Agent BizOps Agentifies Agent Adoption
Agent adoption is a huge challenge. As AI agents proliferate across the enterprise, many of you have highlighted the need for a common language and operating layer to take agents from use case to deployment and govern them in production. We are provisionally referring to that discipline as “Agent BizOps,” but would love your feedback.
Announcing Tavro’s Open-Source Agent BizOps Platform
Consistent with this approach, we are making two announcements:
- We have extended the Agent Metadata Specification with a heightened focus on Agent BizOps
- We released Tavro’s Open-Source Agent BizOps Platform as a reference implementation for the Agent Metadata Specification
Regional Banking Example: Sub-Prime Mortgage Origination Agent
A few weeks ago, we conducted an AI Agent Adoption Session with a regional bank. We used Tavro to rapidly prototype an AI use case to originate sub-prime customers with less than 650 credit scores.
Within a few minutes, we used Tavro’s AgentBizOps Platform to auto-generate the AI Use Case, Agent, and Agent Risk Assessment including tooling and data source configurations.
- Prompted Tavro to Generate the Agent Scaffolding
We prompted Tavro’s AgentBizOps Platform.
- Auto-Generated AI Use Case in Tavro
Tavro auto-generated an AI use case.
- Automatically Prototyped the AI Agent in Tavro
Tavro auto-generated the agent including an initial set of instructions.
- Prototyped the Agent Tools in Tavro
Tavro automatically prototyped the agent tools.
- Auto-Generated the Context Diagram in Tavro
Here is the Agent Context Diagram in Tavro that links the agent to the associated artifacts:- Technical Context (tools)
- Functional Context (blank for now)
- Business Context (AI Use Case)
- Risk Context (Regulatory Assessment, AI Vulnerability Scoring System Assessment for Cyber)
- Auto-Generated the Initial Agent Risk Assessment in Tavro
We used Tavro’s own AI Risk Assessment Agents that classified the agent as High Risk based on the usage of Personally Identifiable Information and Article 6 of the EU AI Act (Access to Essential Private/Public Services and Benefits).
- Conducted “What-If” Assessment on the Agent
We conducted a “what-if” assessment on the agent using Tavro to look at areas where the risk might be reduced.
- Simulated the Agent to Tavro Playground
We moved the agent to Tavro’s Playground to simulate the agent in Microsoft Azure.
We simulated the data for 10 potential borrowers in Tavro’s Playground.
Tavro auto-generated synthetic data in JSON format with relevant attributes: Borrower ID, Credit Score, Debt-to-Income, Loan-to-Value, Monthly Income, Employment, Delinquencies, Cash Reserves in Months, Rental History, Alternative Credit, Compensating Factors, Decision.
- Viewed the Agent Inventory in Microsoft Foundry
We viewed the subprime-mortgage-underwriting-agent within the broader agent inventory in Microsoft Foundry.
- Viewed Agent Observability Metrics in Microsoft Foundry
We viewed the Agent Observability metrics including agent runs and token metrics in Microsoft Foundry.
