Multi-Agent Tracing & Workflows Explained | OpenAI #multiagent #agenticai #openaiagentssdk

Join this channel to get access to perks: https://www.youtube.com/channel/UCcA2nhdC0wzqyv9x1lk5NnA/join Learn how to build a functional multi-agent application where two AI agents collaborate to generate network engineering content. In this tutorial, we implement advanced tracing configurations and build a custom Streamlit UI to visualize the agentic workflow in real-time. The Deep Dive: Welcome to the fourth video in the Agent Learning Series. Today, we move beyond single-agent setups to create an orchestrated flow where data is passed seamlessly between agents. We will build a "Network Expert" app where Agent 1 (The Prompt Architect) structures instructions, and Agent 2 (The Content Generator) executes the request. Key concepts covered in this video: Agent Chaining: How to pass the output of one agent as the input for the next using Python. Advanced Tracing: Using run config and trace IDs to group multiple agent executions under a single workflow name for better debugging and observability. Async Implementation: Handling asynchronous calls with runner.run to ensure smooth performance. Streamlit Integration: Building a reactive UI with uv add streamlit to accept user input and display multi-column agent outputs. UI Visualization: coding logic to show "Running" states and render Markdown output dynamically. This step-by-step guide covers everything from the initial Python notebook logic to the final multi-agent-demo.py file with a polished frontend. Found this walkthrough helpful? Subscribe for more tutorials on building autonomous AI systems. Drop a comment below if you have questions about handling trace errors or Streamlit configs! Multi-agent systems, Python AI tutorial, Streamlit UI for LLMs, OpenAI API, Agent tracing, LLM orchestration, Python AsyncIO, AI workflow automation, coding AI agents, prompt engineering for agents, network engineering AI, software development, building AI apps, debugging AI workflows, latent semantic indexing, visualising agent traces.