OpenAI’s latest DevDay was all about infrastructure. Here’s what shipped:
Apps in ChatGPT
- New Apps SDK built on Model Context Protocol (MCP)
- Conversational apps that surface contextually in ChatGPT
- 800+ million potential users for developers
- Launch partners: Booking.com, Canva, Coursera, Expedia, Figma, Spotify, Zillow
- Open standard that can run anywhere
AgentKit
- Agent Builder: visual canvas for multi-agent workflows with versioning
- Connector Registry: centralized admin panel for data sources across workspaces
- ChatKit: embeddable chat UI toolkit for agentic experiences
- New evals capabilities: datasets, trace grading, automated prompt optimization
Codex (General Availability)
- Slack integration for delegating tasks to @Codex
- Codex SDK for embedding the agent in custom workflows
- New admin tools: environment controls, monitoring, analytics
- 10x daily usage growth since August
- 40+ trillion tokens served by GPT-5-Codex in three weeks
- 70% more pull requests merged per week at OpenAI
API Updates
- GPT-5 Pro and Sora 2 now available in API
- Reinforcement fine-tuning beta for GPT-5
- Third-party model support in Evals platform
That’s a lot. Let’s dig into what this actually means and why some of these decisions should make you uncomfortable.
Apps in ChatGPT
The Apps SDK is OpenAI’s attempt to solve a problem that’s plagued AI interfaces since GPT-3: how do you let the model do something beyond generating text?
The traditional answer has been “function calling” or “tool use,” where the model outputs structured JSON that your code interprets. It works, but it’s clunky. You need to build the entire UI layer yourself, handle state management, deal with streaming responses, and figure out how to present complex interactions in a chat interface.
Apps in ChatGPT aims to simplify. Instead of ChatGPT calling your API and you building a separate frontend, you build the interface directly into the conversation. When someone asks “Spotify, make a playlist for my party this Friday,” the Spotify app appears inline with an interactive interface. You can browse, adjust, and interact without leaving ChatGPT.
This is built on the Model Context Protocol, Anthropic’s open standard that OpenAI is fully embracing. In theory, apps built with the Apps SDK can run anywhere that adopts MCP. In practice, we’ll see how much that actually happens.
AgentKit
AgentKit is OpenAI’s answer to a pain point in agent development: building complex workflows is messy. You need orchestration logic, tool management, evaluation pipelines, safety guardrails, and a frontend. Each piece involves different tools, different frameworks, and a lot of custom glue code.
Agent Builder provides a visual canvas for composing multi-agent workflows (think n8n). You drag and drop nodes for agents, tools, guardrails, and conditional logic. You can configure evals inline, version your workflows, and preview runs before deployment.
Visual workflow builders have a long history in enterprise software, and they all face the same problem: they’re great for demos and prototyping, then you hit edge cases that don’t fit the abstraction.
The real test isn’t whether you can build a simple agent quickly. It’s whether you can maintain and evolve a complex agent system over time as requirements change, edge cases emerge, and your understanding of the problem deepens. Visual builders tend to break down at that point because the abstraction becomes a constraint.