Multi-Agent Systems
A
- Agent-Oriented Architecture
- Agentic AI Alignment
- Agentic AI for Customer Engagement
- Agentic AI for Decision Support
- Agentic AI for Knowledge Management
- Agentic AI for Predictive Operations
- Agentic AI for Process Optimization
- Agentic AI for Workflow Automation
- Agentic AI Safety
- Agentic AI Strategy
- Agile Development
- Agile Development Methodology
- AI Agents for IT Service Management
- AI for Compliance Monitoring
- AI for Demand Forecasting
- AI for Edge Computing (Edge AI)
- AI for Energy Consumption Optimization
- AI for Predictive Analytics
- AI for Predictive Maintenance
- AI for Real Time Risk Monitoring
- AI for Telecom Network Optimization
- AI Orchestration
- Algorithm
- API Integration
- API Management
- Application Modernization
- Applied & GenAI
- Artificial Intelligence
- Augmented Reality
B
C
D
E
G
I
L
M
N
P
R
S
T
V
At Xebia, Multi-Agent Systems refer to AI architectures where multiple autonomous agents collaborate, compete, or coordinate to solve complex problems. Each agent is capable of independent reasoning and action, but together they form a distributed system that is more adaptive and scalable than a single model or agent.
Xebia helps organizations design and deploy multi-agent systems that leverage large language models, retrieval components, and orchestration frameworks. This enables enterprises to automate multi step workflows, handle diverse tasks, and simulate dynamic environments with resilience and intelligence.
What Are the Key Benefits of Multi-Agent Systems?
- Scalability by distributing tasks among specialized agents
- Flexibility as agents can be designed for distinct roles or domains
- Improved problem solving through collaboration and coordination
- Resilience since the system can continue functioning even if one agent fails
- Realism for simulations of markets, supply chains, or multi user interactions
- Faster innovation by reusing and combining modular agent capabilities
What Are Some Multi-Agent Systems Use Cases at Xebia?
- Customer support where multiple agents handle FAQs, technical troubleshooting, and account services
- Financial services with separate agents monitoring compliance, risk, and trading strategies
- Smart cities where agents optimize traffic, energy usage, and public safety simultaneously
- Manufacturing with agents coordinating supply chain, production scheduling, and maintenance
- Research and education through multi-agent simulations of social or economic systems
- Enterprise automation where workflow orchestrators delegate tasks to domain specific agents
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