The Startup Ideas Podcast – Episode Summary

Episode Title: I got a private lesson on OpenAI's NEW Agent Builder
Host: Greg Isenberg
Guest: Amir
Date: October 8, 2025

Episode Overview

In this episode, Greg Isenberg interviews Amir for a hands-on walkthrough of OpenAI’s latest Agent Builder, Chat Kit, and widgets—tools unveiled during OpenAI’s recent Dev Day. They build a demo multi-agent chatbot live, showing how these new features lower the technical barrier for creating complex workflows, automate customer support and lead management, and offer unprecedented customization for non-technical users and startups. The discussion covers practical implementation, best use cases, and how the new platform compares to existing tools like Intercom, Claude, and SaaS chatbot platforms.

Key Discussion Points & Insights

1. OpenAI Agent Builder, Chat Kit, and Widgets Explained

• Agent Builder: New visual workflow tool for multi-agent orchestration—no code needed.
  • Build complex agent chains via drag-and-drop nodes.
  • Enables parallel or sequential agent flows.
  • Create guardrails for response safety and quality.
  • Lower barrier for non-technical users.
• Chat Kit: SDK and UI for integrating Agent Builder-powered bots into web frontends.
  • Embed custom chatbots directly on your website.
  • Connects visual agent workflows to customer-facing experiences.
• Widgets: Dynamic UI components for chatbots.
  • Integrate commerce/order data (Shopify example), estimated delivery, etc.
  • Enhance chat interactivity and data visualization.

“Typically, anytime we've been wanting to build multi agent workflows, we've had to use custom code... What OpenAI has done with the new update is they've created a visual interface...”
— Amir (02:02)

2. Building a Demo Chatbot: Step-by-Step

• Goal: Route inquiries to either customer support or sales based on user type.
  • Start: User submits message (“input as text” node).
  • Classifier Agent: Uses prompt examples to distinguish between customer/lead.
  • Logic Node: Directs input to one of two downstream agents:
    • Customer Support Agent: Answers using company knowledge base (vector store).
    • Sales Agent: Captures lead details for follow-up (company, email, usage, etc.).
• Data Flow:
  • Uses a vector store as persistent context for agents.
  • Potential integrations: Push lead data to CRM, Slack, or database via MCPs.
• Prompt Creation: Can be generated/enhanced within Agent Builder or via ChatGPT itself.
  • Built-in prompt enhancement and role definition.

“You’re essentially, it’s very meta, you’re using an agent to create agent prompts.”
— Amir (07:21)

3. Key Features and Practical Guidance

• Minimal vs. High Reasoning:
  • Tasks that are “just data collection” get minimal reasoning for speed/cost.
  • More complex help cases use higher reasoning for accuracy and nuance.
• MCP (Model Context Protocol):
  • Lets LLM-powered agents push/pull data from third-party tools (e.g., HubSpot, Slack, Shopify).
  • Currently: Limited official connectors; more expected soon.

“An MCP is essentially a new interface for LLMs to interact with external tools... push and pull data.”
— Amir (11:26)

• Guardrails and Testing:
  • Define privacy and content moderation guides.
  • Preview/test agent flows, classify edge cases, handle errors/hallucinations.
  • Track performance with logs and iterate.

4. Setup, Customization, and Cost

• Deployment: Integrate chatbot via Chat Kit with a script and workflow ID; minimal developer involvement once configured.
• Customization: Entire workflow and UI are tweakable by business users—not just software engineers.

“We’re now removing a lot of developer dependency. What does that mean? ...customer support team... can get the chatbot installed...and make changes...not have to rely on engineering.”
— Amir (15:43)

• Costs: Pay per token via OpenAI API usage—no platform license fees.

5. Comparison to Existing Tools

• Why not Intercom/Gumloop/etc.?
  • Out-of-the-box SaaS tools are great for immediate use.
  • Agent Builder provides deeply customizable workflows and data integration.
  • More control, data ownership, and long-term cost savings for engineering-capable teams.
• Non-technical Adoption:
  • GUI is a breakthrough for mainstreaming, similar to the shift from MS-DOS to Windows.
  • Reduces intimidation of command-line interfaces.

“That’s this moment in AI, right, we’re putting canvases on top of, you know, sort of the hardcore technical hood. Like, the average person doesn’t want to be chilling in a terminal.”
— Greg (22:15)

• Comparison with Claude:
  • Claude has broader MCP support (they invented it).
  • OpenAI needs to expand directory and connectivity.

6. Practical Startup Opportunities

• Growth via ChatGPT Apps:
  • Use new "app" layer as a distribution channel for AI-powered workflows.
• Empowering Teams:
  • Give non-technical teams access to Agent Builder/Chat Kit, with light engineering support.
  • Internal bots for operations, lead gen, support, onboarding, etc.
• Data Preparation:
  • Importance of clean, structured data for context and high-quality responses.
  • Use as little context as needed to reduce performance lag.
• Customization: Tailor user journeys, logic, and integrations more than commercial SaaS.

Notable Quotes & Memorable Moments

• On No-Code Agent Building:

"The key takeaway here is that it's essentially reducing the barrier for non technical people to get started with building multi agent workflows."
— Amir (02:48)

• On Customization and Iteration:

"You can determine the level of reasoning... connect different tools... transform how you want the output format of the text to be."
— Amir (08:18)

• On Data and Guardrails:

"You need to refine this agent constantly... you have to iterate on this and you can't get it right [immediately]... guardrails in place to help you refine this process."
— Amir (12:16)

• On the Importance of a GUI:

"I’m old enough to remember using ms.dos... computers didn't hit mainstream adoption until there was some graphical user interface on top of it... That's this moment in AI."
— Greg (22:07)

Timestamps of Key Segments

• [00:58] – OpenAI Dev Day: Agent Builder, Chat Kit, Widgets announced
• [02:00] – Why Visual Workflows Matter
• [04:50] – Step-by-Step Walkthrough: Classifying customers and leads
• [07:08] – Prompting: DIY or with ChatGPT assistance
• [10:08] – Minimal vs. Advanced Reasoning and Cost
• [11:26] – What is an MCP? Layman’s explanation
• [12:16] – Building trust & guardrails in AI workflows
• [13:50] – Live Demo: Lead qualification and support ticket handling
• [15:43] – Removing developer dependencies: real-world implication
• [17:00] – Costs and server setup
• [19:24] – Why not use commercial SaaS chatbots?
• [22:07] – GUI as a catalyst for mainstream AI adoption
• [23:10] – How to get started: process and best practices
• [24:57] – Claude vs. OpenAI for MCP/connectors
• [25:33] – Opportunities for founders: internal tools, growth via ChatGPT apps

Key Takeaways for Founders

• Now accessible: OpenAI’s Agent Builder allows non-technical ops, product, and support teams to create bespoke, multi-agent AI workflows with minimal engineering support.
• Custom > Canned: For startups with unique needs or high chat volumes, building your own is now viable—and can offer better data control and long-term savings.
• Distribution Layer: Treat Agent Builder and Chat Kit as new ways to reach customers and empower internal teams.
• First Step: Define your use-case, prepare data, and map which agent roles and external tools (MCPs) you’ll need. Start simple, iterate, and expand.

Memorable Closing:

"I'll keep it very real... there's still dependency where you gotta have some technical knowledge... but the multi-agent workflow is very interesting... and the visual drag and drop tool is a low barrier entry for non-technical people."
— Amir (20:40)

Resources Mentioned:

• 30+ Startup Ideas Database by Greg Isenberg
• OpenAI Platform: platform.openai.com
• Guide to MCP (see show notes)

This summary provides a thorough walkthrough of the episode’s practical demos, key insights, and actionable advice for founders and product leaders exploring generative AI for startup workflows and customer experience.