AIGuruKul

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Building Verifiable AI Systems

This case study explores how two AI agents work together to produce reliable and verifiable results. One AI agent generates a house layout, while a second independently checks it against real zoning regulations and design constraints. The research shows that AI models often struggle to accurately evaluate their own work, even when they appear confident. By separating generation from verification, the system catches mistakes that a single model would overlook. The findings highlight why independent validation is essential for building trustworthy AI systems.

Two AI Agents, One Job: Design a House, Prove It's Legal

What happens when you ask AI to design a house, and then ask another AI to prove it's legal? This study investigates a two-agent approach in which one AI generates a home layout and a second independently checks it against real zoning laws, lot coverage limits, setbacks, protected areas, and room requirements. By repeatedly identifying mistakes and sending feedback, the verification agent helps refine the design until it meets every rule. The results show that independent verification and precise prompts are the key ingredients for producing AI-generated designs that can be trusted in real-world applications.

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AIGuruKul Foundation

BITS Pilani - Pilani Campus, Rajasthan, India

Indian School of Business, Hyderabad, India

vidyarang.ai@gmail.com