Agentic AI: Actor Models and Subagent Architecture
About this course
Learn to build production agentic AI systems using actor model foundations, subagent architecture patterns, and multi-language implementations. You will explore the actor paradigm for concurrent computation, where isolated processes communicate through message-passing with zero shared memory, eliminating race conditions and deadlocks that crash production systems. The course covers Actix supervision trees in Rust for fault-tolerant actor recovery and location transparency for seamless distributed scaling. You will implement Claude subagent patterns for task-specific AI configurations with isolated state and tool access, and examine pmat subagent architecture for code quality analysis through specialized delegation pipelines. The subagent module demonstrates supervised multi-agent coordination, applies Amdahl's law to understand parallelization limits of subagent systems, and explains why simple agents often outperform complex multi-agent designs. You will also explore small language models as efficient alternatives for agent reasoning tasks. The hands-on module covers actor implementations in three languages: Deno with TypeScript, Go with goroutines and channels, and Rust with ownership-based memory safety. You will build Go supervisor patterns for automatic actor recovery and examine a complete agentic coding project repository. By completing this course, you will be able to design fault-tolerant agentic systems using actor model principles, implement subagent architectures with Claude, and build actor patterns across multiple programming languages.
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What you'll learn
- Build production agentic AI systems using actor model foundations
- Implement Claude subagent patterns with task-specific configurations and isolated state
- Design fault-tolerant actor systems using Actix supervision trees in Rust
- Create actor implementations in TypeScript (Deno), Go (goroutines and channels), and Rust
- Apply Amdahl's law to understand parallelization limits in subagent systems
- Build Go supervisor patterns for automatic actor recovery
- Implement message-passing architectures with zero shared memory to eliminate race conditions
- Design subagent delegation pipelines for specialized task coordination
Course objectives
- Design fault-tolerant agentic systems using actor model principles
- Implement subagent architectures with Claude for task-specific AI configurations
- Build actor patterns across multiple programming languages
- Create supervised multi-agent coordination systems
- Understand when to choose simple agents over complex multi-agent designs
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