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Govern AI agents with external RBAC, tracing, and policy compliance evaluations.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Warden" yet — see the docs or source repo.
Design an RBAC policy for my MCP toolchain: the development agent can only read logs and deployment status, but cannot modify production configuration. Provide roles, a permission matrix, and example policies.
A clear role-permission design with boundaries and executable policy examples.
Help me plan how to use OpenTelemetry to trace each MCP tool call made by an AI agent, including key spans, log fields, error tags, and audit query suggestions.
An observability and audit plan describing what traces and logs should be collected.
Create an LLM-as-judge evaluation suite for my AI agent to verify it always follows access policies. Include test scenarios, scoring criteria, and violation examples.
An evaluation plan for compliance testing that helps uncover overreach and policy bypass issues.
Securely run signed agent skills with sandboxing, trust scoring, and audit transparency.
Enforce permissions, approvals, sanitization, and audit for AI agent MCP calls.
Manage agent credentials, spending, approvals, and audit trails safely.
Protect MCP-connected agents with PII redaction, rate limits, and policy enforcement.
Gate MCP agent actions through compliance policies before execution.
Add pre-execution safety checks for AI agents on Base transactions.