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.
Fail-closed AI agent governance — approve or block agent actions
in real time, score compliance risk, and generate tamper-evident receipts.
Free tier: 10 governed actions/day. Upgrade for unlimited + Ed25519-signed
audit receipts.
Governance kernel for AI agents — policy enforcement, code safety verification,
multi-model hallucination detection (CMVK), trust attestation (IATP), and immutable
audit trails. Works with Claude Desktop, Cursor, and any MCP client.
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.
Universal verifiable recovery for long-running AI agents with semantic checkpoints, idempotent action ledger and hash chained log as a deny by default MCP server. Framework agnostic with adapters for LangGraph, LangChain and OpenAI plus gateway and OTel.
Provides cryptographic governance receipts for AI agents, enabling pre-execution evaluation and signed verdicts (EXECUTE/BLOCK/REVIEW/SHADOW) with offline-verifiable audit trails.
Deterministic, auditable payment policy enforcement for AI agents. It provides pre-action authorization with scopes, budgets, allowlists, and signed mandates via an MCP server.
