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How a Fortune 500 manufacturer enabled production-ready deep research agents
As AI agents move from experimentation to enterprise deployment, the real opportunity lies in scaling them with reliability, observability, and governance. A Fortune 500 global manufacturer operating more than 100 plants worldwide recognized this early and set out to turn decades of distributed knowledge into actionable intelligence with an enterprise deep research agent.
The company’s knowledge base spanned manufacturing, logistics, and supply chain domains but was scattered across systems, making it difficult for teams to find accurate insights in real time. They needed a way to connect this institutional knowledge and deliver trusted, cited answers instantly through an enterprise deep research agent.
See how enterprise deep research agents deliver trustworthy insights in seconds
Enterprise deep research agent platform foundations
Working with Grid Dynamics, the manufacturer built an agentic AI platform powered by Temporal, enabling durable orchestration, state management, and full lifecycle observability. The architecture introduced reusable agent templates, drag‑and‑drop workflow builders, and declarative retry logic so teams could focus on business logic, not infrastructure complexity.
Temporal’s enterprise-grade durable execution ensures every workflow step, from data retrieval to analysis, runs reliably and recovers seamlessly after interruptions. The platform’s Kubernetes‑based scalability allows new agents to spin up on demand, supporting long‑running, multi‑agent workloads with ease, a critical capability for enterprise AI adoption.
The results speak for themselves:
- 5,000 daily users, scaling toward 50,000+
- 90% faster access to enterprise intelligence
- 30–50% reduction in manual agent management effort
By deploying production‑safe enterprise deep research agents, the manufacturer unlocked reliable, governed, and scalable AI workflows, bringing precision, speed, and trust to every decision.
Explore the full case study to learn how your organization can operationalize Temporal‑powered deep research agents at scale.
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