Why Agent Runtimes Need Their Own Infrastructure

Most AI infrastructure today is built entirely around the basic idea that an LLM safely generates a response and something else acts on it, which works perfectly fine for simple chatbots and generic content generation. However, that entire paradigm completely falls apart the exact moment you need an agent to actually do real things in the world, like actively calling APIs, writing files, spinning up processes, or moving actual money. The glaring gap is not in the model’s fundamental reasoning ability, the massive gap is strictly in execution.

The execution problem

When an autonomous agent actually interacts with the real world you immediately run into severe questions that have absolutely nothing to do with language modeling. You urgently need deep isolation because if the agent’s code has a massive bug or gets stuck in a loop you need serious sandboxing and not just polite prompt engineering. You need strict ephemerality where each individual execution is a completely fresh environment, ensuring no leaking state between runs and absolutely no accumulated garbage slowing things down. You must have perfect observability because you desperately need to know exactly what the agent did, what specific tools it actively called, what horribly failed, and exactly why it failed. Finally, you must strictly control resources like CPU, memory, network, and disk, because powerful agents absolutely cannot just run wild across your systems.

These are incredibly difficult infrastructure problems and not AI problems at all, and they are exactly the reason I have been spending most of my waking hours aggressively building on top of Firecracker microVMs.

Why microVMs

Standard containers are simply too fat for this and full traditional VMs are painfully slow to spin up, making microVMs hit the exact sweet spot with sub second boot times, incredibly strong isolation using actual hardware virtualization rather than just namespace separation, and allowing you to tightly control the entire resource envelope. Each individual agent execution gets its own dedicated VM that cleanly boots, safely runs, and immediately dies, meaning absolutely nothing persists unless you explicitly tell the system to persist it, and that is the only secure model.

The orchestration layer

But completely isolated execution is only half the problem, because the other incredibly painful half is orchestration, which involves coordinating multiple complex agents, actively managing tool calls, gracefully handling retries, and securely maintaining durable state across very long running workflows. This is exactly where highly durable orchestration matters, because you desperately need explicit state, incredibly clear transitions, retries that do not completely corrupt the world, and a reliable way to fully recover when a long running task unexpectedly dies halfway through. The specific tool name absolutely matters less than the overall shape of the problem, because autonomous agents are not just simple chat messages, they are highly complex processes with memory, massive side effects, and catastrophic failure modes.

What this means for building products

If you are actually building serious AI products that do substantially more than just generate text, you desperately need to think about deep execution infrastructure from day one instead of treating it as a lazy afterthought or arrogantly claiming you will just add sandboxing later. The only agents that actually matter in the real world are the ones that take real action, and taking action safely requires complex infrastructure specifically designed for that exact brutal purpose. This is the exact stuff I am actively building in my current work, and if you are wrestling with these exact same painful problems I would absolutely love to compare notes.