Agentin AI Is Automating the Boring Enterprise Workflows Nobody Wants to Touch

The Macro: Enterprise Operations Run on Copy-Paste Between Systems

Every large company runs multiple enterprise software systems. Salesforce for sales. NetSuite or SAP for ERP. ServiceNow for IT. Workday for HR. These systems do not talk to each other well. They were not designed to. Each one was built to be the center of its own universe, and the result is that someone, usually several someones, spends their entire workday copying data from one system to another.

This is not an exaggeration. There are people at Fortune 500 companies whose full-time job is to take a signed sales agreement from Salesforce, manually enter the order details into SAP, verify the line items match, update the fulfillment status when the product ships, and reconcile the payment when it arrives. This process is called Quote-to-Cash, and it is one of the most tedious workflows in enterprise operations. The other big one is Procure-to-Pay: creating purchase orders, matching them to invoices, flagging discrepancies, and processing payments. Both are essential. Both are mind-numbing.

The integration market has tried to solve this for years. MuleSoft (now Salesforce-owned) and Boomi (now private equity-owned) build middleware that connects systems through APIs. Workato and Tray.io offer workflow automation for simpler use cases. UiPath and Automation Anywhere built robotic process automation (RPA) that literally clicks buttons on screens the way a human would. Each approach has limitations. Middleware requires expensive integration engineers. Workflow automation breaks when the underlying systems change. RPA is brittle and fails whenever a UI updates.

The new bet is that AI agents can do this differently. Instead of hard-coded integrations or screen-scraping bots, you train an agent to understand the business process, connect to systems through standard APIs, and handle exceptions intelligently. The key word is exceptions. The easy cases were already automatable. What kills productivity is the 20 percent of transactions that don’t match, don’t reconcile, or have missing data.

The Micro: A Google Research Scientist Takes On the Data Plumbing Problem

Agentin AI builds agents that move data and take actions across Salesforce, NetSuite, SAP, ServiceNow, and Workday. The product covers two core workflows right now: Quote-to-Cash and Procure-to-Pay (currently in beta). The agents connect through standard APIs without requiring configuration changes to the underlying systems. That matters because enterprise IT teams are famously protective of their production environments.

One design choice stands out. Agentin’s agents propose actions with explanations rather than executing autonomously. This is a smart trust-building mechanism for enterprise buyers who are not going to let an AI bot make changes to their SAP instance without human approval, at least not initially. The agents explain what they want to do and why, and a human approves or rejects. Over time, as trust builds, more actions can be automated.

Early pilot results are strong: Quote-to-Cash cycle time reduced from 45 to 18 days, order error rates dropped from 12 percent to 0.5 percent, and 83 percent of manual touchpoints eliminated. Those are the kind of numbers that make a CFO pay attention.

Sankeerth Rao Karingula is the founder. He’s an ex-Google Research Scientist with a PhD in Machine Learning from UC San Diego and an undergraduate degree in Electrical Engineering from IIT Bombay. The reinforcement learning angle is central to the pitch: the agents learn from failures and adapt over time. This is not just a wrapper around an LLM with some API calls. Reinforcement learning means the system gets better at handling exceptions the more it encounters them, which is exactly what you want for enterprise workflows where edge cases are the whole problem.

He’s running a two-person team out of San Francisco, part of YC’s Winter 2025 batch. The competitive space includes MuleSoft for integration, UiPath and Automation Anywhere for RPA, and a growing list of AI agent startups like Moveworks, Adept, and Orby AI. Agentin’s focus on specific financial workflows rather than general-purpose automation is a deliberate narrowing of scope that should make the product better at the things it actually does.

The Verdict

I think Agentin is going after the right problem with the right technical approach. Enterprise data plumbing is unglamorous work that costs companies millions of dollars a year in labor and errors. The reinforcement learning component is a genuine differentiator. Most AI agent startups are using LLMs for reasoning and then hard-coding the action layer. If Agentin’s agents actually improve from failure, that compounds into a meaningful moat over time.

The risk is enterprise sales cycles. Selling to companies that run SAP and NetSuite means selling to procurement departments, IT security reviews, and legal teams. A two-person startup is going to face long cycles and heavy compliance requirements. The “propose, don’t execute” design helps with trust, but it also means the product requires human oversight, which limits the labor savings pitch until customers are comfortable removing the guardrails.

At 30 days, I’d watch for pilot expansion. Are the early customers giving Agentin access to more workflows, or keeping it contained to a single process? At 60 days, the key question is whether the reinforcement learning is producing measurable improvement. Can they show that error rates decline over time without human intervention? At 90 days, the question is deal velocity. Enterprise AI has a pattern where pilots convert slowly or not at all. If Agentin can close pilot-to-paid conversions in under 90 days, that is a signal that the product is delivering undeniable value.