Universal governance layer for AI agents — MCP-native,
fail-closed, LNN interpretability. Governed receipts, IPFS audit proofs, and
rollback for any agent in any framework.
DingDawg Loop Protocol (DDLP) — safe scheduled AI agents
with
governance gates. Every loop execution is verified, receipted, and
fail-closed. MCP-native, works with CrewAI, LangGraph, Claude Code, Cursor.
Governance primitives for autonomous agents. Verify actions against policy, record signed provenance, and bind intents cryptographically.
Free tier available.
Identity and credential governance for AI agents. Every agent gets its own cryptographic identity,
scoped short-lived credentials per platform, human approval on sensitive actions, and an immutable
audit log.
A governance proxy for AI tools — every MCP/agent tool call is policy-gated, secret-redacted, and written to a hash-chained, offline-verifiable audit trail.
Cryptographic proof of consent for AI agents. Sign before you act. Policy engine enforces spending caps, action whitelists, and escalation rules. Independently verifiable by anyone.
Local zero-trust permission gateway for AI agents. Enforces policy-based tool authorization, human approvals, scoped permissions, and cryptographically verifiable audit logs.
The Control Plane for Autonomous AI
Enforce policy before execution, require human approvals where risk demands it, and keep a full audit trail — from first action to final result.