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This dawning age of agentic AI requires a total rethink on how we build software. Current enterprise APIs were built for human use; the APIs of the future will be multi-model, native interfaces. 

“We need to build the kind of APIs that will work well with agents, because agents are the ones that are now going to interact with APIs, not humans,” Merrin Kurien, principal engineer and AI platform architect at Intuit, said during the Women in AI breakfast at this year’s VB Transform. 

Kurien had a dynamic discussion on the present and future of AI agents with fellow AI practitioners Mai-Lan Tomsen Bukovec, engineering and product leader for storage and compute services at AWS, and Tiffany To, SVP of product for platform and enterprise at Atlassian.

“I would like to think five years from now, agents will be mainstream,” said Kurien. “A lot of the challenges we face today probably will be overcome with better tooling, if the last two-and-a-half years is any indication. How prepared will you be? It’s dependent on your investments today.” 

How Intuit is getting invoices paid and AWS is supporting faster migration

Intuit has been using agents and seeing “amazing progress,” Kurien reported in the onstage panel, which was moderated by Betsy Peretti, partner for innovation and design at Bain. 

Notably, the financial technology platform company has incorporated automated invoice generation and reminders into its QuickBooks offering, which is popular among small and medium businesses (SMBs). 

“We have seen businesses get paid on an average five days faster, and there’s 10% more likelihood that invoices get paid in full,” said Kurien. 

AWS has also seen success with AWS Transform, an agile infrastructure that migrates .NET, mainframe, and VMware workloads into AWS, said Tomsen Bukovec. 

The traditional migration scenario, as she described it: A customer would go to the application owner and request to, for instance, move their Windows application to a Linux-based application running on AWS. “And guess what they would say? ‘Take a number. You are priority number 42.’” 

But now, enterprises can do the majority of those migrations with AI assistance. “Your generalist teams are able to do way more work on their own, and it reduces the ask to the specialist,” said Tomsen Bukovec. “That is changing migration as an industry.”

Ultimately, how AWS and others evolve will be closely tied with how customers are using AI, she said. She marveled that incredible advancements in AI are “really making us take a new look, a hot take” on how to build applications.

“When we build agentic infrastructures and we incorporate AI into the mission of our businesses, we’re not just taking technology and putting it to work,” said Tomsen Bukovec. “We are actually changing the nature of the workplace, the workforce.”

She added, “We’re seeing this happen right now. We’re seeing this happen at warp speed.”

How Atlassian is learning from experimentation internally and with customers 

Atlassian is taking a thoughtful inside-out approach to AI agents, said To.

For instance, the project management platform has launched an onboarding agent to help new employees access to all the materials they need to get started with their jobs. In the first month of launch, the agent fielded 7,000 requests. Now, it’s just a regular part of the onboarding process, To said. 

Meanwhile, the company’s go-to-market team has numerous interface points with customers, which can make it challenging to gather all the necessary context. Atlassian built a customer agent that pulls all that data together, and To reported that it is one of its most popular agents, used by 80 teams across the company. “I use it quite a bit before I talk to customers,” she acknowledged. 

At Atlassian, there is a strong responsibility to ‘dog food’ — using one’s own products and services — and iteratively experiment to help guide customers as they evolve with AI, To explained. That work can then be translated into what Atlassian ships to customers out of the box. 

“It’s not only going to come from engineering; it’s going to come from across your entire organization,” she said. “So what can you do programmatically to bring the creativity of everyone cross-functionally, to bring ideas together, to design workflows?”

The company recently introduced its ‘Teamwork Collection,’ a curated selection of apps — Jira, Confluence and Loom — managed by ‘rovo agents.’ This is built into its platform and supports various aspects of the collaborative process. For instance, before a meeting, the agent will pull together a “really nice summary” based on Confluence pages and JIRA tickets. 

“So when you go into that meeting, you now have all that shared context,” said To. “You’re not trying to update each other, you can actually spend time on important strategy decisions.” 

Atlassian estimates that that particular use case saves at least four hours per person per week. Customer HarperCollins, in particular, has used it to “great effect,” To noted.

Customers are using AI agents in varying complexities, she said: Sometimes they’re just offloading work, gathering data or writing release notes; other times they’re getting deep into raw data and pre-building strategic roadmaps. 

To explained that Atlassian has built a graph layer on top of its data that provides deeper intelligence on how data is connected. For instance, enterprises can analyze their goals alongside team structuring and projects in progress. “It’s not just an HR org chart,” said To. 

“When you think about how people build their software development lifecycles right now, a huge part of that is creating roadmaps and prioritizing strategies,” she said. “But that can be very dynamic, and taking into account all of that data is hard for humans to do. The agents we’re seeing become really popular now with customers are actually pre-building those strategic roadmaps.”

To emphasized the importance of creating feedback loops with customers, noting that, in just the last three months, Atlassian users have customized 10,000 different versions of the company’s out-of-the-box agents. 

“It’s a really great pool of feedback data that then helps us understand how they’re embedding these agents into their workflows,” said To. “I think part of what is really exciting about this wave is it’s such a collaborative process in designing with customers.”

Earning trust, building it right from the get-go

Trust is the cornerstone of any product and that should be no different with AI, Kurien emphasized. Customers want to know what the agent is doing behind the scenes and have control over its actions. This requires stringent review processes. 

“With new waves came new vulnerabilities,” she said. “We have built a robust process where we are identifying the lifecycle in which an agent fits in and creating the right processes of reviews for that phase.” 

To underscored the fact that it’s more than raw technology; people must collaborate, build complete solutions together and tap into experience. The industry must invest in strong data architecture and have the right data context so that AI agents can make the powerful decisions we’ll be asking of them. 

“Where it becomes really exciting is when it is a superpower in your organization, when it’s able to help you make better decisions, release better products, re-sort your goals, be more competitive as a company,” said To.