Agentic Architectural Patterns for Building Multi-Agent Systems

Generative AI has moved beyond the hype, and enterprises now face the challenge of turning prototypes into scalable solutions. Starting with a GenAI maturity model, you’ll learn how to assess your organization’s readiness and create a roadmap toward agentic AI adoption. You’ll master foundational topics such as model selection and LLM deployment, progressing to advanced methods such as RAG, fine-tuning, in-context learning, and LLMOps, especially in the context of agentic AI. This session introduces a concrete, hierarchical multi-agent architecture where high level Orchestrator agents manage complex business workflows by delegating entire sub processes to specialized agents. You’ll see how these agents collaborate and communicate using the Agent-to-Agent (A2A) protocol.

Apply design patterns to handle instruction drift, improve coordination, and build fault-tolerant AI systems Design systems with the three layers of the agentic stack: function calling, tool protocols (MCP), and agent-to-agent collaboration (A2A) Develop responsible, ethical, and governable GenAI applications Use frameworks like ADK, LangGraph, and CrewAI with code examples Master prompt engineering, LLMOps, and AgentOps best practices Build agentic systems using RAG, fine-tuning, and in-context learning