Rulebound
Summary
Deterministic guardrails for AI coding agents: policy-as-code that runs the deterministic part of code review (AST checks, regex, diff analysis) against agent plans, diffs, and evidence. Its .rulebound/rules/ and docs/threat-model/ are directly reusable for agent QA.
Key Takeaways
- 'Do not trust the agent' - verify plan, diff, and evidence deterministically
- Rule files as review gates make agent policy versionable
- Ships an actual threat model for agentic coding
Reliability Note
Open-source project; threat-model docs are unusually rigorous for the space.
Flows informed by this source
5
Rule-File Code Review Gates
Run deterministic policy checks on everything your agent produces: rule files reviewing plans, diffs, and evidence before anything merges.
+1 more steps to Done
Best forbuilders who have shipped a basic app before
Threat-Model Your MCP Server
Systematically threat-model an MCP server before it is exploited: assets, entry points, trust boundaries, and mitigations documented and tested.
+1 more steps to Done
Best forproduction-grade builds with strict verification
Release Gates for Agent-Generated Code
Put agent output through a real merge gate: automated review, tests, security scans, and human sign-off proportional to risk.
+1 more steps to Done
Best forbuilders who have shipped a basic app before
Dependency and Supply-Chain Scanning for Agents
Guard against the dependencies your agent adds: vulnerability scanning, hallucinated-package detection, and gated installs.
+1 more steps to Done
Best forbuilders who have shipped a basic app before
Secrets Hygiene in Agent Logs
Keep credentials out of prompts, tool outputs, logs, and traces: detection, redaction, and safe handling across the whole agent pipeline.
+1 more steps to Done
Best forbuilders who have shipped a basic app before