Features
Simulates AI agent interactions across real workflows
Captures tool calls, errors, friction, and latency
Records agent reasoning behind each decision
Provides actionable recommendations to fix issues
Detects install errors and doc mismatches
Tracks agent discovery and task completion
Logs full transparency on every simulation run
Supports multiple AI agent frameworks
Runs daily agent experience tests automatically
Ranks product visibility among AI agents
Monitors usage friction in agent-driven flows
High-fidelity synthetic data generation via TabularARGN
Agentic data science automation for training/sampling
Natural language AI Assistant executing Python code
Mock data generation for safe experimentation and testing
Simulated data for edge-case and what-if modeling
Multi-table synthesis with referential integrity
Time-series support and data rebalancing
Differential privacy with temperature control
Deployment on Kubernetes or Red Hat OpenShift
Open-source Synthetic Data SDK under Apache v2 license
Data connectors for Databricks, AWS, Snowflake, BigQuery
Real-time data access from production systems (e.g., Databricks)
Star schema and nested sequences support
REST API and Python Client for programmatic access
Conditional simulation and seeded generation
