Collaborative AI Explained | Multi-Agent Systems in Practice

Описание: Welcome to Class 12 of FusionPact’s AI/ML Daily Sessions.

In this session, we explore Multi-Agent Systems and Collaborative AI Architecture, where multiple AI agents work together to solve complex tasks more efficiently than a single AI model.

Instead of one large model handling everything, multi-agent systems divide work across specialized agents such as researchers, planners, developers, reviewers, and monitoring agents. Each agent has a specific role and collaborates through communication, shared memory, task orchestration, and feedback loops.

This class covers:

What are multi-agent systems
Why collaboration is required in complex AI projects
Difference between single-agent and multi-agent systems
Role-based agents and specialization
Task decomposition and sub-task execution
Agent communication and message passing
Prompt importance in agent collaboration
Task orchestration and monitoring agents
Shared memory and context preservation
Feedback loops for continuous improvement
Collaborative AI system architecture
Popular multi-agent frameworks:
CrewAI
AutoGen (Microsoft)
LangGraph
Business and enterprise use cases:
Product planning and analysis
Software development and testing
Business intelligence and analytics
E-commerce pricing, inventory, and marketing agents
Scientific research and literature analysis

Key takeaway:
Multi-agent systems represent the next evolution of AI, enabling scalable, autonomous, and intelligent systems through collaboration rather than isolated intelligence.

This session is ideal for:
AI & ML students
Agentic AI developers
Software engineers
AI architects
Anyone building autonomous or scalable AI systems

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