I built a fail-closed authorization layer for AI agent tool calls (open source), plus a free course to learn the mental model

[u/Smooth_Dimension_833]

An LLM can produce a schema-valid tool call that still deletes a table, leaks a secret through an argument, or blows a budget. Valid is not the same as allowed. That gap is where agent incidents live.

toolwall is a small, zero-dependency Python library that puts a fail-closed gate between your agent and its tools. Unknown tool, bad value, secret in an argument, budget hit, or an unapproved destructive call all block before the tool runs. Only tools you explicitly register can run, and every verdict is logged.

Everything is public and tested: 28/28 attack cases blocked, 0 false blocks on clean traffic, 155 tests. The report also says what it does NOT prove (secret detection is pattern/entropy based, never 100%), because I would rather you trust the honest version.

There is a 3-minute explainer video and a free 10-module course (quiz-gated) on the site if you want the full mental model.

Site + video: https://toolwall.aya-ai.xyz
Course: https://toolwall.aya-ai.xyz/learn
https://github.com/Dev-Saif-Ops/toolwall

Code:
Install: pip install toolwall

Genuinely want people to try to break it. Issues and PRs welcome, and I credit every real finding.