As generative AI systems evolve from single LLM calls to complex, goal‑driven workflows, multi‑agent architectures are becoming essential for robust, scalable, and explainable AI applications. This talk presents a practical framework for designing and implementing multi‑agent generative AI systems, covering four core orchestration patterns that define how agents coordinate:

Orchestrator‑Worker: A central agent decomposes a task and delegates subtasks to specialized worker agents, then aggregates and validates results.

Hierarchical Agent: Agents are organized in layers (e.g., manager, specialist, executor), enabling abstraction, delegation, and error handling across levels.

Blackboard: Agents contribute to and react from a shared “blackboard” workspace, enabling loosely coupled, event‑driven collaboration.

Market‑Based: Agents act as autonomous participants that negotiate, bid, or compete for tasks and resources, useful in dynamic, resource‑constrained environments.

For each pattern, we show concrete use cases, such as customer support triage, research synthesis, code generation pipelines, and discuss trade‑offs in latency, complexity, and observability.