What is Compartment?
Compartment is an open-source runtime designed to securely deploy and share applications built with AI agents. It provides teams with a self-hosted platform that isolates individual agent instances, enforcing strict access controls and ensuring sensitive operations remain contained. Built from the ground up for AI agent applications, it addresses the security and operational complexity of running untrusted or semi-trusted code within team environments.
The platform creates isolated sandboxes for each agent application, preventing unauthorised access to system resources or neighbouring instances. Teams can define granular permissions, control which agents can access external systems, and maintain complete audit logs for compliance and troubleshooting. Since it is self-hosted, organisations retain full control over their data and infrastructure, making it suitable for regulated industries or those requiring data sovereignty.
Compartment fills a specific gap in the AI agent ecosystem by treating security and team collaboration as first-class concerns rather than afterthoughts. It's particularly valuable for teams building internal tools powered by agents, sharing agent-driven applications across departments, or deploying agents in environments where security isolation is non-negotiable.
Key Features
Isolated environments
Each agent application runs in an isolated context, preventing cross-contamination and unauthorised resource access
Access controls
Fine-grained permissions let you specify which agents can interact with external systems, APIs, or sensitive resources
Private system connectivity
Agents can connect securely to internal systems without exposing infrastructure or credentials
Audit logs
Complete activity logging for compliance, debugging, and security monitoring
Self-hosted deployment
Deploy on your own infrastructure for full data control and compliance flexibility
Team management
Built-in support for multiple users and teams with role-based access
Pros & Cons
Advantages
- Open source with no vendor lock-in or licensing restrictions
- Purpose-built for AI agents rather than retrofitted from general runtimes
- Self-hosted deployment ensures data sovereignty and regulatory compliance
- complete audit trails support compliance and incident investigation
- Strong isolation model prevents agents from interfering with one another
- Direct access to private systems without exposing credentials or infrastructure
Limitations
- Requires technical expertise to deploy, configure, and maintain
- You are responsible for infrastructure, updates, scaling, and security patches
- No managed or fully-hosted option available if you prefer outsourced operations
- Relatively new project, so community and ecosystem are still growing
- Documentation and examples may be limited compared to established platforms
- Operational overhead increases with team size and complexity
Use Cases
Deploying internal AI agent tools securely across departments
Sharing AI-powered applications with external teams whilst protecting internal systems
Running agents in regulated industries where audit trails and isolation are mandatory
Building AI agent applications for organisations requiring data to remain on-premise
Creating multi-tenant agent platforms with strict isolation between customers
Orchestrating multiple AI agents within a single secure environment