Test Data Agent: Validate AI Agents with Synthetic Production-Like Data

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Synthesized has unveiled the Test Data Agent, a novel infrastructure tool designed to generate and manage authentic data, business context, and system states for evaluating AI agents in environments that mirror production conditions.

The initiative aligns with Synthesized’s broader objective

The initiative aligns with Synthesized’s broader objective of delivering AI-centric test infrastructure tailored for complex and heavily regulated technological ecosystems. The Test Data Agent specifically addresses the need for robust validation in enterprise AI deployments, with a focus on supporting intricate SAP environments.

Enterprise AI validation challenges

Organizations often develop AI agents faster than they can confirm their readiness for mission-critical workflows. While model evaluations and demonstration datasets can assess whether an agent produces plausible responses, they cannot guarantee correct decision-making when confronted with the complexities of real enterprise environments. These include factors such as missing data, unusual transactions, conflicting instructions, access restrictions, and cross-system dependencies.

Key capabilities

Key capabilities include generating or subsetting production-representative data while maintaining referential integrity, statistical consistency, and business rules across interconnected systems. It also supports the creation of repeatable test cases, including edge scenarios, exceptions, and adversarial conditions, and integrates with CI/CD pipelines for automated provisioning. The tool operates within on-premises, private-cloud, and hybrid environments, adhering to existing security controls.

Nicolai Baldin’s statement

“Evaluation frameworks can measure performance, but they still require a realistic environment to make that performance meaningful,” he stated.

“The Test Data Agent creates that environment safely before agents interact with live systems.”

Transitioning to continuous agent improvement

Transitioning to continuous agent improvement The Test Data Agent shifts focus from one-time pre-production testing to a continuous improvement cycle. Teams can define business outcomes, generate test environments, execute agents, and refine models based on feedback. Use cases include pre-production validation, regression testing, continuous optimization, model comparisons, and release governance.

SAP-specific validation requirements

Testing AI agents in SAP environments presents unique challenges due to the interdependencies between multiple tables, organization-specific configurations, authorization rules, and integrations with other enterprise applications. The Test Data Agent ensures consistency in document chains, process states, and cross-system dependencies, enabling agents to handle restrictive authorizations and complex data relationships.

Open infrastructure for the agent ecosystem

Synthesized positions the Test Data Agent as an open infrastructure component for the broader enterprise agent ecosystem. It allows agent-platform providers, testing companies, and internal teams to integrate the tool into existing development workflows without introducing isolated testing frameworks. The workflow involves defining business scenarios, creating production-faithful environments, executing agents, evaluating outcomes, refining models, and revalidating before deployment.