Originally published on tamiz.pro.
In 2021, a team running a Go-based infrastructure service hit 1 million concurrent goroutines under load. The lessons they pulled from that scale—structured concurrency, cancellation propagation, resource budgeting, observable failure—landed differently this time around. Today, the same patterns are surfacing as engineers build production AI agent systems, except instead of goroutines racing on an event loop, we're managing LLM calls, tool executions, and streaming responses across distributed services.
The parallel isn't coincidental. Both domains share a core tension: unbounded fan-out looks elegant in code and catastrophic in production. Understanding how the Go community solved this at massive scale gives AI engineers a head start on the problems that are now hitting agent platforms.
1. The Fan-Out Problem: When Parallelism Becomes Chaos
Goroutine Leak at Scale
