What is Agent BizOps?
Agent BizOps is a discipline that drives AI adoption based on an enterprise digital twin. According to McKinsey, 62 percent of organizations are in the early stages of AI adoption. This challenge is made all the more difficult due to the absence of a common language across the enterprise. This is where an enterprise digital twin is important. We recently published V0.5.1 of the Agent BizOps specification on GitHub.
What is an Enterprise Digital Twin?
An Enterprise Digital Twin provides a common intelligence metadata layer across multiple dimensions in the enterprise (see image above).
Health Care Example: Urology CPT Code Real-Time Suggestion Agent
Recently, we conducted an AI Agent Adoption Session with a small health system operating a network of urology ambulatory surgical centers. We used Tavro to rapidly prototype an AI use case to solve one of their persistent problems: Urology-specific CPT and ICD-10 coding errors. This write-up only uses sanitized, publicly-available information.
Within a few minutes, we used Tavro’s Agent BizOps Platform to auto-generate the AI Use Case, Agent, and Agent Risk Assessment — including tooling and data source configurations.
- Created an Enterprise Digital Twin
We created an Enterprise Digital Twin for the company that included its profile, strategy, organization, financials, risks, applications, and processes.
- Tavro Spark Generates AI Ideas Based on the Enterprise Digital Twin
Tavro Spark generates AI ideas based on the Enterprise Digital Twin, and we selected Urological CPT Code Real-Time Suggestion.
- Auto-Generated AI Use Case in Tavro Grounded in Company Financials
Tavro auto-generated an AI use case including a business case grounded in company financials.
- 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 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 Protected Health Information.
- 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 five outputs in Tavro’s Playground.
Tavro auto-generated synthetic data with relevant attributes:
Procedure, Encounter Summary, Code Suggestion with description, confidence interval, and evidence
- Viewed Agent Observability Metrics in Microsoft Foundry
We viewed the Agent Observability metrics including agent runs and token metrics in Microsoft Foundry.
