Agentic AI: Actor Models and Subagent Architecture

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.