NEWS · CODING · #2405
Actors for Agents — actor-model patterns for production AI (QCon AI)
At QCon AI, Manju Rajashekhar (founder & CEO of Mad Labs) presented “Actors for Agents,” arguing that the actor model maps naturally to the runtime needs of production AI agents. He described patterns — state ownership, message passing, write-through state, and supervision boundaries — to manage failures, continuations, and mutable execution graphs so agentic demos can become resilient production systems.
KEY POINTS
- At QCon AI, Manju Rajashekhar (founder & CEO of Mad Labs) presented “Actors for Agents,” arguing that the actor model maps naturally to the runtime needs of production AI agents.
- He described patterns — state ownership, message passing, write-through state, and supervision boundaries — to manage failures, continuations, and mutable execution graphs so agentic demos can become resilient production systems.
- The talk matters because it gives practical architectural patterns for making long‑running, stateful, distributed AI agents robust in production rather than fragile demos.
WHY IT MATTERS
The talk matters because it gives practical architectural patterns for making long‑running, stateful, distributed AI agents robust in production rather than fragile demos.