Saad Ullah
⬤ Coinbase took its AI game from zero to full production in just six weeks. The company's Enterprise AI Tiger Team built a standardized framework that lets internal teams create and deploy AI agents using the same solid processes they use for regular code. The setup routes AI calls through an internal gateway, hosts agents on AWS, and connects with client apps like CRM systems.
⬤ Development speed has skyrocketed—what used to take 12 weeks now takes less than a week. Two agents are already running live, saving over 25 hours weekly, with four more completed and additional teams jumping on board. The company went all-in on code-first approaches using LangGraph and LangChain rather than low-code tools, favoring typed interfaces and testable components that scale better for their needs.
⬤ Tracking and accountability were baked into the foundation from day one. LangSmith traces every tool call and decision, creating permanent records of data usage, reasoning paths, and approvals. The system connects logging infrastructure, internal knowledge bases, control systems, and external data sources through secure orchestration layers.
⬤ Coinbase is treating AI agents like serious engineering work, not just experiments. Faster build times, standardized workflows, and complete visibility are driving wider AI adoption across operations. Their progress shows how financial and tech companies are hungry for dependable, transparent agent systems as they scale up AI automation.
Saad Ullah
