The original title is: "Why your AI agent needs deterministic guardrails (and how to add one in a few lines)"

Short summary

Autonomous AI agents with tool access often fail catastrophically in production—installing hallucinated packages, falling for prompt injection, writing vulnerable code, sending payments to scam addresses. Instead of adding an LLM judge (slow and corruptible), implement deterministic guardrails: stateless rule-based checks that verify packages, scan for injection attacks, audit code for vulnerabilities, and screen payment addresses against sanction lists. Free guard APIs integrate as MCP servers for Claude Code, Cursor, and other agent tools.

• •Agents fail in production via hallucinated package installs (slopsquatting attacks), prompt injection in web scrapes, AI-written code with security bugs, and unauthorized payments to sanctioned addresses.
• •LLM judges as guardrails fail—they add latency and can be socially engineered by the same injection attacks they're supposed to catch.
• •Use deterministic, stateless guardrails instead: free APIs checking package registries, scanning for injection patterns, auditing code, screening payment addresses; integrate as MCP servers.

Generated with AI, which can make mistakes.