Tech Leads & Managers

Safety Rails
For Your Team

AI tools will fail. Your junior devs will make mistakes. Caro provides deterministic safety validation that catches what AI hallucinations and permission flags miss— without you having to review every command.

52+ dangerous patterns blocked automatically
Get Started Free See Real Incidents

AI Tools Have Failed. Repeatedly.

Real incidents from AI coding assistants

Claude Code Dec 2024

Deleted project files when asked to "clean up the repo"

Damage: Local repository deleted Source: Hacker News
Gemini CLI Jan 2025

Executed rm -rf in wrong directory after misunderstanding context

Damage: Production data loss Source: Twitter/X
AI Coding Assistant Nov 2024

Generated curl | bash command that downloaded malicious script

Damage: Compromised development machine Source: Security Blog
AI Shell Helper Oct 2024

Suggested chmod 777 to "fix permissions" on /etc

Damage: Security vulnerability created Source: GitHub Issue

Caro Would Have Caught These

Pattern-based validation doesn't depend on AI judgement. When the AI marks rm -rf / as "Safe", Caro's deterministic patterns still catch it.

Jobs Caro Does for Tech Leads

What you hire Caro to do for your team

Team Safety Standardization

Pain: 4/5

Deploy org-wide safety patterns without micromanaging individual engineers.

Trigger: New team member or after incident

AI Hallucination Protection

Pain: 5/5

Catch when AI tools suggest dangerous commands before they can execute.

Trigger: Team using AI coding assistants

MCP Integration for AI Agents

Pain: 4/5

Give AI agents safe shell command capabilities with guardrails.

Trigger: Deploying AI agents that need shell access

Team-Wide Safety Configuration

Set it up once, protect the whole team

📋

Custom Pattern Library

Define organization-specific dangerous patterns. Block commands that are risky in YOUR environment.

Block deploys to prod on Fridays: deploy.*production.*--no-backup
👁️

Audit Logging

Log every command generated and every pattern matched. Full visibility for compliance.

Export: caro logs --json > audit.json
🔒

Risk Level Configuration

Set team-wide safety levels. Strict mode blocks, moderate warns, permissive logs.

Config: safety_level = "strict"
🚫

Allowlist/Blocklist

Pre-approve specific patterns or block them entirely. No runtime decisions needed.

allowlist = ["kubectl get", "docker ps"]

Example Team Configuration

~/.config/caro/config.toml 
[safety]
level = "strict"  # strict | moderate | permissive

# Custom patterns for your organization
[[safety.custom_patterns]]
pattern = "deploy.*production.*--force"
risk_level = "Critical"
description = "Force deploy to production"

[[safety.custom_patterns]]
pattern = "kubectl delete namespace production"
risk_level = "Critical"
description = "Delete production namespace"

# Allowlist safe operations
[safety.allowlist]
patterns = [
  "kubectl get",
  "docker ps",
  "terraform plan"
]

[logging]
enabled = true
path = "/var/log/caro/commands.log"
format = "json"  # For SIEM integration

Why Pattern-Based Beats Permission-Based

Flags fail. Patterns don't.

Permission Flags Fail Because:

  • AI can convince itself to approve dangerous operations
  • Context windows lose track of what's allowed
  • Prompts can be jailbroken
  • Stochastic systems have edge cases

Pattern Matching Works Because:

  • Deterministic: same input = same result
  • Auditable: you can see exactly what matched
  • Can't hallucinate: regex doesn't make things up
  • Fast: <50ms validation, no API calls

The Math Problem

If your AI tool is 99.9% accurate and your team runs 1,000 commands/day:

1,000 commands × 0.1% failure = 1 dangerous command/day Over a year: 365 potential incidents

Caro provides a deterministic layer that catches the 0.1%. 52 patterns × 0 hallucination = 0 bypasses.

MCP Integration

Give AI agents safe shell capabilities

Claude Desktop Integration

Available

Let Claude generate shell commands through Caro's MCP server. Every command validated before execution.

Claude Code Safety Layer

Coming Soon

Add Caro as a safety layer for Claude Code. Catch hallucinations before they execute.

Custom AI Agent Safety

Available

Any AI agent using MCP can route shell commands through Caro for validation.

How It Works

AI Agent
→
MCP Request
→
Caro Validation
→
Shell Execution

Deployment Options

From individual to enterprise

Individual Install

Each engineer installs Caro locally

Pros:
  • Quick setup
  • No infrastructure needed
Cons:
  • Manual config sync
  • No centralized logging
Best for: Small teams, getting started

Shared Config

Team-wide config file in repo

Pros:
  • Consistent patterns
  • Version controlled
Cons:
  • Manual distribution
Best for: Medium teams, growing adoption

Enterprise Deploy

Centralized management and logging

Pros:
  • Full visibility
  • Audit compliance
Cons:
  • More setup
Best for: Large teams, compliance requirements

Try Caro in 30 Seconds

No account. No API key. No data collection. Just safer shell commands.

bash <(curl --proto '=https' --tlsv1.2 -sSfL https://setup.caro.sh)

Then run:

caro "find files modified in the last 7 days"
Installs to ~/.cargo/bin
Single binary, no dependencies
Uninstall anytime: cargo uninstall caro

Prefer to build from source? See all installation options →