You’ve felt the friction. You’re building an autonomous AI agent. It needs to think, act, learn, and remember. But the moment you try to snapshot its state to save its progress, the entire virtual machine freezes. Your agent’s “train of thought” hits a wall, and latency spikes.
We’ve been forcing a square peg into a round hole. For years, the default sandbox has been Firecracker. It’s a brilliant piece of engineering, but it was built for a very specific purpose: serverless compute. Serverless functions are ephemeral and stateless. AI agents are the exact opposite. They are stateful, iterative, and constantly evolving.
Trying to run a stateful AI agent on a serverless hypervisor is like trying to live out of a hotel room. It works for a weekend, but it falls apart when you need a workshop.
The problem isn’t that we need a slightly faster version of what we have. The problem is architectural. You cannot incrementally patch a tool designed for stateless compute to handle the relentless snapshotting and fast resume cycles that reinforcement learning and agent workloads demand. You have to rethink the trade-offs from the ground up.
That’s exactly what Tarit does. Built on rust-vmm as a direct replacement for Firecracker, Tarit strips away the serverless assumptions and introduces the one primitive AI infrastructure actually needs: live snapshots without pausing VM operations.
The numbers back it up. On bare metal, Tarit hits a 35ms p99 VM acquisition time from a warm pool. You can snapshot a small sandbox and resume it in roughly 80ms. It is currently one of the fastest VM-based sandboxes available, and it achieves this not by cutting corners, but by aligning the architecture with the actual needs of agentic computing.
The future of AI isn’t just about smarter models; it’s about infrastructure that doesn’t freeze when an agent tries to remember what it just did.
If you are deploying, scaling, or building AI agents that require secure, isolated environments, stop wrestling with Fargate or bending Firecracker to your will. Tarit includes a basic orchestrator for cluster placement, HA, and networking. You can hand the docs to your coding agent and spin up a multi-node cluster on nested-virt machines today.
We are entering the era of autonomous systems. They need an operating system built for their reality, not the reality of a 2019 AWS Lambda function. Build on infrastructure that actually understands what your agents are doing.
FAQ
Q: Isn't Firecracker already fast enough for most workloads?
A: Fast is relative. Firecracker is fast for spinning up and tearing down stateless functions. But if your AI agent needs to snapshot its state every few seconds to save progress, Firecracker's lack of live snapshotting forces a pause. That pause kills the latency and momentum required for real-time agentic compute.
Q: What does this mean for developers building AI tools today?
A: It means you can finally stop building hacky workarounds to persist agent state. You can deploy a hypervisor that natively supports the iterative, state-heavy nature of AI and RL workloads, allowing you to focus on agent logic rather than infrastructure plumbing.
Q: Is it really necessary to replace existing infrastructure just for AI agents?
A: Yes. The shift from stateless web APIs to stateful, autonomous agents is a paradigm shift in compute. Attempting to run the next generation of autonomous systems on infrastructure optimized for 2019 serverless architectures is a fundamental mismatch that will bottleneck your scale.