Right now, the architecture looks like this: give an agent a sandbox, a prompt, access to some files and tools, and let it run. Constrained enough that it can't break anything critical. Powerful enough to do real work.

That's where we are. And models are getting smarter fast.

Watch the full episode of Samar Abbas on the Lightspeed Ventures podcast: https://www.youtube.com/watch?v=w4spcXq5uCw
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Temporal's platform is a central hub for developers interested in distributed systems, workflow orchestration, and fault-tolerant computing. Through articles, tutorials, and documentation, Temporal offers insights into building resilient and scalable distributed applications. Developers can learn about workflow automation, state management, and handling failure scenarios using Temporal's workflow engine and coordination APIs.

Temporal

AI agents are evolving from synchronous interactions to long-running autonomous workflows lasting hours, days, or even months. Drawing an analogy to the MS-DOS era of computing, the current architectural pattern emerging for AI agents involves sandboxed environments where agents have controlled access to file systems, data, and tools like bash scripts, while being prevented from taking destructive actions on enterprise systems. This sandboxing approach reflects the early, constrained-but-functional stage of agentic AI development.

We're in the MS-DOS era of AI agents