Build Your Own Multi-Agent AI Teams
Train Agent Swarms That Collaborate at Scale—Own Your AI Workforce
One agent is powerful. Multiple agents working together are transformative.
Single agents are limited by what one LLM can do in one conversation. Multi-agent systems break that barrier—distributing complex tasks across specialized agents that collaborate using protocols like MCP and A2A. This masterclass takes you from single-agent foundations to fully orchestrated multi-agent architectures. You'll build the complete infrastructure: agent loops, tool orchestration, inter-agent communication, shared memory, and distributed task execution. By the end, you'll have a working multi-agent system capable of tackling problems no single agent could solve—and the deep understanding to adapt it for research, business automation, or production deployment.
Your Competitive Moat
Proven Transformation Results
Real outcomes from students who completed The LLM Sovereignty Stack™ and built their competitive moats
📈 Career Transformation
💰 Business Impact
What You'll Actually Build
Choose Your Path to Mastery
All modalities include the complete LLM Sovereignty Stack™. Choose based on your learning style and goals.
Self-Paced Mastery
- All 9 steps available immediately
- 50 hours of content + code
- Lifetime access to updates
- Community support
- Monthly live office hours
- 30-day money-back guarantee
9-Week Live Cohort
- Everything in Self-Paced
- Weekly live workshops (2 hrs)
- Direct instructor access
- Cohort accountability & networking
- 24-hour code review turnaround
- 1-on-1 kickoff & graduation calls
- Certificate + alumni network
Founder's Edition
- Everything in Live Cohort
- 6 monthly 1-on-1 coaching calls
- Fractional CTO advisory and implementation support
- Custom learning path for your business
- Same-day code reviews
- Architecture consulting for your product
- Your proprietary model built with you
- Investor pitch coaching
5-Day Immersive Bootcamp
Executive format: Monday-Friday intensive (8am-6pm). Build complete GPT in one week. Limited to 15 participants for maximum attention.
Course Curriculum
12 transformative steps · 45 hours of hands-on content
Module 1: The Strategic Case for Multi-Agent Systems
6 lessons · Shu-Ha-Ri cycle
- Executive Overview: Why Multi-Agent Systems Create Business Value
- Single Agent Limitations: When One Agent Isn't Enough
- Use Cases: Report Generation, Deep Research, Coding Teams, Business Automation
- Agent Specialization: Why Roles Matter
- Protocols for Coordination: MCP, A2A, and the Emerging Standard
- Build vs. Buy: When Custom Multi-Agent Systems Create Advantage
Module 2: Agent Infrastructure Review
5 lessons · Shu-Ha-Ri cycle
- Quick Review: The Agent Processing Loop
- Tool Abstractions: BaseTool, ToolCall, ToolCallResult
- LLM Interfaces: Provider Independence and Abstraction
- The Agent Class: Bringing It All Together
- Prerequisites: What Your Agents Need Before They Can Collaborate
Module 3: The Model Context Protocol (MCP)
6 lessons · Shu-Ha-Ri cycle
- What is MCP? The Standard for Agent-Service Communication
- MCP Architecture: Servers, Clients, and Tool Discovery
- Making Your Agents MCP-Compatible
- Resource Management: Files, Databases, and External APIs
- Implementing MCP Servers for Your Services
- Testing MCP Integration
Module 4: Capstone—Deep Research with MCP
6 lessons · Shu-Ha-Ri cycle
- Designing a Deep Research Agent
- Connecting to Multiple MCP Servers
- Orchestrating Multi-Source Research Workflows
- Synthesizing Information Across Sources
- Generating Structured Research Reports
- Evaluating Research Quality
Module 5: Human-in-the-Loop for Multi-Agent Systems
6 lessons · Shu-Ha-Ri cycle
- Why Multi-Agent Systems Need More Oversight
- Approval Checkpoints: Pausing Distributed Workflows
- Escalation Patterns: When Any Agent Can Request Human Input
- Audit Trails: Tracking Decisions Across Multiple Agents
- Implementing Multi-Agent Human-in-the-Loop
- Balancing Autonomy and Oversight
Module 6: Shared Memory for Agent Collaboration
6 lessons · Shu-Ha-Ri cycle
- Why Agents Need Shared State
- Memory Architectures for Multi-Agent Systems
- Implementing Shared Memory Modules
- Conflict Resolution: When Agents Disagree About State
- Persistence: Saving and Restoring Multi-Agent State
- Memory Patterns for Different Use Cases
Module 7: Capstone—Deep Research Revisited
5 lessons · Shu-Ha-Ri cycle
- Upgrading Your Research Agent with Memory
- Adding Human-in-the-Loop Checkpoints
- Persistent Research Sessions
- Iterative Refinement Based on Human Feedback
- Performance Comparison: Before and After Enhancements
Module 8: The Agent2Agent (A2A) Protocol
6 lessons · Shu-Ha-Ri cycle
- What is A2A? The Standard for Inter-Agent Communication
- A2A Architecture: Agent Cards, Task Distribution, and Responses
- Implementing A2A-Compatible Agents
- Task Delegation: How Agents Assign Work to Each Other
- Response Handling: Processing Results from Other Agents
- Error Handling in Distributed Agent Systems
Module 9: Multi-Agent Orchestration Patterns
6 lessons · Shu-Ha-Ri cycle
- Manager-Worker Pattern: Centralized Task Distribution
- Peer-to-Peer Pattern: Decentralized Collaboration
- Pipeline Pattern: Sequential Agent Processing
- Debate Pattern: Agents Challenging Each Other's Work
- Consensus Pattern: Multiple Agents Agreeing on Results
- Choosing the Right Pattern for Your Use Case
Module 10: Building Multi-Agent Teams
5 lessons · Shu-Ha-Ri cycle
- Designing Agent Roles: Researcher, Writer, Reviewer, Editor
- Specialization vs. Generalization Trade-offs
- Dynamic Role Assignment: Agents That Adapt
- Team Composition: How Many Agents and Which Roles
- Implementing Your First Multi-Agent Team
Module 11: Capstone—Multi-Agent Report Generation
6 lessons · Shu-Ha-Ri cycle
- The Report Generation Use Case: End-to-End Automation
- Designing the Agent Team: Researchers, Analysts, Writers
- Implementing Task Distribution and Coordination
- Quality Control: Review and Revision Cycles
- Generating Professional Reports Automatically
- Evaluation: Measuring Multi-Agent Performance
Module 12: Production Deployment
6 lessons · Shu-Ha-Ri cycle
- Monitoring Multi-Agent Systems: What to Track
- Cost Optimization: Managing LLM Calls Across Agents
- Failure Handling: What Happens When Agents Fail
- Scaling Multi-Agent Workloads
- Security Considerations for Distributed Agents
- Capstone: Your Production-Ready Multi-Agent System
Production-Grade Tech Stack
Master the same tools used by OpenAI, Anthropic, and Google to build frontier AI systems
Frequently Asked Questions
Agentic Systems teaches you to build a single agent from scratch. This course assumes you have that foundation and focuses on multi-agent coordination: A2A protocol, shared memory, distributed task execution, and orchestration patterns.
Recommended but not required. We include a quick review of agent fundamentals, but you'll get more value if you've built a single agent first.
No. You'll build everything from scratch—agent communication, task distribution, shared memory, and orchestration. This gives you deeper understanding and complete control.
Everything runs on a standard laptop. Multi-agent systems don't require more hardware—just more LLM calls, which we optimize throughout the course.
A complete multi-agent system with A2A communication, shared memory, human-in-the-loop patterns, and MCP integration—capable of automated research, report generation, and complex task execution.
Complex business processes often require multiple specialized roles working together. Multi-agent systems can automate these workflows—research teams, review processes, content pipelines—at a fraction of the cost of human teams.
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Command $250K-$400K salaries or save $100K-$500K in annual API costs. Own your model weights. Build defensible technology moats. Become irreplaceable.
Self-paced · Lifetime access · 30-day guarantee
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