The trust economy for autonomous AI agents. Credit scores for machines. Agents earn Trust Capital through verified behavior, gating what they are allowed to do. Autonomy earned, not granted.

Open-source FDE toolkit — pre-commit audit + knowledge base + harness layer；开源 FDE 工具包：提交即审计 + 知识自沉淀 + AI 约束层。

Agent says done. VALP asks for proof.

CML (Causal Memory Layer) — a foundational memory layer for recording reasons, permissions, and responsibility behind actions, not just events or results. Enables systems in AI, fintech, security, and distributed computing to preserve meaning and causal accountability across time, independent of execution or transport.

Java-native runtime for long-running AI agents with live execution control, human approvals, and audit trails.

The open specification and reference SDK for recording AI Agent decision chains. Every decision, recorded. Every alternative, documented.

Cryptographically verifiable audit trail for AI systems — Ed25519 signed, hash-chained Decision Receipts with ISO 42001 / NIST AI RMF compliance. No blockchain, no SaaS.

Apohara agent audit + seal — consolidated monorepo (codename: probanza, final product: synthex). 5-layer seal: HMAC + Ed25519 + RFC 3161 + Rekor v2 + C2PA.

Claude Code skill for static audit of OpenClaw config and agent proposals — llms.txt-first research, F1–F12 incident library, Notion-bridged Galatea audit workflow.

Proof-first tools for AI agents. So humans stay accountable when machines fail.

Replayable, reviewable, rollbackable evidence bundles for autonomous coding runs.

Forward-deployed AI implementation toolkit: customer context, readiness scoring, audit trails, rollout checks, and MCP patterns.

Local-model mode for your existing OpenClaw/Codex workspace.

Open-source CLI that audits AI agents for security vulnerabilities, performance issues, and model fit — based on OWASP Agentic AI Top 10