Join me as I chat with Amir about how to use OpenAI’s new Agent Builder to create a multi-agent chatbot workflow that can classify user inquiries, provide customer support, and capture lead information. The video showcases how to build a complete solution using vector stores for context, logic nodes for decision-making, and ChatKit for website integration, all without requiring extensive coding knowledge.
Timestamps:
00:00 – Intro
00:57 – Overview of Agent Builder
02:13 – Overview of ChatKit
03:05 – Overview of Widgets
03:57 – Building a workflow with classifier and support/lead agents
13:57 – Demo of support/lead agents
16:29 – Integration with ChatKit to embed the chatbot on websites
19:23 – Differences between Agent Builder vs other alternatives
20:48 – Key Takeaways
25:25 -Opportunities for founders
Key Points:
• OpenAI released three major tools: Agent Builder (visual workflow creator), ChatKit (SDK for embedding chatbots), and Widgets (dynamic UI components)
• Agent Builder allows non-technical users to create multi-agent workflows with a drag-and-drop interface
• The demo shows how to build a chatbot that classifies users as leads or existing customers and responds accordingly
• ChatKit enables easy integration of these workflows into websites without developer dependency
1) The Three Major Updates from OpenAI Dev Day
OpenAI just released THREE game-changing tools:
• Agent Builder: Visual interface for multi-agent workflows
• ChatKit: SDK to embed chatbots on websites
• Widgets: Dynamic UI components for chat interfaces
These tools DRAMATICALLY lower the barrier to entry for non-technical people to build powerful AI solutions!
2) Agent Builder: Multi-Agent Workflows Made Visual
Before: You needed custom code to create multi-agent orchestration
Now: Drag-and-drop visual interface where you can:
• Create sequential or parallel agent workflows
• Connect to vector stores for context
• Add logic and conditions
• Implement guardrails for safety
"It’s essentially reducing the barrier for non-technical people to get started with multi-agent workflows" – Amir
3) How Agent Builder Actually Works
Each workflow consists of connected "nodes" representing actions:
• Start with user input
• Add classifier agents to determine intent
• Create logic branches based on conditions
• Connect specialized agents for different tasks
• Add tools and vector stores for context
The POWER is in specialization – each agent handles ONE specific task extremely well!
4) The Demo: A Smart Customer Support Bot
Amir built a chatbot that:
1. Classifies if you’re an existing customer or new lead
2. If customer → Answers support questions using knowledge base
3. If lead → Collects info and prepares for sales follow-up
All without writing a single line of code!
The bot can even pass data to your CRM or Slack using MCPs (Model Context Protocols)
5) ChatKit: Embedding Your Agents Anywhere
Once you’ve built your workflow in Agent Builder:
• Get your workflow ID
• Use ChatKit to embed it on your website
• Customize appearance and behavior
• Deploy without developer dependency
6) Why This Matters for Non-Technical Teams
The BIGGEST insight: This removes the terminal/CLI barrier!
"The CLI is daunting for people… computers didn’t hit mainstream adoption until there was a graphical user interface on top" – Greg
7) How This Compares to Claude
• OpenAI is catching up on visual workflow builders
• Claude still leads with MCPs (they invented the protocol)
• OpenAI needs to expand their MCP directory
8) Getting Started with Agent Builder
1. Define your use case first
2. Map out your workflow
3. Prepare your data context (vector stores)
4. Use as little context as possible for best performance
5. Specify agent roles clearly
6. Add external tools as needed
9) Key Opportunities for Founders
1. Use ChatGPT apps as a distribution channel
2. Get Agent Builder in front of your NON-TECHNICAL teams
3. Empower product managers, support and sales teams
4. Have engineers support with MCPs and server setup
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