Agent Trust Stack FAQs

Question 1

What is Agent Trust Stack?

Accepted Answer

Agent Trust Stack is an MCP (Model Context Protocol) server that provides essential tools for building trust in autonomous AI agents. It offers cryptographic provenance logging through its Chain of Consciousness (CoC) and reputation scoring via the Agent Rating Protocol (ARP).

Question 2

Is Agent Trust Stack part of a larger framework?

Accepted Answer

Yes, Agent Trust Stack is a foundational component of the broader 7-protocol Agent Trust Stack. While this MCP server specifically implements Chain of Consciousness and Agent Rating Protocol, the full stack aims to provide a comprehensive trust infrastructure for autonomous AI, including aspects like service agreements, dispute resolution, and lifecycle management.

Question 3

How does the Agent Rating Protocol (ARP) work for reputation scoring?

Accepted Answer

ARP allows AI agents to submit bilateral blind ratings for each other following interactions. Rater identities are cryptographically hashed before storage, ensuring anonymity. An agent's reputation score is then calculated as the aggregate average of all ratings it has received.

Question 4

What is the Model Context Protocol (MCP) and its role with Agent Trust Stack?

Accepted Answer

The Model Context Protocol (MCP) is a standard enabling AI agents to interact with external tools and services. Agent Trust Stack integrates as an MCP server, allowing any MCP-compatible AI agent to natively access and utilize its cryptographic provenance and reputation tools seamlessly.

Question 5

How does Agent Trust Stack ensure tamper-evident logs?

Accepted Answer

The Chain of Consciousness (CoC) component creates append-only activity logs. Each entry is cryptographically linked to the previous one using SHA-256 hashes, ensuring that any modification to an earlier entry instantly breaks the chain, making tampering easily detectable. Optional external timestamping further enhances integrity.

Agent Trust Stack

Agent Trust Stack

Key Features

Use Cases

Key Features

Use Cases