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Control AI agents with rule checks, audit logs, and a global kill switch.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Habenula" yet — see the docs or source repo.
Help me design a control policy for a coding agent: allow reading the project directory and running tests; forbid pushing to remote repositories and changing production configuration; require confirmation before any file deletion.
A clear set of agent control rules that limits risky actions and keeps human approval checkpoints.
List which consequential AI agent actions I should log for tamper-evident auditing, including command execution, file changes, external calls, and rule-block events.
A checklist of key events suitable for an audit chain to track agent behavior.
Create a global kill-switch policy for my AI agent system, including when agents should be stopped immediately and what manual review steps should follow.
A shutdown and follow-up review procedure that reduces the risk of agent misbehavior.
Developers can use Habenula to check consequential actions against user-defined rules before coding agents run commands, modify files, or use external capabilities, helping prevent overreach and risky behavior.
When teams need to trace what AI agents have done, Habenula logs all actions to a tamper-evident audit chain for later review, troubleshooting, and accountability.
If an agent makes abnormal decisions, attempts unauthorized actions, or behaves unexpectedly, users can quickly stop it with the global kill switch and regain control.
It is a personal control harness for governing AI agents. It checks consequential actions against user-defined rules, logs all actions to a tamper-evident audit chain, and provides a global kill switch.
The provided information says it can run as an MCP server sidecar alongside coding agents or as a standalone service. Specific installation and configuration steps are not provided; see the source repository.
Its focus is not primarily on performing tasks for the agent itself, but on keeping users in control of agent permissions and behavior. The key differences are rule enforcement, audit logging, and a global kill switch.
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Add human approval and tamper-evident logs to risky AI agent actions.
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