Agentic AI and the Role of Boomi Enterprise Platform
As artificial intelligence continues to evolve, agentic AI is transforming the technological landscape by introducing autonomous agents capable of making decisions, initiating actions, and executing complex tasks with minimal human intervention. For agentic AI to operate effectively, it requires access to diverse, high-quality data. The Boomi Enterprise Platform plays a crucial role in supporting this need by facilitating agile master data management and data imputation.
Chris Hallenbeck, senior vice president and general manager for AI and platform at Boomi LP, highlighted the platform’s capabilities during a discussion with theCUBE at Boomi World 2025. “I think one of the great things we have about the Boomi Platform is we can do really lightweight [master data management] using agents to clean,” Hallenbeck explained. “I can do data imputation, create the golden records and bring those to bear in that project in a really agile, lightweight way as I’m building out my agents. Data quality is up 10%, and I can build quickly. I don’t have to wait for these data products on the data mesh that’s promised in two years.”
Agentic AI has the potential to streamline various business processes, such as invoice reconciliation and manual journal entries, by automating repetitive tasks, reducing human error, and providing intelligent decision support. Hallenbeck noted that this level of automation relies on context awareness, transparent reasoning, and clear goal representation. “I’ve seen a lot over time with matching, whether it’s trades, goods receipt, invoice reconciliation or manual journal entries,” he said. “These are things that stop you from closing the books on time. I have to have a number of accountants based on my high watermark. It slows me [down] from closing the books. With an explainable AI, I can actually make those things run really smoothly and auditable.”
However, the effectiveness of agentic AI is heavily dependent on the quality, accessibility, and orchestration of data. Shawn Rogers, chief executive officer of BARC US, emphasized that poor data quality can lead to suboptimal decisions. “AI might actually be the forcing factor required for people to slam on the brakes, go backwards two steps and solve some of their data quality and data access and orchestration issues,” Rogers stated. “If you’re going to let autonomous or agentic AI go running and doing really cool stuff with your customers or with your inventory, whatever, you better have your act together.”
To harness AI effectively and responsibly, organizations must prioritize AI readiness, which is crucial for growth, resilience, and long-term success in today’s digital landscape. Rogers pointed out that despite its importance, AI readiness remains low across many organizations. “We do a lot of research at my firm on the industry, and what we’re seeing is an 80/20 split, 79/21 split,” he said. “Twenty-one percent of the people that are doing AI right now really laid down a very fantastic technology and policy foundation. They’re ready to play. They’re asking the right questions, they’re trying to get in the game. The other folks are trying to figure out what to prioritize.”
The discussion between Hallenbeck and Rogers underscored the importance of robust data management in the era of agentic AI. As organizations continue to adopt AI technologies, ensuring high-quality data and effective data orchestration will be critical to realizing the full potential of agentic AI.
