Share-ready summary

I shipped a case study on mcpgate v1.1: a local governance gateway for AI-agent tool calls. The work focuses on a practical security problem: how to catch risky prompts, tool arguments, and reverse-channel behavior before an agent turns them into production-impacting actions. The key takeaway: I can identify and reduce AI-agent security risk before it becomes production damage.

Built mcpgate, a local AI-agent governance gateway focused on pre-flight policy checks, injection heuristics, reverse-channel risk detection, and audit-friendly decisions. The case study explains the security problem, implementation tradeoffs, and validation path.

Designed and shipped mcpgate v1.1, a local AI-agent governance gateway that reduces tool-call risk with pre-flight policy checks, injection heuristics, reverse-channel detection, and audit-ready decision records.

Hi, I wanted to share a concise case study that represents the kind of engineering work I want to do next: identifying and reducing AI-agent security risk before it becomes production damage. It covers mcpgate v1.1, a local governance gateway for agent tool calls, including the problem, design tradeoffs, and validation path.