AI Agents vs. Multi-agent systems: From solo expertise to orchestrated collective intelligence

Tuesday, 27 May 2025

AI Agents vs. Multi-agent systems: From solo expertise to orchestrated collective intelligence

Choosing between individual AI agents and multi-agent systems is becoming a strategic decision for organizations. Discover how these emerging tools can revolutionize your operational performance.

This article is part of our trend series on Multi-agent AI. Explore all related content.

Two approaches to creating value with Agent-based AI

AI agents – these autonomous programs capable of executing specific and complex tasks - are already transforming business processes by automating and streamlining operations. But innovation is going even further with multi-agent systems (MAS), which coordinate multiple specialized AI agents to work together.

According to Gartner, 75% of large enterprises will have adopted MAS by 2026. Even more striking, BCG projects that MAS will generate $53 billion in revenue by 2030, nearly ten times the $5.7 billion recorded in 2024.

Comparison table: AI Agent - Multi-agent system

Criteria	AI Agent	Multi-agent system (MAS)
Definition	An autonomous program designed to perform specific tasks based on defined goals. It can interact with its environment and make decisions.	A coordinated ecosystem of multiple specialized agents working together to achieve a shared goal, creating a form of “collective intelligence” greater than the sum of its parts.
Challenges addressed	Repetitive, time-consuming tasks Need for hyper-personalized customer interactions Difficulty accessing targeted information Limited human resources for specialized tasks	Multidimensional problems Coordinating complex actions in shared environments Siloed processes Conflict resolution and negotiation
Use cases	Personal assistant for information retrieval Specialized customer service chatbot Administrative task automation	Supply chain optimization (e.g., DHL: -15% fuel costs) Multi-criteria financial analysis (e.g., JPMorgan DeepX) Robotic warehouse orchestration (e.g., Ocado: +50% efficiency) Predictive quality monitoring Advanced risk and anomaly detection
Key benefits	Easy to implement and deploy Wide range of frameworks available Focused excellence in a specific domain Predictable and controllable outcomes	Dynamically adapts to change Solves both simple and complex problems Scalable solutions Modular and specialized Emerging standards (e.g., A2A)
Limitations /Challenges	Limited capabilities Struggles with complex, multidimensional tasks Limited scalability Often isolated from broader ecosystems	Complex to implement and orchestrate Requires monitoring of agent interactions Conflict management between autonomous agents Security and risk of compromise Requires technical expertise Integration with existing infrastructure

AI Agent or Multi-Agent system: Which technology best fits your use cases?

Personalized virtual assistant for bank advisors (AI Agent)

Challenge

A retail bank found that its advisors were spending over 35% of their time searching for information across various systems, reducing time available for high-value client interactions.

Solution

Deployment of a dedicated AI assistant for each advisor, capable of:

Instantly retrieving full client history before meetings
Preparing personalized financial summaries of the client’s financial situation
Recommending relevant products based on client profile and goals
Automating the generation of personalized documents

Results

25% increase in effective client advisory time
18% improvement in customer satisfaction
15% boost in conversion rates for commercial offers
22% reduction in administrative errors

Why this case is a standout example

This case perfectly illustrates the power of an individual AI agent when it’s properly designed to integrate into a professional workflow. It significantly enhances advisor efficiency in a specific domain (financial consulting) while remaining under human supervision. The value is immediately measurable, for the institution (conversion rates), the clients (satisfaction), and the employees (reduced low-value tasks). It shows that a well-designed single agent can significantly transform a business function without the need for the complex orchestration of a multi-agent system.

Industrial predictive maintenance and monitoring (MAS)

Challenge

An industrial group was struggling to maintain optimal performance across its factories in multiple countries, facing costly production downtime due to unexpected equipment failures and a maintenance approach that was more reactive than preventive.

Solution

Implementation of a multi-agent system for predictive monitoring and maintenance, including:

Data collection agents: Real-time monitoring of IoT sensors on each piece of equipment
Sector-specific analysis agents: specialized by equipment type to detect anomalies
Predictive modeling agents: Calculate the probability of failure for each component
Maintenance optimization agents: Plan interventions for maximum efficiency
Resource management agents: Allocate technicians and spare parts
Central orchestrator agent: Resolve potentially conflicting recommendations

Results

35% reduction in unplanned downtime
22% increase in lifespan of critical equipment
28% optimization in maintenance costs
15% overall productivity improvement

Why this case is a standout example

This case embodies the true power of multi-agent systems to tackle multidimensional challenges that a single agent couldn’t handle effectively. Industrial-scale predictive maintenance involves analyzing diverse data types (vibration, temperature, electricity, acoustics), predicting failures across thousands of machines, and optimizing both human and material resources. Each agent brings its own specialized expertise, but it’s their orchestrated collaboration that creates a collective intelligence capable of radically transforming operational performance. Potential conflicts between objectives (e.g., minimizing interventions vs. minimizing failure risk) perfectly illustrate the critical role of the orchestrator in a multi-agent system. Additionally, integration into the company’s technical infrastructure is a key challenge to ensure the project can scale effectively.

A strategic approach to deploy these technologies

Diagnosis and identification of priority use cases

Before deploying an agent-based solution, it’s essential to clearly identify your operational pain points and asses their potential to be addressed through AI. A detailed analysis of your business processes will help prioritize high-impact use cases and define clear performance indicators.

Architecture and design tailored to your needs

Designing the right architecture is a critical step:

For AI agents: it allows you to clearly define their scope, capabilities, and how they interact with existing systems.
For MAS: it enables you to design the orchestration model (centralized, decentralized, or hybrid) and anticipate potential conflicts.

Governance and ethics by design

Integrating ethical safeguards from the design phase is essential. This includes:

Human validation mechanisms for sensitive decisions
Transparency in decision-making processes
Compliance with applicable regulations

Monitoring and continuous improvement

Implementing robust monitoring is essential to:

Evaluate technical and operational performance
Identify optimization opportunities
Quickly detect and fix issues
Measure real business impact and ROI

The importance of expert support

The success of your transformation through agent-based AI depends on specialized support. Whether you choose an AI agents or multi-agents systems, AI experts can help you:

Analyze your processes and identify the highest-ROI opportunities
Design a scalable architecture aligned with your overall strategy
Secure deployment and ensure adoption by your teams
Measure impact and continuously optimize performance

Without this expertise, AI projects risk remaining isolated initiatives with little transformational impact on your organization.

Empower your teams with AI

Discover more in our trend report

Multi-Agent AI Systems

AI Agents vs. Multi-agent systems: From solo expertise to orchestrated collective intelligence

Two approaches to creating value with Agent-based AI

Comparison table: AI Agent - Multi-agent system

Criteria

AI Agent

Multi-agent system (MAS)

Definition

Challenges addressed

Use cases

Key benefits

Limitations
/Challenges

Personalized virtual assistant for bank advisors (AI Agent)

Challenge

Solution

Results

Why this case is a standout example

Industrial predictive maintenance and monitoring (MAS)

Challenge

Solution

Results

Why this case is a standout example