The Massive Promise of Multiple Agent Systems (MAS)
In the rapidly evolving world of artificial intelligence, Multiple Agent Systems (MAS) stand out as a disruptive advancement that promises to revolutionize most industries. Building on an article I published back in October of last year, "Going Beyond Bots with Autonomous "Special" AI Agents" that highlighted how Autonomous Agents are moving AI technology from knowledge-based to action-based, I’d like to discuss the massive promise of Multiple Agent Systems (MAS). Multiple Agent Systems are progressing Agentic AI technology from discrete use-case or function-based “action”, to more complex, and dynamic “collaboration and reasoning-based” solutions.
Think of MAS as an orchestrated arrangement of AI capabilities, where discrete purpose-built agents collaborate to solve complex issues and workflows. Unlike traditional AI models like Large Language Models (LLMs) or Robotics Process Automation (RPA), MAS offers enhanced verification, citation, and reasoning capabilities, significantly minimizing the possibility of hallucinations and elevating the potential for AI-driven solutions.
By orchestrating multiple AI Agents, you can tackle complexities, analyze vast amounts of data, apply economic, environment, competitive, compliance, and customer insights to move beyond action and basic intelligence to interpret, infer, contextualize, and collaborate - more closely resembling human reasoning and wisdom. In today’s increasingly complex and dynamic business world, across industries, MASenabled capabilities may determine who are the future winners and who are the “also-rans”
A New Era of Collaboration
Multiple Agent Systems (MAS) represent a new paradigm in AI, transitioning from mere automation to sophisticated collaboration and decisioning. These systems consist of multiple, context-aware agents, each with specialized capabilities, working together in an orchestrated manner to address complex problems. This collaborative approach allows MAS to outperform traditional RPA, which primarily focuses on automating repetitive tasks and lacks context for ad-hoc decisioning.
Multi-Agent Systems go beyond surface-level analysis, mere data manipulation and text generation to find deeper or nuanced connections between datasets, emerging trends, and potential risks, more closely resembling human wisdom and foresight.
Similarly, while LLMs are able to generate coherent and contextually relevant responses, they are prone to hallucinations, often lack deeper understanding, and are often unable to verify (cite sources of) their outputs. The integration of Generative AI with Multi-Agent Systems is predicted to drive innovation and creativity in problem-solving. MAS go beyond surface-level analysis, data manipulation and text generation to find deeper or nuanced connections between datasets, emerging trends, and potential risks, more closely resembling human wisdom and foresight. In coordinated fashion, MAS can seamlessly integrate disparate data allowing the orchestrated team of Agents to connect discrete insights and collaborate to understand the implications a changing factor, decision, or action has across the enterprise. Multi-Agents Systems can adapt to changing conditions by adding, modifying, removing, or even intentionally having agents compete. This makes MAS highly scalable and agile for solving complex and dynamic problems.
Furthermore, MAS systems like WEAVE.AI and AllegroGraph-enabled solutions, designed using Neuro-Symbolic Knowledge Graph technology integrate symbolic reasoning, structured knowledge, and collaboration between specialized agents to provide more accurate, reliable, and insightful outcomes. They can verify information, cite sources, and reason in ways that resemble human thinking, heightening clarity and confidence in recommended decisions and actions.
Relationship Between Agentic AI and Neuro-Symbolic AI
Neuro-symbolic AI combines neural networks (deep learning) with symbolic reasoning. This approach integrates the pattern recognition capabilities of neural networks with the logical, rule-based reasoning of symbolic AI. Neuro-symbolic AI - combines the efficacy of symbolic AI in managing structured data and the proficiency of neural networks in processing large-scale unstructured data.
The relationship between Multiple Agent Systems and Neuro-symbolic AI is extremely synergistic. They can complement each other to provide:
- Enhanced Capabilities: By integrating Neuro-symbolic AI into Agentic AI systems, Agents can use both deep learning and symbolic reasoning. This enhances their ability to understand complex patterns, selflearn from data, detect anomalies, perform “WhatIf” analysis, and make logical decisions.
- Improved Collaboration: Neuro-symbolic AI can improve communication and collaboration between Agents. Symbolic reasoning allows Agents to share knowledge and rules, comprehend linguistic nuances, and facilitate better coordination.
- Increased Verification & Explainability: Neuro-symbolic AI can provide better verification and explainability of Agentic AI systems. The symbolic component ensures that decisions are transparent and based on logical rules, improving consistency, trust and reliability.
The collaborative interaction between Multiple Agent AI and Neuro-symbolic AI brings together the best of both worlds—autonomous collaboration and sophisticated reasoning—enabling the development of more intelligent and explainable AI systems.
Unlocking Value with Multiple Agent Solutions
MAS is poised to have a profound impact on several key roles across industries. Researchers, analysts, risk managers, and logistics managers will all benefit from the enhanced capabilities that MAS brings. Several early solution entrants prove the immense value Multiple Agent Systems can unlock. For instance:
- Healthcare: A Multi-AI Agent System for patient care might include Agents specialized in diagnosis, treatment planning, and patient monitoring. Integrating Neuro-symbolic AI would enhance their ability to reason about medical knowledge, verify diagnoses, and provide explainable, personalized treatment plans. In addition to enabling improved patient experiences and outcomes, MAS can help reduce operational costs by 10% to 30%.
- Finance: In investment management, MAS solutions can analyze vast data sets, corporate documents, and market data to more accurately forecast market Source: Franz Inc. trends, accelerate discovery, and manage risks. Neuro-symbolic AI can improve the reasoning behind investment decisions and ensure that recommendations are well-supported and transparent. This leads to improved trend forecasting, enhanced decision-making, and improved investment thesis and strategies. Based on early use cases, asset portfolio performance can be improved by up to 20% via holistic risk and compliance management, rapid identification of underperformers, emerging trends, and competitive benchmarking.
- Enterprise Risk Management (ERM): MAS can employ specialized Agents to analyze relevant climate, supply chain, financial, competitive, macro-economic, regulatory, and geopolitical data. Identifying interconnected vulnerabilities and quantifying potential risks. Improving scenario analysis, providing actionable insights and improved planning to mitigate identified risks. Increasing security and resilience and preemptively avoiding costly incidents. According to a recent Statista report, the average cost in the US of a data security or reputational risk incident is $9.36M, not including any drop in stock value.
- The optimal approach is not necessarily to replace human knowledge workers but rather to augment to make them more accurate, productive, and proactive.
Future Adoption and Impact
Multiple Agent Systems (MAS) enable AI agents to work together, using each other's strengths to achieve more sophisticated outcomes. MAS is a transformative advancement in the field of artificial intelligence, moving beyond automation and information to true collaboration, complex problem-solving, and insight. By harnessing the collective capabilities of specialized AI agents, MAS solutions enhance verification and reasoning, and unlock new value. For industries such as finance and healthcare, operational costs can be reduced by >40% by automating complex workflows, improving collaboration and decision-making, reducing the time required to arrive at actionable insight. The future adoption of MAS solutions is expected to be swift and widespread. As adoption grows, MAS is set to revolutionize business models, ushering in a new era of innovation, efficiency, and competition.
ABOUT THE AUTHOR
Chad Holmes specializes in capitalizing on new technology and business model trends to create transformative, technology-driven change to accelerate growth and value creation. Chad is NiVRT’s Chief Growth Officer and leads the Human-Centered AI practice focusing on the intersection of AI-Empowerment, Human Experience (HX) Design, and Intelligent Operations. SAIGILITY collaborates with clients to shape a future where technology enhances human life, addressing pressing challenges with design and intelligence that both augments and automates human abilities. chad@nivrt.com
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showing good judgment.
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