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).
Financial Services Example: Account Takeover Behavioural Biometrics Classification
Recently, we conducted an AI Agent Adoption Session with a financial services organization. This write-up is based on sanitized, publicly-available information. We used Tavro to rapidly prototype an AI use case to solve one of the most persistent problems in the financial services industry: account takeover (ATO) fraud within the digital banking channel, where credential-based attacks and session hijacking were bypassing traditional static authentication controls.
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 Financial Institution
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 Company Blueprint
Tavro Spark generated AI ideas around Fraud Prevention based on the Enterprise Digital Twin. We selected Account Takeover Behavioral Biometrics Classification.
- Auto-Generated AI Use Case in Tavro Grounded in Company Financials
Tavro auto-generated the AI use case that was grounded in the Enterprise Digital Twin and 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 Article 6 of the EU AI Act (Biometrics).
- 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:
Event_type, session_id, user_id_hash, trigger, telemetry_summary, keystroke_cadence_ms_avg, mouse_velocity_px_s_avg, scroll_depth_pct, device_fingerprint_match, geo_velocity_km_h, session_age_sec, model_version, risk_score, threshold, decision, step_up_auth_required, and risk_event_sent.
- Viewed Agent Observability Metrics in Microsoft Foundry
We viewed the Agent Observability metrics including agent runs and token metrics in Microsoft Foundry.