Agentic artificial intelligence (AI), a class of autonomous systems empowered by advanced large language models (LLMs) and decision-making frameworks, is reshaping the backbones of B2B commerce.
“Agentic AI is showing promise across the entire customer life cycle,” Boost Payment Solutions Chief Operating Officer Illya Shell told PYMNTS during a conversation for the June “What’s Next in Payments Series: Secret Agents.”
For most companies, the customer life cycle is dotted with inefficiencies such as lost leads, delayed follow-ups, and mismatched priorities. Shell sees that as fertile ground for agentic AI.
“From business development to client management and straight through to operations, these autonomous systems are helping us improve how we touch, understand and serve our customers,” he said. “We’re expecting agentic AI to deliver customer insights, construct account plans, coordinate meetings, and overall help to target and close growth opportunities.”
This is more than automating customer relations. AI agents can synthesize metadata, historical activity and behavioral patterns to proactively enable sales teams, freeing them from the reactive cycles of the past.
At the same time, with cross-border payments surging and operational complexities multiplying, Shell stressed that if AI’s promise lies in its ability to reduce human error and enhance operational scale, then Boost’s own sights are firmly set on payment execution.
Read more: Automated B2B Payments Leave Legacy Processes in the Dust
Redefining Agentic AI for B2B
In B2B payment execution, agentic AI takes on a new role: acting not just as a decision-making assistant, but as an orchestrator of outcomes. These systems can ingest payment requests, validate them against a library of rules and partner requirements, and either execute autonomously or escalate intelligently.
“We’re looking to improve and even maximize the efficiency of our straight-through processing engine,” Shell said, referencing the company’s proprietary Boost Intercept system. “The goal is to reduce touch and get payments out the door effectively, quickly, and 100% accurately.”
But that doesn’t mean oversight disappears.
“We [plan on] using one model to test the results of another, as well as creating the guardrails and checks and balances we need in this payments world to make sure that every payment is accurate and paid out quickly,” Shell said.
Of course, making autonomous payments work at scale doesn’t just require smart models. It requires the digital scaffolding, including layered governance strategies, to support them.
“It’s a multifold strategy,” Shell said, noting that Boost has adopted Amazon Bedrock to access LLMs while also deploying Microsoft Copilot for internal experimentation. “Putting this capability in the hands of more of our employees … is really important for us. You never know where your next big idea is going to come from.”
Supporting this experimentation is a rigorous testing infrastructure, designed to ensure that even when AI gets creative, it doesn’t get reckless.
“We’re in a world of ‘trust but verify’ — and that applies to the agents too,” Shell said. “Sometimes it’s a human in the loop to keep things flowing in the right place or to double-check that the inputs equal the outputs.”
“We’re hyper-vigilant with the models we use and the proprietary data we use to tune them,” he added. “Where our data goes, where our customer data goes — that has to be very tightly guarded.”
After all, when it comes to B2B payments, AI governance looks a lot less like a Silicon Valley experiment and more like a blueprint for modern financial control.
Pragmatic Path to Autonomy
For all the headline buzz around artificial intelligence-driven disruption, Boost’s own vision is grounded. Autonomous agents won’t replace core infrastructure; they’ll refine it. They won’t eliminate people; they’ll elevate them. And they won’t transform everything overnight — but they will move the industry toward a future where machine-scale intelligence becomes inseparable from enterprise operations.
“Payments is a zero-error industry,” Shell said. “Taking calculated risks is OK. But they’ve got to be very thoughtful and meticulous.”
One area of calculated focus is cross-border B2B. Cross-border B2B payments remain notoriously inefficient, weighed down by manual redundancies and time zone handoffs. That’s why Boost has launched its Boost 100XB Cross Border initiative, which is designed to acquire payments domestically and distribute them to over 180 countries.
“Cross-border payments aren’t optimized,” Shell said. “But agentic AI can help us streamline the front end to ensure we know exactly who’s paying whom, and that the payments are geared in the right way to get out the door quickly.”
Shell doesn’t pretend that AI will solve every inefficiency overnight. But it can accelerate payment validation, streamline onboarding and reduce the friction of reconciliation.
“Our proprietary technology already in place is only going to benefit from the agentic AI [embedded in] it,” he said.
