OpenAI AgentKit Explained Why Agent Builders Fail in Production | Dev in the Details
Building AI agents has never been easier.Shipping them safely to production has never been harder.
In this episode of Dev in the Details, I sit down with Predibase co-founder and CTO Travis Addair to break down what OpenAI’s AgentKit launch actually means for teams trying to deploy agents in the real world.
We unpack:
* What OpenAI announced at Developer Day — and what AgentKit really is
* Why agent builders, drag-and-drop UIs, and “5-minute agents” break down in production
* How AgentKit compares to RPA tools (UiPath), automation platforms (Zapier), and open-source frameworks like n8n
* The d…
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Chapters (15)
Why AI agents are easy to build but hard to ship
1:05
OpenAI Developer Day recap & AgentKit overview
2:10
What AgentKit includes: canvas, connectors, evals
3:20
Are AI agents really new? RPA, Zapier, and history
5:10
Who does OpenAI AgentKit compete with?
6:40
AgentKit vs UiPath, Zapier, and n8n
8:35
Why AgentKit feels more like a chatbot builder
10:10
Why RPA and agent platforms are on a collision course
12:00
Build vs buy for AI agents in the enterprise
14:20
Why no-code ML tools historically failed
16:00
Prediction: no-code vs low-code vs custom agents
18:40
Where real enterprise value from agents is created
21:00
What agent builders usually ignore
23:30
Why production agents are still “just software”
26:10
CI/CD, versioning, and trus
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