Intuit was an early pioneer in the usage of agentic AI, but its path to success has hardly been a straight line.
At VB Transform 2026, Intuit VP of AI Nhung Ho described how the company rebuilt its agent architecture twice in the span of about four months, first moving from a fleet of specialist agents to a central orchestration layer, then abandoning that layer for a skills and tools based system once the orchestrator itself started failing under its own complexity. The full second rebuild took 60 days, with a first working version in under 20.
The failure mode that forced the second rewrite was specific. Agents in the orchestrated system passed results to each other in natural language, and each handoff lost context the next agent needed to act correctly.
“If you have 10 agents and they all are passing to each other, every time that pass happens, error compounds,” Ho said.
Why the orchestration layer broke down
Ho said the original push toward specialist agents came from a straightforward customer complaint. A fleet of capable agents is still something a customer has to manage, deciding which agent to use for which task. Intuit’s answer was a system that could take a task and route it internally, without asking the customer to pick an agent themselves.
That orchestration layer held up for about three months, which Ho described only half joking as roughly a year in the compressed timeline of agent development in 2026.
It broke for a structural reason rather than a capacity one. Passing outcomes between agents in natural language meant each downstream agent had to infer how the upstream agent reached its conclusion, and that inference degraded with each additional hop. A ten agent chain did not fail occasionally, it compounded errors by design.
That diagnosis is what sent Intuit back to a skills and tools architecture.
The 60-day rebuild, and what it took to get engineering buy-in
Rebuilding a production agent system in 60 days required more than an architectural decision. Ho said the harder problem was internal, convincing both leadership and the engineers who had built the original agents that scrapping recent work was the right call.
The pitch to leadership relied on evidence rather than argument. Ho’s team built a demo of the new architecture using real customer queries pulled from production, then showed it performing better than the existing system on the same tasks.
“The best proof, at least my belief, is what are customers trying to do? And whatever system you build needs to address those problems,” Ho said.
Winning over engineering required a different case. Hundreds of engineers outside Ho’s core team had built the specialist agents being retired, and the ask was to take their agents apart into individual skills and tools instead.
Ho said the motivating argument was scale. A standalone agent solved one narrow problem, while a shared skill or tool built into the new architecture could serve every customer who touched that part of the product. That shift also changed what partner teams were responsible for day to day, moving their focus from building agents to running evals, since evals became the only way to measure whether the new architecture was actually working.
Bringing a human into the loop, and feedback at a different scale
The clearest customer facing result of the rebuild is a feature that lets a live agent conversation pull in a human — though it’s currently in early testing, live to about 1% of Intuit’s customer base. “We’re going to be scaling it up in the next few weeks,” she said.
Ho said a customer can bring in an Intuit product support person mid conversation, or their own accountant, or one of Intuit’s own bookkeepers, and that person joins with the full context of what the agent has already done.
Ho drew a direct contrast with how most AI chat products handle the same situation. A general purpose assistant answering a tax question typically ends with a disclaimer to consult a professional. Intuit’s system is built to connect the customer to that professional directly, inside the same conversation.
That human handoff sits alongside a permissions model built for financial data specifically. Every action an agent takes on a customer’s financial data requires explicit permission first, though Ho said that requirement can ease over time as customers build trust in the system. Intuit keeps an audit log of everything an agent does that can be reversed if needed.
Feedback in the agentic AI era
The rebuild also changed how Intuit gathers and uses feedback, a shift Ho said is qualitatively different from what came before.
“Feedback in the past used to be very, very sparse, and it was also very bimodal,” Ho said. “Either they loved it or they hated it, and usually it tends towards the negative.”
In a chat based system, every conversation functions as feedback, which Ho said moved the company from roughly 0.3% of customers ever giving explicit feedback to something close to 100%.
Ho said she has returned to writing code herself specifically to build models that analyze that feedback volume systematically, looking for where the system is falling short at a scale no manual review process could keep up with.
That volume comes with a tone most product teams aren’t used to hearing directly. Customers tell the agent exactly where it failed, in plain terms.
“They straight up tell you, ‘You suck. I hate this. This is not right,'” Ho said. “But they’re also willing to give the systems grace and correct it as well, and so the onus is on all of us to harvest this new piece of feedback and type of feedback, and actually improve the system.”
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