<p>The sophistication of contemporary software ecosystems requires a paradigm change in testing practices that move beyond silos specific to domains. This paper proposes a Lattice-Based Cross-Industry Automation Framework for Software Testing, fueled by AI-based multi-agent systems. The framework is designed with four adaptive layers namely Cross-Industry Requirement Mapping &amp; Scenario Generation, Parallel Multi-Agent Test Execution, Intelligent Defect Detection &amp; Prioritization, and Adaptive Lattice Learning &amp; Analytics. By using natural language processing (NLP) for scenario creation, lattice-based coordination for parallel run and deep learning anomaly detection for defect detection, the framework provides scalable, context-aware, high-coverage testing. The performance is evaluated on simulated finance, healthcare, and manufacturing domains by comparing the proposed system with LSTM, Transformer, and reinforcement learning-based testing systems. Results show a 32% increase in defect detection, 41% fewer false positives, and 2.8× faster running speed while maintaining high cross-industry adaptability. These results confirm the novelty of lattice-based agent coordination and causal adaptation, thus setting the new standards for scalable and smart software testing automation.</p>