AI agents are quickly moving from simple chat interfaces to systems that can use tools, access data, trigger workflows, write messages, and sometimes take actions on behalf of users. That shift is exciting, but it also creates a serious security question:
How do we evaluate the risk of an AI agent before we deploy it?
That question led me to build AgentGuardian, a local-first AI security web app that scans agentic AI workflows for risks such as prompt injection, tool misuse, excessive autonomy, sensitive data exposure, insecure output handling, and lack of human oversight.
The goal was to build a practical prototype using:
Python
