Why We Built Polpo: The Runtime for AI Agents

Why We Built Polpo We kept solving the same infrastructure problems every time we shipped an agent. Streaming, sandboxing, memory, tool execution, evaluation — the same backend plumbing, over and over. So we built a runtime that handles all of it. This post explains the gap we saw, why existing tools didn't fill it, and what Polpo does about it. Agents got good fast. Infrastructure didn't keep up. A year ago, AI agents could barely handle a multi-turn conversation. Today, they write code, research topics, manage files, ask clarifying questions, spawn sub-agents, and orchestrate complex workflows. The capabilities evolved at breakneck speed. The infrastructure to run them? Not so much. Building a production-ready agent means stitching together a surprising amount of backend plumbing — streaming, tool execution, sandboxed file access, persistent memory, session management, scheduling. Every team building agents hits the same wall: the agent works on your laptop. Now what? The demo-to-pro