Run AI-Generated Code Safely: VM-Isolated Sandboxes for Agents & LLM Workflows

The World's Codebase Is Shifting, and Infrastructure Hasn't Caught Up

Estimates suggest AI generated around 41% of new code in 2024—roughly 256 billion lines—while the AI agents market is projected to grow from around $5 billion in 2024 to $50 billion by 2030 at a 45%+ CAGR.A majority of enterprises are now experimenting with AI agents, with many anticipating significant autonomy gains in the coming years.

But today’s infrastructure wasn’t designed for executing untrusted, runtime-generated code. When things go wrong—and they do—companies face security risks and compliance headaches. Access control gaps remain a top driver of AI-related incidents.

AI coding assistants were phase one. The real shift is toward iterative, non-deterministic systems that generate and execute code in real time. By 2028, a substantial portion of enterprise software is expected to incorporate agentic capabilities.

This mismatch between agent behavior and existing infrastructure is what we call the Agent Execution Gap.

Akira Labs is building the infrastructure layer that closes it.

Why Today’s Infrastructure Fails

Containers Share Too Much

Containers share the host kernel. For reviewed applications this is fine, for runtime-generated, untrusted AI code,it’s a weaker isolation boundary.

If an agent escapes a container, it can touch everything on the host: data, other agents, and the internal systems. Containers lack the VM-level separation multi-agent systems need.

VMs Are Too Slow

Traditional VMs provide real isolation. Each has its own kernel, but they often take several seconds to boot. When agents operate in iterative loops, every second matters. VM latency breaks the workflow.

Compliance Becomes Chaotic

When agents run in production, you need data boundaries, audit trails, and execution isolation. If agents share environments or network access, enforcing boundaries requires custom logic everywhere. Akira solves this with:

• Per tenant encryption
• immutable audit logs
• SOC 2-designed infrastructure
• isolated execution by default

What's Actually Happening in Production

Teams are running agent workloads that the current infrastructure can't properly handle:

• Iterative agent loops where each execution depends on previous outputs
• Dynamic multi-agent orchestration where agents call other agents dynamically
• Non-deterministic execution path 
• Agentic CI/CD pipelines that generate and execute deployment logic
• Production systems requiring strict data boundaries

Companies face an impossible tradeoff: move fast and risk security incidents, or slow down and lose the automation benefits of agentic AI. Akira eliminates that tradeoff.

What Agent Infrastructure Actually Needs

Hardware-level isolation per execution. Each agent needs its own isolated environment with VM-level separation. Akira provides hardware-isolated micro-VMs for every execution.

1. Sub-second cold startup. Agents operate in loops. If infrastructure adds latency, it breaks the workflow. Akira boots in under a second (~625ms median in benchmarks).
2. Native integration with LLM and MCP.  AI should be built-in, not added later.
3. Isolation enforced by default. Execution, data, and network boundaries must be primitives, not add-ons. Built-in compliance. Audit logs, per-tenant encryption, and SOC 2-designed infrastructure should be part of the platform. 
4. Minimal DevOps overhead. Developers should integrate in minutes. Akira provides APIs, SDKs (Python, TypeScript), and CLI with no servers to manage.

How Akira Works

Akira is built specifically for the unpredictability of AI agents. One standard interface. Minimal DevOps overhead. Hardware-grade security.

Execution Flow

1. Agent Generates Code (Claude, GPT-4, Cursor or MCP-compatible client
2. API Handoff: MCP server sends request via Akira's API or SDK using the standard "run_code" interface
3. Isolated Execution: Akira executes securely in hardware-isolated micro-VMs with VM-level separation
4. Structured Output: Returns structured metadata (stdout, stderr, logs, exit codes) instantly

Built for Speed, Safety, and Scale

• Hardware-isolated execution ensures untrusted code never touches your infrastructure. Sub-1 second cold starts (~625ms median in benchmarks) make it feel instant for interactive workflows.
• MCP-native integration means it works with any MCP-compatible client (execution flow + built section). Plug and play reliability with Claude, OpenAI, and more.
• Enterprise-grade observability provides audit logs, per-tenant encryption, and SOC 2-designed infrastructure. Know exactly what your agents are doing.
• Cost-efficient infrastructure with up to 75% storage savings…(in benchmarks) via intelligent caching and deduplication. Simple pay-per-run pricing structure.
• Minimal DevOps overhead. Developers integrate in minutes. No servers to manage, no scaling headaches. Cloud-native orchestration handles everything.

Currently supports Python and TypeScript, with more languages coming based on developer demand.

What You Can Build

Anything your agent can generate:

• Swarm agent orchestration
• No/low-code platforms at scale
• Code evaluation and grading
• Data scraping and transformation
• Agentic CI/CD 
• LLM-powered research and analysis

Akira turns untrusted AI-generated code into production-safe execution, instantly.

The Execution Layer for the Agent Economy

As agents move from experiments to production, they need infrastructure built for how they actually operate: iterative, non-deterministic, orchestrated, and autonomous.

Akira provides the execution layer that makes it possible. Where untrusted AI code becomes production-ready code. In milliseconds and at scale.

When your agent executes code, it runs in a hardware-isolated micro-VM that boots in under a second. When agents orchestrate workflows, the platform handles dynamic execution natively.

The infrastructure for AI-native development is still being built. This is one piece of it.