Rippling's AI Lessons: Flat Agents and Simple Tools Win in Production

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At LangChain Interrupt 2026 in San Francisco, engineering teams that had shipped AI agents at scale shared a consistent finding: sophisticated architectures failed in production while simpler ones survived. Rippling, which runs AI across its HR, IT, and Finance platform simultaneously, found that flat agent structures outperformed hierarchical sub-agent trees because failures are easier to trace and debug. The company distilled its production experience into four principles: keep agents flat, build generic composable tools, pass code rather than raw data into LLM context, and use SQL interfaces instead of narrow bespoke retrieval tools. These lessons reflect a broader pattern where operability — not architectural elegance — determines what actually holds up under real-world conditions. Teams that reduced complexity before scaling were consistently the ones shipping reliably.

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