Presentation: Actors for Agents: Patterns for Production AI
Manju Rajashekhar explains how the actor model solves the complex runtime challenges of production AI agents. By treating agents as distributed systems, he discusses state ownership, message passing, write-through state, and supervision boundaries to manage failures, continuations, and mutable execution graphs, moving agentic workflows beyond fragile demos to resilient architecture. By Manju…
Manju Rajashekhar, Founder & CEO at Mad Labs, discusses the challenges of scaling AI agents in production environments. He explains how treating agents as distributed systems and applying the actor model can address runtime issues such as state ownership, message passing, write-through state, and supervision boundaries. Rajashekhar emphasizes that the actor model is not an outdated concurrency idea but a practical tool for building resilient agentic workflows.
He introduces the concept of actors for agents, highlighting how these systems can better handle production failures, continuations, and mutable execution graphs. By moving agentic workflows beyond fragile demos, the actor model offers a more robust architecture for real-world AI applications.
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