Sandboxing
intermediateIsolating AI agents in controlled environments that limit their ability to affect the real world. Sandboxes allow testing agent capabilities and behaviors while preventing unintended consequences.
Overview
Sandboxing is a critical safety technique for AI agents. Before giving an agent access to real systems, APIs, or data, it operates in a constrained environment that simulates real capabilities without real consequences. A well-designed sandbox provides realistic feedback so agents behave authentically while preventing actual harm. This allows observation of agent behavior, testing of edge cases, and validation of safety measures. Key challenges include making sandboxes realistic enough to elicit genuine behavior and detecting when agents might behave differently in sandboxed versus production environments.
Key Concepts
Environment Simulation
Creating realistic mock versions of production systems.
Capability Limiting
Restricting what actions an agent can actually execute.
Behavioral Monitoring
Observing and logging all agent actions and reasoning.
Escape Detection
Identifying attempts by agents to break out of sandbox constraints.
Real-World Use Cases
- 1Pre-deployment agent testing
- 2Safe capability evaluation
- 3Training environment for agent learning
- 4Incident investigation and replay