What happens when you put an LLM on top of a 25-product SaaS company? Ilya Zayats, CTO at Factorial, shares the story of building Factorial ONE — an AI agent designed to help users work across HR, payroll, time, talent, finance, IT, documents, permissions, and many other product surfaces. The first approach looked simple: let the model query the product directly. But the product graph was too large, the GraphQL schema did not fit in context, and the model struggled with product entities, permissions, and stable answers. The talk follows the evolution from “LLM magic” to a production agent harness: semantic tools, skills, smaller schemas, runtime context, deterministic computation, permissions, and browser-based actions. The main lesson: useful AI agents are not built by giving the model the whole world. They are built by designing the right harness — context, tools, workflows, permissions, and runtime — so the agent can safely understand the product, take actions, and create new work across it. Recorded at AI Builders Barcelona. Speaker: Ilya Zayats, CTO at Factorial Topics: AI agents, Factorial ONE, SaaS, LLMs, context engineering, tool use, skills, GraphQL, product automation, AI platform, agent harness, enterprise AI
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