Productionizing Agentic AI for Operational Intelligence in Semiconductor Manufacturing

In semiconductor manufacturing, operational data is abundant, but access to it is not.

Supply chain performance, logistics tracking, and SKU-level inventory insights are distributed across complex, interconnected systems that require deep domain expertise to navigate. Business leaders needed fast, reliable answers to questions like: ‍

• ‍"What is the current supply chain status for SKU 123?" ‍
• "Is the shipment from China for SKU 456 on track?" ‍
• "Where are we at risk of delays or inventory shortfalls?"

These answers existed in the data. Getting to them required analyst intervention, manual querying, and significant institutional knowledge to interpret.

Existing BI tools and dashboards weren't enough. Users needed to know where data lived and how to query it. Multi-step business questions were difficult to execute. Reporting was fragmented across teams and systems, with no unified way to surface insight on demand.

The organization didn't lack data. It lacked a way to use it.

Aimpoint Digital designed and deployed a production-grade multi-agent AI architecture on Databricks, built to make complex operational intelligence accessible to every user, regardless of technical background.

At the center of the system is an orchestrator agent that interprets user intent, surfaces any missing context through natural conversation, and routes queries to purpose-built sub-agents embedded within Databricks Genie spaces. Each sub-agent handles domain-specific structured data analysis, spanning supply chain, logistics, and business operations. The orchestrator also reformats user queries into Genie-optimized prompts, significantly improving accuracy and consistency downstream. Together, the system was designed to address approximately 85 common business analysis scenarios with high accuracy.

Production readiness, not just functionality, was the standard. Aimpoint built a rigorous evaluation and optimization framework from the ground up, developing ground truth datasets through direct stakeholder collaboration, applying DSPy to systematically tune prompts and reasoning strategies across agent nodes, and leveraging MLflow to deliver full observability into agent decision paths, query routing, and response quality over time. The result is a system designed for continuous improvement and built to earn enterprise trust.

To support over 1,500 concurrent users, the infrastructure was engineered for scale: queue management systems to handle demand, real-time latency monitoring dashboards, scalable Flask-based backend services, and a React TypeScript conversational interface, ensuring a responsive, reliable experience even under peak load.