Introducing Silo: A Secure Runtime for AI Agents in Production

TL;DR

AI agents are no longer just smart chatbots - they are autonomous workloads with access to your code, APIs, and data. Prompt-level guardrails aren’t enough to secure them. Silo is a secure execution runtime built by Oort Labs that sandboxes AI agents, enforcing strict access controls, network policies, and full auditability without breaking your existing workflows.

The Problem: AI Agents Are Not Just Prompts

Today’s AI security conversation focuses heavily on guardrails at the prompt layer:

Content filters
System prompts
Input sanitisation
Output validation

Those matter. But they’re not enough.

An AI agent with access to your source code, credentials in environment variables, network egress, and database read permissions is no longer just a language model. It’s a runtime. And runtimes need isolation.

What Silo Is: A Secure Runtime for AI

Silo is a secure execution runtime for AI agents. It treats agents as untrusted workloads, policy-constrained actors, and auditable system participants.

Each agent runs inside a hardened execution boundary with:

Strict file access control: Agents only see what they explicitly need.
Policy-enforced secret mediation: Credentials are never exposed directly to the model.
Inspected and controlled network egress: Outbound connections are strictly whitelisted and monitored.
Full telemetry of runtime behaviour: Every action is logged and auditable.

The agent never directly touches raw secrets, sensitive files, or the open network. Everything flows through policy. If an agent wants to read a file, it must be allowed. If it wants to call an API, it must be authorised. If it attempts something unusual, it is observable.

This is not prompt-level defence. This is system-level control.

Treating AI Agents Like Staff

Organisations monitor employees who have access to production systems, sensitive documents, and financial controls. They apply least privilege, logging, policy enforcement, and behavioural monitoring.

AI agents should be treated the same way. Not because they are malicious, but because they are autonomous.

Silo provides constrained capabilities, observable actions, enforceable policies, and full audit trails. Security teams can apply familiar detection and response patterns to AI workloads. This reduces uncertainty, enables accountability, and makes AI governance practical.

How It Fits Into Your Stack

Silo is not a model. It is not a guardrail wrapper. It is infrastructure.

It sits between the agent, your data, your secrets, and your network. It enforces access boundaries while allowing useful work to happen. Existing AI frameworks can run inside it. Existing models can be used. Existing workflows remain intact. What changes is the execution boundary.

What Silo Is Not

A content moderation tool
A prompt firewall
A chatbot platform
A sandbox for running random code snippets

It is a secure runtime designed specifically for autonomous AI agents operating in production environments. The difference matters.

The Road Ahead

This is the first step. Our focus is on strong isolation, policy-driven control, production-grade observability, and practical integration into real systems.

AI agents are going to become core parts of business workflows. The question is not whether organisations will use them. The question is whether they will run them safely. Silo exists to make that possible.

If you are building with AI in production and thinking seriously about isolation, governance, and runtime control, you are already in the right conversation. Welcome to Silo.

FAQ

Does Silo replace my existing LLM guardrails?

No. Silo complements prompt-level guardrails (like input sanitisation and output validation) by providing a secure execution environment. Guardrails protect the model; Silo protects your infrastructure from the model.

Which AI frameworks does Silo support?

Silo is designed to be framework-agnostic. Because it operates at the infrastructure level, you can run popular frameworks like LangChain, LlamaIndex, or custom agent architectures inside the Silo runtime.

Will Silo slow down my AI agents?

Silo is built for production performance. While it introduces a policy enforcement layer, the latency overhead is minimal and designed not to bottleneck standard agent workflows.