Intuit scrapped its AI agent architecture twice in four months, first moving to a central orchestration layer and then abandoning it when the orchestrator became too complex.
The original system failed because agents passing results to each other in natural language lost critical context at each handoff, causing errors to compound across the chain.
The company's VP of AI described this rapid iteration as the "fast path" to finding a working model—ultimately settling on a skills and tools based system instead.
What happened
Intuit VP of AI Nhung Ho revealed at VB Transform 2026 that the company rebuilt its agentic AI architecture twice within four months. The first redesign replaced specialist agents with a central orchestration layer; the second, completed in 60 days with a working version in under 20, abandoned that layer entirely in favor of a skills and tools based system.
Why it matters
Intuit discovered that passing results between agents in natural language caused context loss at each handoff, compounding errors across the chain—Ho noted that "if you have 10 agents and they all are passing to each other, every time that pass happens, error compounds." This reveals a core challenge in building reliable multi-agent systems: orchestration itself can become a failure point when complexity grows.
What to watch
Intuit's shift to a skills and tools based architecture suggests the company believes a flatter, more direct model of agent coordination will scale better than orchestration. The speed of the rebuild—first working version in under 20 days—indicates how quickly engineering teams can iterate on AI system design when core problems surface.
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Intuit's experience underscores a critical tension in agentic AI: the intuitive appeal of a central orchestrator (a single coordinator managing specialist agents) collapses under the weight of its own coordination overhead. The failure mode Ho described—context loss during natural-language handoffs between agents—is not a coding bug but a structural flaw: every agent in a chain depends on receiving complete, accurate state from its predecessor, yet natural language is lossy and ambiguous by design.
The move to a skills and tools based system suggests Intuit concluded that avoiding the orchestration layer altogether is preferable to patching it. Rather than having a master agent coordinate 10 specialist agents (each introducing a potential error point), the new model likely gives agents direct access to tools and skills without intermediate translation steps. This trades the simplicity of a unified orchestrator for the complexity of each agent being able to call the right tool at the right time—but eliminates the context-loss bottleneck. Ho's framing of two rebuilds in four months as "the fast path" implies the team views rapid experimentation and architecture-level pivots as faster than incremental tuning of a broken model.
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