Automated Hardware Validation Test Plan Generation for Large Scale AI Datacenter Platforms Using a Generative AI Multi-Agents Architecture

2026-07-17

AI Summary

This work addresses the inefficiencies of manual test planning in large-scale AI data center hardware verification, which suffers from low productivity, insufficient coverage, and poor reusability. The paper proposes the first hardware verification framework that integrates generative AI with multi-agent collaboration to automatically construct structured test plans from self-healing verification documents and bills of materials. The framework enables automated test case generation, intelligent coverage gap closure, and end-to-end traceability. Key technical innovations include input normalization, context-aware component classification, and fault mode synthesis, ensuring high portability across platforms. Experimental results on two production platforms demonstrate coverage improvements of 74.2% and 51.4%, respectively, reducing test plan development time from days to hours while achieving high expert acceptance in novel scenarios.

Abstract

Large-scale AI datacenter platforms comprise thousands of heterogeneous hardware components whose validation requires comprehensive fault injection test plans. Today these plans are authored manually: engineers review hardware self-healing validation documents and bills of materials, enumerate failure modes per field-replaceable unit, and produce flat lists of single-layer test cases. This process is labor-intensive, error-prone, and dependent on institutional knowledge; coverage gaps surface late, traceability to source specifications is implicit, and the effort is largely repeated per platform. This paper presents a generative AI multi-agent architecture that automates the generation of structured hardware validation test plans from two canonical inputs: self-healing validation documents, which enumerate known failure modes and their detection and remediation behaviors per field-replaceable unit, and component Bills of Material. An ingestion agent normalizes heterogeneous inputs into a canonical representation; a classification agent maps components to functional domains via contextual reasoning over part descriptions and sub-category hierarchies; and a generation agent synthesizes test cases by combining normalized failure modes with domain-classified data, filling gaps and producing edge cases. The output conforms to a standardized schema for direct import into internal validation software. Evaluated on two production platforms against manual baselines, the framework achieves coverage expansions of 74.2% and 51.4%, cutting authoring from days to hours. It yields fully traceable mappings from each test case to its source specification, and its multi-agent decomposition is portable across platform generations. Automated and expert evaluations confirm 100% extraction fidelity and high acceptance of new scenarios, validating the framework as a robust human-in-the-loop force multiplier.

Problem

• hardware validation
• test plan generation
• large-scale AI datacenter
• fault injection
• coverage gaps

Innovation

• Generative AI
• Multi-Agent Architecture
• Hardware Validation
• Test Plan Generation
• Fault Injection