What If Your AI Could Work Like a Team? Exploring Multi-Agent Reinforcement Learning

When Bill Gates dropped the bombshell at the AI Forward 2023, declaring that the ultimate technology race would be around developing the top AI Agent, not everyone grasped the weight of that statement. But as more AI agents have emerged, it’s now clear exactly what he meant.

Back in 2024, when we first started talking about AI agents for financial institutions, only a handful of businesses could truly relate to this revolutionary tech. But by 2025, the game will have completely changed. AI agents are now everywhere. Across industries, businesses are actively seeking AI agents to get the job done. And if you’re one of them, a business owner, senior manager, team lead, or someone who landed on this blog to do some tech research before choosing the right product (or maybe just curious about all the hype around AI agents), you’ve come to the right place.

In this blog, we’ll walk you through everything you need to know from a non-technical perspective, just the angle you need to think about automation for your business.

Single-Agent Systems

AI agents were built to automate complex tasks that require cognitive capacity, vision, and critical thinking, enabling them to operate with increasing autonomy. Early agentic platforms were based on single-agent systems, designed to function like a human brain: independently planning, executing, and completing tasks. In these systems, one AI agent is equipped with various tools and uses the reasoning power of a large language model (LLM) to strategize and solve problems. It maps out a plan, applies the right tools at each step, and compiles the outputs to deliver the final result, handling both simple and complex goals on its own. Their simplicity makes them easier to design, implement, and manage. 

However, single-agent systems come with limitations. They are typically narrowly focused, which restricts their ability to handle diverse or rapidly changing tasks. Their specialized nature hinders performance in environments with varied or dynamic requirements.

As task complexity increases, these systems struggle. They can’t split themselves to perform tasks in parallel. If something goes wrong mid-process, there are no internal checks or fail-safes, making them prone to errors. They might be equipped to handle multiple simple tasks, but not a single complex one. If you think about it, the old saying “jack of all trades, master of none” fits perfectly here.

Single-Agent-Systems might be equipped to handle multiple simple tasks, but not a single complex one.

Scaling a single agent to manage broader or higher-value tasks requires significant reengineering, especially when dealing with complex, document-heavy industries like mortgage, banking, and insurance. These industries go far beyond basic data extraction. They require cross-document checks, verifications, and calculations.

In such cases, single-agent systems fall short. They can’t juggle multiple tasks at once, they lack internal checkpoints, and they often fail under the weight of complexity.

Multi-AI Agent Agentic System and Multi-Agent Reinforcement Learning 

That’s when the reality hit: these agentic systems need to be more powerful. What if we had individual agents within a single agentic platform that worked together as a team to deliver better output? This is the inception of multi-agent reinforcement learning. So here, instead of just one agent handling everything from scratch, multiple agents were introduced into one agent system. 

What is Multi-Agent Reinforcement Learning (MARL)?

Multi-Agent Reinforcement Learning is an advanced form of Machine Learning (ML) where multiple AI agents learn, interact, and evolve together in a shared environment. Unlike single-agent systems, where one agent learns a policy based on its own actions and rewards, MARL involves several agents, each developing its own strategy (or policy) while adapting to the behavior of others.

A policy is just a strategy or decision-making rule that tells an agent what to do in any given situation.

These agents can collaborate, like players on a soccer team; compete, like bidders in an auction; or operate in hybrid settings, balancing cooperation and self-interest.

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Why Mult-Agent Reinforcement Learning Changes Everything

The biggest leap with multi-agent reinforcement learning wasn’t just parallel task handling, it was continuous self-correction and internal feedback. In multi-agent systems, tasks are distributed among specialized agents. Each agent focuses on a specific part of the workflow, while others supervise, validate, and course-correct the process.

This internal feedback loop ensures that errors are caught and resolved in real-time, long before human review is ever needed. It's not just about dividing the work; it's about making sure that every piece of work gets reviewed, cross-checked, and improved upon by other agents.

At its core, reinforcement learning is about training agents to operate in uncertain, dynamic environments, exactly like real-world document workflows in industries such as mortgage, banking, or insurance.

And now, Multi-Agent Reinforcement Learning is the backbone of today’s most powerful agentic platforms - a system of intelligent, autonomous agents that learn, verify, and act together.

Single Agent and Multiple Agent System Key Differences 

Zoom In to Multi-Agent Reinforcement Learning: Citic Agents, Observer Agents, and Actor Agents 

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Now that we’ve gone over the basics of how multi-agent reinforcement learning works, let’s break down the key players involved. All advanced multi-agent systems are built around three main roles: the observer, the actor, and the critic. Think of them as teammates, each with a specific job, working together to help the system make better decisions and keep improving over time.

The Observer Agent

It all begins with the observer agent, which serves as the agent’s connection to the environment. Much like human senses, the observer doesn’t make any decisions; its job is to perceive the world and collect data. At each time step, it scans the environment and gathers observations, i.e, information about what’s happening in that moment. These observations are then passed along to both the actor and the critic, setting the stage for what comes next.

The Actor Agent 

The actor agent is the decision-maker of the team. Once it receives the observation from the observer, it uses a learned policy, a kind of strategy based on past experiences, to decide what action to take. This decision is not hardcoded; the actor continuously learns and adapts its behavior based on what seems to lead to the best outcomes. The action it selects is then sent to the environment, triggering a new response from the system and inching the agent closer to its goal.

The Critic Agent 

But to truly learn, the agent needs more than just actions and outcomes - it needs evaluation. That’s where the critic agent comes in. The critic’s job is to evaluate how good the actor’s decision was, using the observation, the action taken, and the reward received from the environment. The critic doesn’t choose actions itself, but it plays a vital role in training the actor. It provides feedback that helps the actor understand whether its actions are moving it toward higher long-term rewards or not.

Together, these three components form a continuous learning loop. The observer feeds information, the actor makes decisions, and the critic judges the outcomes and guides improvement. This dynamic allows the agent to not just respond to its environment, but to evolve its behavior over time, becoming more accurate, efficient, and intelligent with every iteration.

This collaborative structure, where sensing, decision-making, and feedback all work hand-in-hand is what powers some of the most advanced AI systems in use today. Whether it's automating document reviews, optimizing robotic movements, or managing complex workflows, the combination of observer, actor, and critic agents enables AI to learn from experience and perform with precision in uncertain environments.

* The terms observer, actor, and critic are not standardized across all systems. While the names may vary, their core functions remain the same.

Decoding What’s Behind Infrrd’s AI Agent - Ally 

If you unscrew and dismantle Infrrd’s AI agent Ally, you’ll find a multi-agent reinforcement learning system within. The teamwork of this multi-agentic Ally is at the heart of our agents’ industry-specific expertise.

Meet Ally: Infrrd’s Industry-Trained AI Agent Built for Mortgage Lending Precision

The mortgage process is filled with nuance: whether it's parsing through complex 1003s, identifying trailing documents, or reconciling data across a convoluted paper trail. Generic automation tools often fall short because they’re not built with the mortgage lifecycle in mind. That’s why Infrrd’s AI Agents are built on a robust foundation of multi-agent reinforcement learning, a system where three distinct roles - observer, actor, and critic agents collaborate to simulate the expertise of a well-trained mortgage team.

Introducing Mortgage Ally, our first AI Agent designed exclusively for the mortgage industry. Ally brings deep, purpose-built intelligence to the table, trained on massive volumes of industry-specific documents including LE, CD, AUS findings, W-2s, tax returns, appraisal reports, and closing disclosures. 

What makes Ally different is that it's not just another OCR tool. It’s a mortgage-native AI that understands your workflow - be it loan origination, pre-funding QC, post-close audits, or servicing transfers. Trained on domain-rich datasets and reinforced by mortgage-specific logic, Ally offers end-to-end automation that doesn’t just extract data but interprets it like an underwriter, validates it like a compliance officer, and learns from it like a seasoned loan processor. For lenders and servicers, this means faster turn times, reduced repurchase risk, fewer suspended loans, and the highest possible accuracy in document classification, data validation, and field-level extraction.

With Ally, you're not just automating the mortgage process. You're elevating it—with an AI that speaks your language, knows your pain points, and delivers enterprise-grade automation at scale.

Top Up Your Mortgage Automation with Ally 

The key features of Infrrd's Mortgage Ally AI Agent:

• Pre-Trained Mortgage Expertise: Ally comes with built-in knowledge of mortgage processes, eliminating the need for extensive training.​
• Automated Income Calculation & Verification: Handles complex income assessments, ensuring compliance with lending standards.​
• High Accuracy: Achieves approximately 99% accuracy in processing, reducing errors significantly.​
• Memory Retention: Remembers context and decisions across workflows, improving efficiency over time.​
• Workflow Automation: Automates up to 80% of mortgage processing tasks, streamlining operations.​
• Compliance Assurance: Flags any discrepancies against uploaded business rules to maintain compliance.​
• Adaptive Learning: Continuously learns and adapts to changing business objectives and conditions.​
• Integration Capabilities: Seamlessly integrates with existing mortgage LOS and IDP systems for smooth data transfer.​
• Comprehensive Audit Trails: Provides detailed audit trails for transparency and regulatory compliance.​
• Real-Time Notifications: Keeps users informed with dynamic screens and real-time updates during the loan audit process.

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