Register an AI Agent

Registering and connecting AI Agents to TrustLogix

1. Create Agent

• To register an AI Agent into the TrustLogix system, you can navigate to the AI Agents screen using the top left menu.

• Here, you can click Create Agent, and provide name, description, and status.

Only AI Agents set to the "ACTIVE" status will be available in the policy engine

2. Configure Agent

• Once created, you MUST set up the agent configuration. Navigate to the created AI Agent, then to the Configuration tab.

• Here, you have the option to configure the Entitlement Source and OAuth.

Currently, only other registered data accounts are supported as entitlement sources. Direct entitlement sync from configurable sources is a planned feature.

• OAuth configuration corresponds to the AI Agent identity.

• We currently recommend using an existing registered application, as this identity will be used for agent activity monitoring in other registered systems in TrustLogix as well as Just-In-Time policies.

Ensure the provided scope follows Client-Credential/Self-Issued scope formats according to your Identity Provider (i.e. Azure uses 'api://{client-id}/.default', Okta uses 'default', etc)

If connecting to Databricks, select "OAuth User to Machine Per User" for Auth type.

• After completing the form and clicking Save, an optional API-Key is generated, which can be used for additional security if securing a custom AI Agent by including it in the request headers of the MCP Client.

3. Connecting Agent

• To communicate with AI Agents, TrustAI utilizes the MCP protocol to offer real-time policy decisions with respect to user identity, agent identity, and a host of conditions.

• See MCP Server for more details.

A common concern with connecting via MCP is the non-determinism in whether a policy is evaluated, as the AI Agent must call the tool. We recommend distributing responsibilities across your agents and orchestrating according to responses from the TrustAI MCP Server. If managing deployment yourself (i.e. LangSmith), LLM guardrails must be implemented by developers.