SafeClaw vs Manual Code Review for AI Agent Safety

Manual code review catches bugs in your code, but it cannot catch decisions an AI agent makes at runtime. SafeClaw by Authensor solves this by gating every agent action — file writes, shell commands, network requests, code execution — through deny-by-default policies evaluated in real-time before execution. Your code review process and SafeClaw address fundamentally different attack surfaces.

The Core Problem with Manual Review

When you review code that integrates an AI agent, you're reviewing the scaffolding — the tool definitions, the prompt templates, the API calls. You are not reviewing what the agent will actually decide to do when a user asks it to "clean up my project directory." That runtime decision happens after deployment, and no amount of static analysis or peer review can predict it.

Consider this scenario: your code review approved a tool that lets the agent delete files matching a pattern. The code is correct. But at runtime, the agent decides rm -rf / matches the user's intent. Code review passed. The agent still destroyed the system.

What SafeClaw Does Differently

SafeClaw operates at the action layer, intercepting every tool call before it executes:

# .safeclaw.yaml
version: "1"
defaultAction: deny

rules:
  - action: file.delete
    path: "/tmp/**"
    decision: allow

- action: file.delete
    path: "**"
    decision: deny
    reason: "File deletion outside /tmp requires approval"

- action: shell.execute
    command: "rm *"
    decision: deny
    reason: "Destructive shell commands blocked"

With this policy, the agent can delete temp files but cannot touch anything else — regardless of what it decides at runtime.

Side-by-Side Comparison

| Capability | Manual Code Review | SafeClaw |
|---|---|---|
| Catches code bugs | Yes | No (not its job) |
| Catches runtime decisions | No | Yes |
| Prevents unauthorized file access | No | Yes |
| Prevents secret exfiltration | No | Yes |
| Enforces budget limits | No | Yes |
| Produces audit trail | No (just approval records) | Yes (hash-chained logs) |
| Evaluation speed | Hours/days | Sub-millisecond |

They're Complementary, Not Competing

The right answer is both. Review your agent integration code thoroughly. Then deploy SafeClaw to gate what the agent actually does at runtime. Code review is your first line of defense for code quality. SafeClaw is your runtime safety net for agent behavior.

Quick Start

Install SafeClaw in under 30 seconds:

npx @authensor/safeclaw

This initializes a deny-by-default policy file. Every action is blocked until you explicitly allow it. Your code review process stays exactly the same — SafeClaw adds runtime enforcement on top.

Why SafeClaw

• 446 tests covering policy evaluation, audit logging, and edge cases
• Deny-by-default — nothing executes without an explicit allow rule
• Sub-millisecond policy evaluation adds no perceptible latency
• Hash-chained audit trail for compliance and forensics
• Works with Claude AND OpenAI — no vendor lock-in
• MIT licensed — fully open source, zero lock-in

FAQ

Q: Can't I just review the agent's tool definitions carefully?
A: Tool definitions describe capabilities, not runtime behavior. An agent with a "write file" tool could write anything anywhere. SafeClaw constrains the "where" and "what."

Q: What about automated code analysis tools like SAST?
A: SAST tools find vulnerabilities in code. They cannot analyze what an LLM will decide to do at runtime. SafeClaw and SAST solve different problems.

Q: Does SafeClaw replace my CI/CD security checks?
A: No. Keep your existing security pipeline. SafeClaw adds a runtime layer that your CI/CD cannot provide.

Related Pages

• Running AI Agents Without Safety Controls
• SafeClaw vs Building Custom Safety Middleware
• Myth: AI Agents Always Follow Instructions
• Myth: The LLM Provider Handles AI Agent Safety