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Intuit has been on a journey over the last several years with generative AI, incorporating the technology as part of its services at QuickBooks, Credit Karma,Turbotax and Mailchimp.
Today the company is taking the next step with a series of AI agents that go beyond that to transform how small and mid-market businesses operate. These new agents work as a virtual team that automates workflows and provides real-time business insights. They include capabilities for payments, accounts and finance that will directly impact business operations. According to Intuit, customers save up to 12 hours per month and, on average, will get paid up to five days faster thanks to the new agents.
“If you look at the trajectory of our AI experiences at Intuit in the early years, AI was built into the background, and with Intuit Assist, you saw a shift to provide information back to the customer,” Ashok Srivastava, chief AI and data officer at Intuit, told VentureBeat. “Now what you’re seeing is a complete redesign. The agents are actually doing work on behalf of the customer, with their permission.”
Technical architecture: From starter kit to production agents
Intuit has been working on the path from assistants to agentic AI for some time.
In September 2024, the company detailed its plans to use AI to automate complex tasks. It’s an approach built firmly on the company’s generative AI operating system (GenOS) platform, the foundation of its AI efforts.
Earlier this month, Intuit announced a series of efforts that further extend its capabilities. The company has developed its own prompt optimization service that will optimize queries for any large language model (LLM). It has also developed what it calls an intelligent data cognition layer for enterprise data that can understand different data sources required for enterprise workflows.
Going a step further, Intuit developed an agent starter kit that builds on the company’s technical foundation to enable agentic AI development.
The agent portfolio: From cash flow to customer management
With the technical foundation in place, including agent starter kits, Intuit has built out a series of new agents that help business owners get things done.
Intuit’s agent suite demonstrates the technical sophistication required to move from predictive AI to autonomous workflow execution. Each agent coordinates prediction, natural language processing (NLP) and autonomous decision-making within complete business processes. They include:
Payments agent: Autonomously optimizes cash flow by predicting late payments, generating invoices and executing follow-up sequences.
Accounting agent: Represents Intuit’s evolution from rules-based systems to autonomous bookkeeping. The agent now autonomously handles transaction categorization, reconciliation and workflow completion, delivering cleaner and more accurate books.
Finance agent: Automates strategic analysis traditionally requiring dedicated business intelligence (BI) tools and human analysts. Provides key performance indicator (KPI) analysis, scenario planning and forecasting based on how the company is doing against peer benchmarks while autonomously generating growth recommendations.
Intuit is also building out customer hub agents that will help with customer acquisition tasks. Payroll processing as well as project management efforts are also part of the future release plans.
Beyond conversational UI: Task-oriented agent design
The new agents mark an evolution in how AI is presented to users.
Intuit’s interface redesign reveals important user experience principles for enterprise agent deployment. Rather than bolting AI capabilities onto existing software, the company fundamentally restructured the QuickBooks user experience for AI.
“The user interface now is really oriented around the business tasks that need to be done,” Srivastava explained. “It allows for real time insights and recommendations to come to the user directly.”
This task-centric approach contrasts with the chat-based interfaces dominating current enterprise AI tools. Instead of requiring users to learn prompting strategies or navigate conversational flows, the agents operate within existing business workflows. The system includes what Intuit calls a “business feed” that contextually surfaces agent actions and recommendations.
Trust and verification: The closed-loop challenge
One of the most technically significant aspects of Intuit’s implementation addresses a critical challenge in autonomous agent deployment: Verification and trust. Enterprise AI teams often struggle with the black box problem — how do you ensure AI agents are performing correctly when they operate autonomously?
“In order to build trust with artificial intelligence systems, we need to provide proof points back to the customer that what they think is happening is actually happening,” Srivastava emphasized. “That closed loop is very, very important.”
Intuit’s solution involves building verification capabilities directly into GenOS, allowing the system to provide evidence of agent actions and outcomes. For the payments agent, this means showing users that invoices were sent, tracking delivery and demonstrating the improvement in payment cycles that results from the agent’s actions.
This verification approach offers a template for enterprise teams deploying autonomous agents in high-stakes business processes. Rather than asking users to trust AI outputs, the system provides auditable trails and measurable outcomes.
What this means for enterprises looking to get into agentic AI
Intuit’s evolution offers a concrete roadmap for enterprise teams planning autonomous AI implementations:
Focus on workflow completion, not conversation: Target specific business processes for end-to-end automation rather than building general-purpose chat interfaces.
Build agent orchestration infrastructure: Invest in platforms that coordinate prediction, language processing and autonomous execution within unified workflows, not isolated AI tools.
Design verification systems upfront: Include comprehensive audit trails, outcome tracking and user notifications as core capabilities rather than afterthoughts.
Map workflows before building technology: Use customer advisory programs to define agent capabilities based on actual operational challenges.
Plan for interface redesign: Optimize UX for agent-driven workflows rather than traditional software navigation patterns.
“As large language models become commoditized, the experiences that are built upon them become much more important,” Srivastava said.
