SafeClaw: Action-Level Gating for AI Agents - Why Monitoring Isn't Enough
AI agents are getting more capable every month. They read your files. They execute code. They make network requests. They interact with databases, APIs, and system services. And the pace is accelerating.
But here's the uncomfortable question: what actually stops an AI agent from doing something dangerous?
The Current State of AI Agent Safety
Today's agent safety tools fall into two broad categories:
1. Monitoring Tools
These watch what an agent does and log it. Some alert you when something suspicious happens. But by the time you see the alert, the action has already executed. The file was already written. The network request was already sent. The damage is done.Monitoring is observation, not prevention.
2. Sandboxing Tools
These restrict the perimeter. They define coarse-grained boundaries - this agent can access these directories, but not those. This agent can make HTTP requests, but not to internal IPs.Sandboxing is better than monitoring, but it's blunt. It can't evaluate individual actions in context. It can't distinguish between an agent writing a harmless log file and an agent writing to a sensitive configuration file - if both are within the allowed directory, both pass.
What's Missing: Action-Level Gating
Neither monitoring nor sandboxing evaluates individual actions before they execute. That's the gap.
Action-level gating means intercepting every single action an AI agent wants to perform - every file write, every code execution, every network request - and evaluating it against a dynamic policy engine before it touches your system.
Not batched. Not reviewed later. Not broadly permitted or denied by category. Each action, individually, in real time.
This is what SafeClaw does.
How SafeClaw Works
SafeClaw sits between your AI agent and your computer. When an agent wants to perform an action:
- The agent requests the action - write a file, run code, make a network request
- SafeClaw intercepts it - the action is paused before execution
- The policy engine evaluates it - against your configured rules and conditions
- The action is resolved - allowed, denied, or held for human approval
Deny-by-Default Architecture
SafeClaw follows a deny-by-default model. Every action is blocked until you explicitly create a rule that allows it. This is the opposite of most tools that start permissive and ask you to add restrictions.
The advantage: you can never be surprised by an action you didn't anticipate. If you didn't write a rule for it, it doesn't happen.
Dynamic Policy Engine
SafeClaw's policy engine supports:
- Conditional rules: Allow file writes only to specific directories. Allow network requests only to whitelisted domains.
- Simulation mode: Test your policies before deploying them. See what would be allowed or denied without actually enforcing.
- Version control: Roll back to a previous policy version if something goes wrong.
- Effect types: Allow, deny, or escalate to human approval.
Provider-Agnostic Design
SafeClaw works with Claude and OpenAI. It's not locked to any single agent framework. If your agent can describe its actions in a standard format, SafeClaw can gate them.
Tamper-Proof Audit Trail
Every action - whether allowed, denied, or pending - is recorded in a cryptographic hash chain. Nobody can alter the audit trail after the fact. This matters for compliance, debugging, and accountability.
Local-Only Security
Your API keys stay on your machine. SafeClaw runs locally. Nothing is sent to external servers. Your agent's actions and your policies remain private.
The Numbers
- 446 automated security tests - comprehensive coverage of the gating engine, policy evaluation, and audit trail
- Zero third-party dependencies beyond the AI SDK - minimal attack surface
- 100% open source - inspect every line of code
- Full visual dashboard - not CLI-only; manage policies, review actions, and monitor agents through a browser interface
- Installs in under 60 seconds - one command, no configuration files to write
Why This Matters Now
The AI agent ecosystem is expanding rapidly. Agents are being given more tools, more autonomy, and more access to sensitive systems. The safety infrastructure hasn't kept up.
Monitoring tools tell you what went wrong. Sandboxing tools give you coarse boundaries. But neither gives you the granular, per-action control that real security requires.
Action-level gating is the next layer. It's the difference between knowing your agent wrote to /etc/passwd and preventing it from happening in the first place.
Try SafeClaw
Getting started takes three steps:
- Have Node.js installed
- Open your terminal
- Run:
npx @authensor/safeclaw
SafeClaw is built on Authensor, an open authorization framework for AI agents.
Links:
Try SafeClaw
Action-level gating for AI agents. Set it up in your browser in 60 seconds.
$ npx @authensor/safeclaw