Agent-Based Test Report Generation

Creating technical test reports is extremely time-consuming, as it involves processing massive volumes of test data and documenting it in a structured and compliant way. The high level of precision and product-specific formatting rules make the process error-prone and labor-intensive – especially for less experienced test engineers.

A multi-stage agent system, including a RAG model, processes raw test data and generates a structured draft report in which a test engineer remains part of the loop. Functional Agents create the initial version, while Quality Agents review, refine, and ensure regulatory and formal compliance – all with the human-in-the-loop.

Agents:

🤖 RAG Agent: Uses Retrieval-Augmented Generation to enrich report generation with relevant external content (e.g. standards, prior reports, templates).

🤖 Summary Agent: Summarizes large volumes of raw test data into precise, readable narrative sections.

🤖 Refinement Agent: Enhances structure, readability, and tone of the draft text based on domain-specific language requirements.

🤖 Gap Finder Agent: Detects missing content, regulatory gaps, or inconsistencies and suggests additions or corrections.

🤖 Quality Checker: Verifies language, grammar, formatting, and domain terminology for consistency and compliance with standards.

🤖 Subtitle Checker: Ensures headlines, section labels, and figure/table captions are complete, accurate, and aligned with the required structure.

🤖 Image Optimizer: Processes and improves embedded test images or plots (e.g. labeling, formatting, resolution) to meet documentation standards.

Source: [at] Webinar: Chat with your data

• Saves multiple workdays per report

• Increases report quality, even for less experienced engineers

• Reduces the burden of a repetitive and unpopular task

• Enhances consistency and legal reliability of test documentation