Agentic AI Governance Playbook
The Tavro team used the Tavro Agents to ingest the metadata from LangGraph AI Agents into Collibra AI Governance. We also used the Tavro Agents to automatically assign an EU AI Act risk rating for each agent as well. This “playbook” automated the Agentic AI Governance process via integrations and agents.
LangGraph Agents
LangGraph, created by LangChain, is an open source AI agent framework designed to build, deploy, and manage complex generative AI agent workflows.
As shown in the screenshot above, we developed a very simple Tavro Hiring Agent to automate the recruitment process. The agent included very simple instructions focused on Python or Java skills.
The screenshot below shows the agent responses at various nodes in LangGraph Studio. Because the applicant was a Python developer, the agent responded that the candidate was selected for further steps.
LangGraph Agentic Metadata Ingested into Collibra
LangGraph also includes additional configuration parameters such as model name, temperature, description, top_p, and max_tokens.
The Tavro team ingested this metadata into Collibra AI Governance.
Tavro EU AI Act Risk Classification Agent Automatically Scores the LangGraph Agent and Send the Results to Collibra AI Governance
The Tavro EU AI Act Risk Classification Agent received the agentic metadata to automatically score the agent. The screenshot below showcases the Tavro agent UI in Hugging Face (we called this agent via the Hugging Face API).
Based on the agentic metadata, Tavro classified the Tavro Hiring Agent as High Risk based on Article 6 of the EU AI Act – Employment, Workers’ Management and Access to Self-Employment.
This Tavro Agentic AI Governance Playbook combined integrations with agentic auto-classifications to reduce the need for manual AI Governance interventions in Collibra.
