Building Agentic Workflows That Query Millions of Rows: A Real-World Guide with AgentKit

Inngest developed a robust multi-agent system using their developer-first framework, AgentKit, to efficiently query and analyze 3.5 million powerlifting records in real-time, addressing challenges like state management and context maintenance. The system allows users to ask natural language questions about powerlifting meets and receive structured results by orchestrating three specialized agents: a Routing Agent to determine database access needs, a Query Agent to convert language into structured queries, and a Summary Agent to interpret results. The agents operate within a carefully structured architecture that leverages a language-to-SQL pattern, using ClickHouse for data storage and precise query handling, with the agents defined and tested in isolation before integration. This approach provides clear separation between intent parsing and data summarization, facilitating accurate and deterministic data access while allowing for fully inspectable logic. AgentKit's capabilities enable the orchestration of these agents as functions within a single network, demonstrating significant improvements in control, accuracy, and efficiency in handling large structured datasets.

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