Building Reliable AI Systems: Lessons from the Retail Resilience Engine
Key insights and methodologies from developing the first comprehensive Agentic AI framework for retail
Building the Retail Resilience Engine was a journey that taught us valuable lessons about deploying AI systems at enterprise scale...
The Challenge
Traditional retail operations rely heavily on human expertise and decision-making. Our goal was to augment, not replace, this expertise with AI systems that could operate at scale.
Our Approach
We developed a multi-agent system using LangChain and OpenAI, with each agent specializing in different aspects of retail operations...
Results
The system achieved 97.5% alignment with human experts while processing requests 10x faster than traditional methods.
About the Author
Lalit Narayan Mishra is a Sr. Manager, Software Engineering and IEEE published researcher with 18+ years of experience in enterprise software engineering. He currently serves at Lowe's, leading initiatives in Agentic AI and modern software architecture.
Learn more about Lalit →