The power of many: Why multi agent AI systems are essential for AR optimization

This post was originally published in October 2024 and was updated in August 2025 with additional information on multi agent AI systems.

“Imagine a team of virtual experts tailored to your business working tirelessly behind the scenes to optimize your entire AR process. It’s something that could be here sooner than later,” says Ahsan Shah, SVP, AI & Analytics, responsible for driving the AI evolution at Billtrust. He believes in the transformative potential of multi-agent AI, a cutting-edge approach that goes beyond traditional AI by employing multiple intelligent agents, each specialized in distinct tasks, to collaborate towards shared objectives.

This article explores how multi-agent AI systems have the potential to revolutionize AR by optimizing the AR lifecycle and enhancing strategic decision-making. Ahsan Shah offers an insider’s perspective and forecasts future trends and innovations poised to transform B2B SaaS accounts receivable software from 2025 onward.

Before we explore the magic of multi-agent systems, let’s clarify what an AI agent is. An AI agent is a sophisticated software program designed to act autonomously, making decisions and performing actions to achieve a specific goal. These agents can interact with their environment, learn from data, and adapt to changing circumstances. Think of them as highly intelligent assistants capable of handling complex tasks without constant human intervention.

Read the blog → Transforming accounts receivable with AI: Meet Billtrust’s Autopilot

Unlike single-agent systems, where one AI handles all tasks, multi-agent systems distribute tasks among multiple, specialized agents, each with its own specific task and expertise. Together, they form a cohesive system adept at tackling intricate challenges. By interacting with each other and their environment, agents collaborate to solve complex problems more efficiently than a single agent could alone.

This collaborative approach boosts efficiency and effectiveness, proving especially beneficial in managing the complex processes of AR. Like human organizations — such as your own finance team — multi-agent systems collaborate to achieve shared goals.

“These systems will revolutionize various business processes, including accounts receivable,” says Ahsan Shah. Specialized AR agents are AI entities designed to manage specific aspects of the AR lifecycle, such as collections, credit management, payments, and invoicing. Each agent is equipped with domain-specific knowledge and capabilities, enabling it to perform its tasks with high precision and efficiency.

Multi-agent AI systems are most powerful when specialized agents collaborate. These agents communicate and share information, allowing them to coordinate their efforts and optimize the AR lifecycle. For example, a collections agent might work with a credit agent to identify high-risk accounts and develop strategies to mitigate potential losses.

This type of AI goes beyond simple question-and-answer interactions. “Instead, agents can reason, plan, and execute tasks autonomously, like how a team of experts would work together in a company”  says Ahsan Shah. “Essentially, we’re teaching AI to work collaboratively. By giving it tools, support, and context, and setting objectives, it can reason and plan using a chain of thought.”

AI as an advisor

Ahsan Shah: “This represents a substantial leap for AI in B2B AR software. What we’re seeing is the transformation from an AI agent, with a big human presence with premeditated questions, to AI as an advisor.”

“It enables AR professionals across all levels to focus on strategic goals, with multi-agent AI offering valuable insights, guidance, and working as a collaborative partner in the future finance organization. This will enable AR teams to significantly reduce human capital and allow for more sophisticated analyses.”

“At Billtrust, we’re currently creating this set of graphs with specialized agents, which will be given various tools, data access layers, and domain specific knowledge to enable organizational collaboration between Al artifacts. We’re starting with payments now, but are extending it across AR. Other scenarios could be a collections procedure optimizer or real time AI search.”

Ahsan Shah, SVP, AI & Analytics, Billtrust

Continuous improvement

What’s unique to this multi-agent approach is that this intricate network works dynamically and cyclically, continuously passing information and insights back and forth. The system runs perpetually in the background, like a dedicated team that allows your actual team to focus on more strategic growth objectives.

This optimization ensures that AR processes remain efficient and effective, adapting seamlessly to changing conditions and new information. “What’s great about it is that it can be hyper personalized to the specific needs of customers, industries and buyer behavior patterns,” says Shah.

The human element

The setup mirrors the current structure of an AR organization, much like a collections or credit manager interacting with payment policies, or a terms manager assessing credit risk.

This human element is important, articulates Shah. “The final action can be configured so that human workers can simply approve to apply the recommended configurations. Our philosophy on AI, particularly Gen AI, views it as a creative augmentation, rather than a replacement for human involvement.”

“What excites us about this ability to use agents, nodes, and graphs is that it allows us to take a higher-level business objective and have a system execute the objective in a non-deterministic way.”

Ahsan Shah, SVP, AI & Analytics, Billtrust

Read the industry report → AI pushing the boundaries of what’s possible for OTC

Multi-agent AI empowers you to go beyond simple questions, supporting you in achieving advanced business goals. As a supplier, navigating a vast array of buyers can be daunting, especially when it comes to analyzing data sets and personalizing buyer behavior.

Imagine wanting to identify the most relevant opportunities, assess risks, and provide recommendations based on the past three months of payment activity among your buyers. “For starters, this is a unique question,” says Shah, “since we haven’t defined what risk or opportunity means, nor do we know what a recommendation could be. And this isn’t just a question —it’s more of a higher-level strategic business objective.”

“With the payment policy optimizer we’re currently testing at Billtrust, we’re leveraging a graph to identify both opportunities, such as surcharging and payment grace periods, and risks, like low-margin customers and late payment penalties.”

The multi-agent AI system gathers initial data sets, examines buyer information, analyzes trends, detects anomalies, and identifies opportunities. It scrutinizes payment methods, terms, and Days-to-Pay for each buyer. By consolidating this information, it can reveal specific buyers who might benefit from switching to ACH.

Additionally, it can categorize higher-risk buyers where penalties may be necessary, providing suppliers with a hedge against low-margin transactions. Conversely, there are top-performing buyers—those who consistently pay well within their terms. These are opportunities to enhance relationships by offering them better terms, extending credit, or providing early payment discounts as a reward for their reliability.

Billtrust Collections Agentic Procedures uses behavioral data to recommend optimal outreach strategies. Learn more about the latest Billtrust features.

A noteworthy feature is its ability to generate visualizations that can be easily integrated into PowerPoint or other presentations, thereby accelerating decision-making and action for customers.

The AI agent proceeds by presenting an executive summary, whether in an email or a document uploaded to the application. This summary encapsulates key findings, sources, outliers, early payers, risks, and recommendations. It delivers a tactical overview that results from thorough research across accounts receivable.

Ahsan Shah states, “The design of this interface within your software is still under discussion. However, it certainly won’t resemble the Q&A interface you’re familiar with in Billtrust Autopilot. We envision it as a back-end process operating systematically. Imagine arriving in the morning to a list of tasks for optimizing your payment policy, all generated while you sleep. We are deliberating whether this should be presented in-app or sent via email.”

Why multi-agent AI systems represent the next step in AR automation

Finance leaders know that traditional rule-based automation only scratches the surface of what’s possible. Multi-agent AI systems deploy specialized AI agents that work together—much like your own finance team—to solve complex AR challenges efficiently and at scale.

Unlike single-agent AI that attempts to handle everything from invoicing to collections, multi-agent systems recognize that different AR functions require different expertise, with each agent collaborating to optimize your entire order-to-cash process.

How multi-agent AI systems transform your AR operations

Your accounts receivable process generates massive amounts of data across invoicing, payment processing, credit evaluation, collections, and dispute resolution. Multi-agent AI systems excel by mirroring how successful finance organizations work—specialized agents focus on their expertise while keeping communication lines open. A collections agent collaborates with a credit agent to identify high-risk accounts, while a payments agent works with an invoicing agent to optimize delivery channels based on customer preferences.

Billtrust Agentic AI can boost productivity by 80% for your collections team. Collections AI agents organize your inbox and craft emails using GenAI, accelerating case creation and dispute resolution. Click ‘approve’ and your work is done.  Learn more in this podcast.

Bringing multi-agent AI systems to your finance operations

Traditional automation tools create isolated improvements, while multi-agent systems operate as an integrated network that sees the big picture while excelling at individual tasks. Your AI agents identify opportunities to control costs, accelerate cash flow, and improve customer satisfaction across every touchpoint in your order-to-cash cycle.

Inside multi-agent AI architecture: How specialized agents work together

Understanding how multi-agent AI systems operate helps finance leaders appreciate their transformative potential for AR processes. The architecture consists of interconnected components that mirror your own finance team’s collaborative approach to problem-solving.

The strategic framework

Multi-agent AI systems work like your best-performing finance team—with specialized roles collaborating toward shared objectives. Each component focuses on what it does best while maintaining communication to accelerate cash flow and improve customer satisfaction.

How the system delivers results

The system begins by analyzing your business objectives and breaking them into focused tasks. Specialized agents handle specific AR functions—one might focus on collections optimization while another analyzes payment patterns. Throughout the process, you maintain oversight and control, approving recommendations that align with your priorities. Finally, all insights are synthesized into clear, actionable recommendations that help you control costs, accelerate cash flow, and improve customer experiences.

This collaborative approach transforms complex AR challenges into manageable opportunities, giving you the insights needed to make decisions that drive business growth.

Enterprise-grade security for multi-agent AI systems

Finance leaders rightfully prioritize data security when evaluating AI solutions for their AR processes. Billtrust’s multi-agent AI systems are built with enterprise-grade security at every level, ensuring your sensitive financial data remains protected while delivering powerful automation capabilities.

Multi-layered security approach

Your data security operates through multiple protective layers. Access to sensitive information is strictly controlled through authentication protocols, with AI agents operating within carefully defined boundaries. The system creates an abstraction layer between the AI agents and your actual data, ensuring that sensitive financial information remains protected while agents access only the data they need to perform their specialized functions.

Human oversight built in

Multi-agent AI systems are designed to augment your decision-making, not replace it. The system recommends changes and optimizations, but you maintain final approval authority over all actions. Rather than making autonomous changes to your AR policies, the AI provides recommendations that you can review and approve based on your business priorities. This ensures that AI assists your decisions while you retain complete control over your AR processes.

This security-first approach gives you confidence to leverage AI’s power for accelerating cash flow and controlling costs while maintaining the data protection standards your business requires.

Billtrust is at the forefront of this revolution, investing heavily in AI-driven solutions to empower finance teams. “We don’t look at AI as a silo; we see it as an integral part of our overall data analytics strategy, augmented by AI,” Shah explains. Billtrust’s phased approach to AI development ensures that we are continuously pushing the boundaries of what’s possible in AR.

Ahsan Shah believes that using multi-agent tools in graph execution represents the future of B2B SaaS for enterprises. This vision for the future of B2B software extends beyond mere cost reduction. “It’s about creating value and identifying new opportunities,” Shah emphasizes. By empowering finance organizations to define their objectives, and then unleashing a team of AI agents to strategize and execute, Billtrust is paving the way for unprecedented levels of AR optimization.

Multi-agent AI presents an exciting opportunity for finance leaders to transform their AR processes and optimize the AR lifecycle. By adopting this cutting-edge technology, you can unlock unprecedented efficiency, insight, and strategic advantage. Leveraging the collective intelligence of specialized agents enables businesses to achieve continuous optimization and make more informed decisions.

Multi-agent AI has the potential to revolutionize not just accounts receivable, but the entire B2B software landscape. By enabling AI systems to function more autonomously and collaboratively, companies can unlock new levels of efficiency, insight, and value creation. As Shah notes, “It’s in early innings still, but we’re moving at a rapid pace.”

As we move towards 2025 and beyond, the adoption of multi-agent AI systems is expected to increase, driven by advancements in AI technology and the growing need for efficient and effective AR management. Businesses that embrace these systems will be well-positioned to stay competitive and achieve their strategic goals.

Billtrust is committed to leading this charge, empowering businesses to navigate the future of finance with confidence and achieve unprecedented success.