AI development thrives when execution is frictionless, but unrestricted autonomy can introduce risk. Sandboxing and autonomy modes reconcile speed with safety. You allow AI to execute in trusted scopes and require manual approval in sensitive contexts.
The Three Modes
Assistant Mode
AI suggests changes. You approve each action. This is safe but slow.
Agent Mode
AI executes within a defined sandbox. It runs tests, modifies files, and fixes errors without constant approvals. This maximizes momentum in trusted environments.
Autonomous Mode
AI operates end-to-end with minimal human intervention, typically within CI/CD pipelines. You review results rather than steps.
Why Sandboxing Matters
You can define boundaries: which directories AI can write, which commands it can run, and which external resources it can access. This prevents accidental damage while preserving the speed benefits of autonomy.
Practical Uses
- Allow AI to run tests automatically.
- Permit file edits in the project workspace.
- Block network access unless explicitly required.
- Require manual approval for dependency installation.
Human-in-the-Loop, Not Human-in-the-Way
The goal is to shift from constant approvals to strategic oversight. You define the rules once, then let AI operate within them. You step in only when decisions exceed the sandbox’s scope.
Risks and Mitigations
- Risk: AI executes risky commands. Mitigation: strict allowlists and logging.
- Risk: AI installs unsafe dependencies. Mitigation: manual approval for new packages.
- Risk: AI alters sensitive data. Mitigation: isolate environments and use mock data.
Autonomy modes allow you to tailor the level of AI independence to the task’s risk profile. You can move fast without losing control.