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

Comparison table: AI Agent - Multi-agent system

Business 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 matters: It 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 matters: It 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 

Successfully implementing AI agents or multi-agent systems requires a methodical approach and multidisciplinary expertise. To maximize ROI and avoid technical pitfalls, here are the key steps for a successful deployment: 

1. 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. 

2. 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. 

3. 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 

4. 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