From prototype to production-ready agentic AI solution: A use case from Grid Dynamics

Grid Dynamics embarked on a challenging journey to build a production-ready deep research AI agent for a Fortune 500 client using LangGraph, but encountered numerous obstacles, prompting a shift to Temporal for enhanced scalability and efficiency. The initial LangGraph-based architecture faced hidden costs related to development, support, and state management, leading to issues with error handling and resource expenses. The migration to Temporal revolutionized state management, allowing it to become an integral part of the workflow rather than a separate object, which streamlined the process and eliminated the need for custom retry logic. This transition also simplified scaling, as Temporal's architecture enabled effortless horizontal scalability and decoupled the monolithic system into self-contained activities. By leveraging Temporal, the team shifted focus from managing low-level system operations to concentrating on core business logic, ultimately creating a more robust and efficient solution. The experience highlighted the importance of understanding production requirements, scalability, and orchestration in developing stable, production-ready AI applications.

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