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).
Dairy Industry Example: Constraint-Based Production Schedule Optimisation via SAP S/4 HANA
Recently, we conducted an AI Agent Adoption Session for a dairy company. We used Tavro to rapidly prototype an AI use case to solve one of the most persistent problems in the dairy industry: production scheduling that cannot keep pace with the volatility of raw milk intake, multi-SKU bill of materials complexity, and real-time customer order commitments. This write-up only uses synthetic data.
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 for the Dairy Company
We created an Enterprise Digital Twin for the company that included 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 Constraint-Based Production Schedule Optimisation via SAP S/4 HANA.
- Auto-Generated AI Use Case in Tavro Including Business Case Grounded in Company Financials
Tavro auto-generated an AI use case including a draft business case that was grounded in company financials.
- Automatically Prototyped the AI Agent in Tavro Grounded in the Enterprise Digital Twin
Tavro auto-generated an underlying agent including an initial set of instructions and mappings to applications and data sources based on the Enterprise Digital Twin.
- 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 Medium Risk based on an AIVSS Score of 4.3.
- 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:
Planning date, Raw milk volumes (L), Waikato, Canterbury, Taranaki, Open sales orders, Skim Milk Powder, Whole Milk Powder, UHT Milk 1L, Butter, BOM / product routes
- Viewed Agent Observability Metrics in Microsoft Foundry
We viewed the Agent Observability metrics including agent runs and token metrics in Microsoft Foundry.