Synthesized has announced the development of its new Test Data Agent, an agentic infrastructure capability designed to create and provision realistic data, business context, and system states for validating AI agents. The goal is to ensure AI agents can safely and reliably complete real business processes in enterprise environments before being deployed into production.
The Test Data Agent integrates with existing agent development, evaluation, testing, and orchestration frameworks. It aims to provide production-faithful environments that go beyond simple model evaluations or demonstration datasets, which often fail to account for the complexities of real-world data, permissions, application states, business rules, and cross-system dependencies.
According to Synthesized, enterprises often build AI agents faster than they can prove their readiness for mission-critical workflows. An agent that performs well in controlled tests might fail when encountering missing records, unusual transactions, conflicting instructions, access restrictions, or complex dependencies across multiple systems. Recreating these conditions is challenging due to stale or incomplete test environments and privacy, security, and regulatory concerns that limit the use of sensitive production data.
The Test Data Agent addresses this by identifying and provisioning the necessary data, relationships, and system states for a given business scenario or testing objective. It can generate, mask, or subset production-representative data while preserving referential integrity, statistical characteristics, and business rules across interconnected systems. This allows teams to create repeatable scenarios, including happy-path, exception, failure, and adversarial conditions, and refresh validation environments as applications and enterprise data evolve.
The system is designed to operate within an enterprise's existing security controls in on-premises, private-cloud, and hybrid environments. It can be triggered through REST APIs and CI/CD pipelines, allowing downstream testing and evaluation frameworks to call it. The Test Data Agent complements existing agent platforms and testing systems by providing the underlying data and environment for rigorous validation, rather than merely scoring an agent's output.
Synthesized emphasizes that the Test Data Agent facilitates a continuous agent-improvement loop beyond one-time pre-production testing. Use cases include pre-production validation, agent regression testing, continuous optimization through curated datasets, model and framework comparison, and release governance to ensure agents meet defined business, security, and operational conditions.
A key focus for the Test Data Agent is purpose-built support for complex SAP environments, including critical finance, procurement, supply-chain, and operational workflows, as well as SAP ECC-to-S/4HANA transformation and testing programs. In SAP environments, a single business process can involve multiple related tables, organization-specific configurations, authorization rules, and integrations with other enterprise applications. The Test Data Agent ensures that relevant document chains, process states, and cross-system dependencies remain consistent for testing.
Specific SAP-focused use cases include pre-production validation of AI agents in SAP environments, SAP ECC to SAP S/4HANA migration validation, regression and business-process testing, application modernization and release assurance, privacy-safe SAP development and testing environments, and testing across SAP and connected databases or enterprise applications. This also extends to continuous validation as agent models, instructions, and tools change.
