builders get stuck at the start. They know what multi-agent AI can do but struggle to turn ideas into systems that actually work in production. Without a clear design approach, agents fail, coordination breaks down, costs blow up, and hallucinations derail results.

This course guides you from zero to production-ready multi-agent AI thinking. Over the course, you will learn to:

Design agent roles and responsibilities from scratch even with no prior experience
Manage context, memory, and coordination so agents reason reliably
Plan, execute, and integrate tools safely within agent workflows
Evaluate, debug, and improve agent behaviour iteratively

By the end, you will not just understand the theory. You will build functioning multi-agent systems, know how agents coordinate and solve real problems, and gain the confidence to ship reliable AI products, setting the foundation for advanced AI system design and production deployment.

A limited number of need-based scholarships are available for applicants facing real financial hardship. See if you qualify.

What you’ll learn

Build, manage, and scale multi-agent AI systems from scratch, even with no prior AI experience.

Learn what agents are and how they work together
Understand how multi-agent systems differ from single-agent workflows
Get a clear mental model of agent roles, responsibilities, and coordination

Define agent roles and tasks from the ground up
Learn how to manage memory, context, and interactions so agents reason reliably
Integrate tools safely and make agents perform real actions

Detect and fix problems early before they affect the whole system
Iterate on your system to make it reliable and scalable
Walk away with a fully functioning multi-agent AI system you built yourself

Learn directly from Aki & Manu

Dr. Aki Wijesundara

Contact

AI Founder | Educator | Google AI Accelerator Alum

Manu Jayawardana

Contact

AI Advisor | Founder, TAI Labs

Previous Students from

See all products from TAI Labs

Who this course is for

Engineers building AI features or platforms
Product managers owning AI initiatives
Founders designing AI-powered products

What's included

Live sessions

Learn directly from Dr. Aki Wijesundara & Manu Jayawardana in a real-time, interactive format.

Project-based learning

Work on practical exercises that simulate real-world data workflows from ingestion to analytics-ready tables.

Downloadable resources

Access code templates, cheat sheets, and pipeline examples to practice and reuse after the course.

Community of learners

Collaborate, ask questions, and share insights with peers in a supportive learning environment.

Certificate of completion

Showcase your data engineering skills to employers, clients, or your team.

Guided workflow playbooks

Step-by-step guides for building reliable pipelines using best practices and beginner-friendly tools.

Maven Guarantee

Your purchase is backed by the Maven Guarantee.

Course syllabus

Week 1

Jul 23—Jul 26

What is an agent and what is a multi-agent system

5 items

Single-agent vs multi-agent workflows

5 items

Real-world examples of multi-agent AI

5 items

Core coordination patterns and communication methods

5 items

Common challenges in multi-agent systems

5 items

Hands-On / Outcome:

3 items

Assignment To Do

1 item

Resources

5 items

Useful Interviews

1 item

Articles

6 items

Week 2

Jul 27—Aug 2

Defining agent roles: executor, planner, reviewer, supervisor

6 items

Delegation and handoffs between agents

5 items

Preventing coordination breakdowns

5 items

Handling context and short-term memory

5 items

Safe planning and basic decision-making logic

5 items

Hands-On / Outcome:

3 items

Assignment To Do

1 item

Resources

6 items

Useful Interviews

1 item

Articles

7 items

Schedule

Live sessions

2 hrs / week

You will learn core concepts into multi-agents

Hands On Projects

4 hrs / week

Complete practical exercises and mini-projects that simulate real-world multi-agent AI workflows. Apply what you learn in live sessions to design agents, coordinate tasks, integrate tools, and manage memory, ensuring you gain hands-on experience that prepares you to build production-ready multi-agent systems.

Testimonials

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
Not only are the instructors experts in their field, they're incredibly skilled at breaking down complicated AI concepts so students can grasp them quickly. Anyone interested in building foundational AI knowledge should take this training - it's worth the investment.
Dr. Elizabeth Creighton
Founder & Principal at Brazen
The instructors help break down AI model development and clearly have plenty of experience to help others learn about complex concepts like infrastructure setup. The practical approach to NLP and LLM applications was exactly what our team needed.
Alissa Valentine
NLP & LLM Real World Data Scientist
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