We're creating the foundational infrastructure that enables AI agents to operate safely, transparently, and accountably in the real world.
As AI agents become more capable and autonomous, we face a fundamental challenge: How do we establish trust between humans, organizations, and AI systems?
DAT (Decentralized Agent Trust) provides the answer. We're building an open-source identity and trust infrastructure that gives every AI agent a verifiable identity, tracks their reputation based on actual behavior, and enables fine-grained authorization controls.
Today, DAT powers a live platform with 19 microservices, 11 MCP tool servers, 20 compound skills, multi-agent delegation, an agent marketplace, and omnichannel messaging across Telegram, Slack, and Teams — all governed by trust-gated autonomy.
Trust infrastructure should be transparent and auditable. That's why DAT is 100% open source.
No single entity should control agent identity. We use DIDs for self-sovereign agent identities.
Agents and their operators maintain control over their data with selective disclosure.
Built on W3C DIDs, ERC-8004, MCP, A2A, and emerging agent protocols. Integrates with Telegram, Slack, and Teams.
AI agents are rapidly moving from research to production deployment.
Major AI labs release agent frameworks. Autonomous coding assistants, research agents, and trading bots proliferate.
Protocols like MCP and A2A enable agents to work together. But how do agents verify each other's identity and trustworthiness?
DAT deploys 19 microservices, 11 MCP tool servers, multi-agent delegation, an agent marketplace with negotiation, and omnichannel messaging across Telegram, Slack, and Teams. Trust-gated autonomy becomes real.
Leveraging proven technologies and emerging standards.
Ed25519 signatures provide fast, secure, and quantum-resistant cryptographic operations for agent authentication.
Built on the W3C DID Core standard. The did:dat method provides cryptographically verifiable, self-sovereign identity for AI agents.
Trust scores update instantly based on agent behavior, with time-decayed signals and category weighting.
Ensemble learning (Isolation Forest + Autoencoder + LSTM) identifies suspicious patterns in real-time.
Ed25519 signed trust signals and task steps with SIEM webhook export to Splunk, Sentinel, Elastic, and Datadog.
Telegram, Slack, and Microsoft Teams with live step streaming, inline HITL approvals, and voice-to-agent via Whisper.
We believe trust infrastructure for AI must be secure, auditable, and built to enterprise standards. DAT is designed with defense-in-depth security, cryptographic verification, and full compliance readiness — giving organizations the confidence to deploy autonomous AI agents at scale.
Ready to get started? Read our documentation or contact our team for a personalized demo.
Help us build the trust infrastructure for the autonomous agent economy.