Patterns & Practices for building Multi-Agent Systems

Multi-Agent Systems are poised to transform industries by enabling end-to-end automation of complex business processes. They are composed of autonomous AI Agents that cooperate in a distributed & decentralized manner.

Unfortunately implementing these systems in practice is hard due to factors like decentralized architecture with the need for coordination, cooperation & conflict resolution.

This talk is about building such systems and covers architectural patterns, the challenges associated with implementation, and strategies to mitigate them. We start with the anatomy of AI agents and Model Context Protocol for inter-agent communications.

We then introduce Agentic Workflows and their characteristics like adaptability, distributed decision-making, goal-oriented nature, robustness with fault tolerance, continuous feedback loop, and tool integration. Various architecture patterns like Orchestrator-Work pattern, Hierarchical Agents pattern, Blackbox pattern, and Event-driven pattern are also discussed.

Towards the end, we can package these workflows together as a system that can be deployed in an enterprise and cover aspects like context construction using RAG, input/output guardrails for safety & security, cache for optimization, etc.

The objective of the talk is to show how to implement Multi-Agents Systems and that use sophisticated reasoning & planning to solve complex, multi-step problems. Practical Applications of Multi-Agent systems are also included.