Agent Testing Platform FAQs

Below are a few commonly asked questions about our Agent Testing Platform:

1. Does the Agent Testing Platform provide AI-enabled translation or multilingual analysis capabilities for testing?

Yes. It supports AI-enabled multilingual requirement analysis, allowing teams to extract and analyze requirements from documents written in multiple languages. It generates test scenarios that account for multilingual support requirements defined in agent specifications and supports testing AI agents that handle multiple languages across interactions.

Key capabilities include:

• Multilingual requirement extraction and analysis from documents.

• AI-driven test scenario generation aligned with multilingual support requirements.

• Support for analyzing AI agents designed to operate across multiple languages.

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2. Is model governance for GenAI systems supported?

Yes, model governance through controlled workflows, version tracking, access controls, audit trails, and policy-aligned oversight across GenAI model lifecycles is supported.

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3. Does the platform support NLP, unstructured data processing, and metadata management for GenAI engineering?

Yes. The platform provides comprehensive NLP, unstructured data processing, and metadata management capabilities to support GenAI engineering workflows. It enables multi-modal document processing, advanced requirement extraction, natural language understanding, and metadata traceability across diverse data formats using multiple LLMs.

Key capabilities include:

• Multi-modal document processing for text (PDF, DOCX, XLSX, TXT, MD), images (with OCR), audio (with speech-to-text), and video (with frame extraction and transcription).

• Structured requirement extraction from unstructured data, including entity extraction, business rules, integration points, and security requirements.

• NLP capabilities such as natural language understanding, conversational AI testing, intent recognition, context memory testing, and multi-turn reasoning.

• Metadata management with document and file metadata extraction, requirement relationship mapping, source requirement ID traceability, and cross-document linkage.

• Unstructured data processing including OCR for images, speech-to-text for audio, video frame analysis, and semantic understanding across formats.

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4. How many data source connectors does the platform offer out of the box, and does it support third-party integrations for GenAI engineering?

The platform provides three native out-of-the-box (OOTB) data source connectors and supports additional integrations via third-party services and secure tunnel infrastructure. This enables secure access to both public and private data sources used in GenAI engineering workflows.

Native OOTB connectors include:

• GitHub for repository content extraction, README retrieval, and file access, with support for public and private repositories via secure tunnels.

• JIRA for ticket retrieval, project information access, and issue tracking integration, including private JIRA instances via secure tunnels.

• Confluence for page content import, documentation retrieval, and knowledge base integration, with support for private Confluence instances via secure tunnels.

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