Use Cases

SCC CLI addresses specific challenges teams face when adopting AI coding agents. This section explores the most common use cases.

Use Case Guides

Why Sandbox AI Coding Agents Understand the risks of unrestricted AI execution and how sandboxing mitigates them

AI Coding Agent Governance See how organizations roll out Claude, Codex, or both with delegated team ownership

Network Controls for AI Coding Agents Understand why containers are not enough and how SCC controls outbound reach

Run Agents in Containers Run Claude, Codex, or both in container sandboxes for isolation and reproducibility

AI Coding Guardrails Implement safety engine, plugin governance, and network enforcement for AI-assisted development

Common Scenarios

Enterprise Teams

Organizations adopting AI coding agents across multiple teams need:

• Consistent configurations (team profiles across providers)
• Security policies (plugin governance, network control)
• Provider management (Claude Code, Codex, or both)
• Diagnostics and audit surfaces (scc config explain, dry runs, scc support launch-audit)

Security-Conscious Development

Teams working with sensitive code want:

• Container isolation (OCI sandboxing)
• Built-in safety engine (fail-closed git and network command interception)
• Topology-enforced network egress (proxy sidecar, not just env vars)
• Protected git history (safety engine + optional safety-net plugin)

Parallel AI Development

Developers running multiple AI experiments need:

• Branch isolation (git worktrees with protected branch prompts)
• Session management (resume, list, stop, prune)
• Provider flexibility (different providers for different tasks)
• Clean separation from main development

Getting Started

Quick Start Install and run SCC in minutes

Core Concepts Understand providers, sessions, and safety

Security Model Understand the network and isolation layers behind the use cases