From the course: Agentic AI and Autonomous Development

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PMAT subagent architecture deep dive

PMAT subagent architecture deep dive

From the course: Agentic AI and Autonomous Development

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PMAT subagent architecture deep dive

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- [Instructor] One issue with non-deterministic AI agents is that they can corrupt state, they can crash in production, and this is gonna cause things like infinite retry loops. And if you look at distributed computing in general, it's one of the most difficult things to get correct. PMAT solves this with Russ Actor Model. In this case, a mailbox isolation prevents race conditions and supervision trees auto recover from crashes. And then, event sourcing enables time travel debugging. So if you look at some of the things that happen here, this allows you to work with Claude Code, and build sub-agents that neatly enforce quality with zero self-admitted technical debt, low complexity, looking at graph metrics, et cetera. And this is an architecture that many agents will start adopting. So if we take a look at this diagram here, we can see that at the very beginning we have things like actor isolation. There's four specialized…

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