Running the Workflow & Final Output | Multi Agent Workflows for Beginners | Part 10

Why It Matters

This illustrates practical, low-effort automation for complex workflows—combining code generation and domain research via specialized agents—potentially speeding development and research tasks for businesses and professionals. It shows scalable, domain-agnostic patterns for deploying multi-agent systems to improve productivity and task routing.

Summary

The video demonstrates running a multi-agent workflow where a supervisor routes tasks to specialized agents: a coder agent that generates complete HTML/CSS/JavaScript portfolio code and a researcher agent that produces a structured, iterative research report on radiology. The presenter runs code cells to invoke each agent and shows the coder’s output can be copied into an index.html to produce a working portfolio site. The researcher agent is configured with a recursion limit to iterate and refine a detailed report covering history, imaging types, and use cases. The walkthrough highlights how agent orchestration can be applied across domains such as healthcare and sales.

Original Description

In Part 10, we finally execute our multi-agent workflow and see how the system performs in action.

In this session:

- Run real prompts through the supervisor agent

- See task routing in action (e.g., coding → coder agent, research → researcher agent)

- Generate practical outputs like:

Full HTML/CSS/JS portfolio code via the coder agent

Structured domain reports via the researcher agent

- Observe how the supervisor assigns tasks based on intent and returns final results

This part demonstrates how everything comes together — from setup to execution — showing a working multi-agent system you can modify and reuse for real use cases.

#AI #MultiAgent #AgenticAI #LLM #WorkflowExecution #BuildInPublic #GenerativeAI #Automation

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