Buildfunctions™
Serverless for AI agents
Status:Systems goodA simple solution to serverless computing:
The Agentic Mismatch
Traditional serverless was built for trusted web requests. AI agents demand something fundamentally different.
Traditional serverless products are excellent and efficient mechanisms for trusted web requests; however, AI agents push far beyond what they were designed for, introducing autonomous decision making, dynamic execution of untrusted actions, and long-running stateful systems that create performance, cost, and security challenges that traditional serverless was never built to handle.
As a result, these “legacy” serverless products become dangerous vectors when handling untrusted AI actions, such as running AI-generated code or executing reward functions.
Rigid Runtimes
Imagine an agent decides mid-task that it needs a new library, needs to run longer than expected to complete analysis, or tries to pass a file to the next agent task, only to fail due to rigid runtimes, hard timeouts, or ephemeral filesystems.
Runaway Costs & Resource Starvation
Or worse, imagine an agent hits a bad loop: it starves other workloads through shared compute and triggers autoscaling that turns a single mistake into a massive cloud bill within minutes.
The Multi-Provider Stack Problem
Beyond these failure points, popular products also lack the fundamentals required to self-host AI agents, including automatic model mounting, full GPU access inside sandboxes, and a simple, unified setup that doesn’t require stitching together multiple services.
In fact, many setups today require customers to manage a multi-provider stack to support a single agent workflow, combining one platform for hosting a top-level application, another for sandboxed execution, and a third for model inference. Each service comes with its own contracts, billing cycles, and developer workflows, creating significant operational and financial friction.
Built for Production AI
Everything you need to run AI agents reliably at scale