Who it is forA developer building an agent that proposes writes and needs an approval step with a clear audit trail.
The problemA write can change after review, or an approval can be replayed to run a different operation.
The outcomeAn audit receipt that shows the requested operation and the policy decision, with approvals bound to the exact request.
What you need first
- Node.js, npx, and python3 installed for the coding agent tutor
- A skill-capable coding agent such as Claude Code or Codex, with a writable skills scope
- Python 3.10 or newer for the retrieval and firewall projects
- Node.js 22.18 or newer for the agent trace debugger
- A clone of the repository so the project_test.py grader is available
Steps
- Confirm a Rust toolchain is installed, since the README names Rust but does not give a version. (inferred, check the README)
- Run
python3 scripts/project_test.py tool-call-firewall --init learning-artifacts/tool-call-firewallfrom the repository root. (inferred, check the README) - Validate the call envelope and check the caller's role and path in the starter.
- Consume an approval bound to the exact request and content, then run
python3 scripts/project_test.py tool-call-firewall --all --path learning-artifacts/tool-call-firewall --strict. (inferred, check the README) - Keep the audit receipt and confirm the replay test fails.
Prompt for your agent
In the repo rohitg00/ai-engineering-from-scratch, run the tool-call-firewall project with --init into learning-artifacts/tool-call-firewall, then implement the stages until the --all --strict grader passes. Expected output: an audit receipt for each tool call with the policy decision. Verify that a replayed approval and an altered write are both rejected.
Caveat. The caller application supplies identity, and the project does not provide persistent authorization or an operating-system sandbox.
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Disclaimer
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