ChatGPT: From Chatbot to Application Platform

Users of ChatGPT see it as a conversational agent; a chatbot. You open a text box, type a question, and receive an answer. It's powerful, sometimes remarkably so, but the interaction model is simple: prompt in, response out. That mental model is about to shift dramatically.

The announcements at OpenAI's Dev Day 2025 signal something bigger than incremental improvements. ChatGPT is evolving from an interface into a substrate. From a place where you talk to AI, to a place where you build with AI. The new Apps SDK and AgentKit are turning ChatGPT into a runtime environment for applications and autonomous workflows.

This matters. For developers, it means a new platform to target. For product builders, it opens up distribution channels and capabilities that didn't exist before. For businesses, it offers infrastructure to connect AI directly into business workflows without rebuilding everything from scratch.

The thesis is straightforward: ChatGPT is no longer just a chatbot. It's becoming a platform where apps live, agents run, and users accomplish complex tasks without ever leaving the conversation.

Dev Day Recap & Strategic Context

High-Level Themes

OpenAI framed Dev Day 2025 around a single principle: making it easier to build with AI. Not just smarter models, but better bridges between models and production systems. The scale metrics are staggering. Developer growth has accelerated. Token volumes continue to climb. The user base is massive. These aren't just metrics; they're signals of an ecosystem ready for a platform play.

Why This Is More Than an Incremental Update

OpenAI has introduced building blocks before. GPTs allowed custom configurations. Plugins enabled tool use. But those felt like experiments, not pillars. The Apps SDK and AgentKit feel different. They're cohesive, documented, and designed for production use.

There's a risk here, too. Turning ChatGPT into a development platform means competing not just on model quality, but on developer experience, ecosystem health, and platform governance. Rivals are circling. Anthropic, Google, and others are building their own app and agent ecosystems. OpenAI is betting that ChatGPT's existing user base and brand recognition give it a structural advantage.

Embedding Apps in ChatGPT: The New SDK & Interaction Model

What "Apps Inside ChatGPT" Means

Imagine you're chatting with ChatGPT about music. Mid-conversation, it suggests: "Want me to create a Spotify playlist for this?" You click yes. A small interface appears inline, authenticated against your Spotify account. You tweak a few settings, hit create, and the playlist is live. You never left ChatGPT.

This is the new paradigm. Apps are embedded directly into the conversation. Examples from the keynote included:

• Spotify: playlist creation from chat context

• Zillow: real estate search with inline results

• Canva: design generation inside the interface

The key innovation is contextual invocation. ChatGPT can recommend an app when it detects relevance. You're not hunting through a menu or copying data between tabs. The app appears when it's useful, with consent and data connection flows handled cleanly.

The Apps SDK: Architecture, Features, and Developer Experience

The Apps SDK builds on Model Context Protocol (MCP), a standard for connecting AI models to external data and tools. It provides:

• Actions: Define what your app can do (search, create, update, etc.)

• UI embedding: Render custom interfaces inside ChatGPT

• Authentication: Handle OAuth and login flows

• Consent management: Let users control data sharing

• Monetization hooks: Support checkouts, subscriptions, and payments

Preview access is live. Developers can start building, and OpenAI plans to open a broader submission pipeline and app directory in the coming months.

Building a simple app looks like this:

1.    Define your actions and data schema

2.    Implement handlers (usually API calls to your backend)

3.    Configure UI components for inline rendering

4.    Submit for review and listing

Implications of App Embedding

The strategic move here is retention. By keeping users inside ChatGPT, OpenAI positions itself as the primary interface layer. You don't get redirected to Spotify's web app or Zillow's site. Everything happens in chat.

This is powerful for OpenAI, but it raises questions:

• Discovery: How will smaller apps compete in a crowded directory?

• Data sharing: What guardrails exist around sensitive information?

• UI constraints: How much control do developers have over presentation?

Tradeoffs are inevitable. Developers gain access to ChatGPT's massive user base, but they cede control over the experience. For many, that's a worthwhile exchange.

From Chat to Actions: Agents & AgentKit

The Case for Agents

Chat-based tool invocation works well for simple tasks. "Search for flights" or "summarize this document" can be handled in a single step. But what about complex workflows?

• Booking a multi-leg trip with preferences, budget constraints, and calendar conflicts

• Conducting market research that spans dozens of sources, synthesizing insights, and generating reports

• Managing customer support tickets that require escalation, data lookup, and follow-up

These tasks require agents: systems that can reason, plan, invoke tools, evaluate results, and iterate. They're autonomous, multi-step, and context-aware.

AgentKit is OpenAI's toolkit for building these systems.

Introducing AgentKit

AgentKit is not a single tool; it's a unified platform with multiple components:

1. Agent Builder

A visual, drag-and-drop canvas for constructing workflows. Think of it as a flowchart editor where each node is a tool, a model call, or a decision point. Non-technical users can prototype agents. Developers can export to code.

2. Connector Registry

A registry of pre-built connectors for external data sources: databases, APIs, SaaS platforms, file systems. Instead of writing integration code from scratch, you pick from a library and configure.

3. ChatKit

An embeddable chat/agent UI for integrating agents into third-party applications. If you're building an internal tool or customer-facing product, ChatKit gives you a ready-made interface.

4. Evals

Enhanced evaluation features:

• Datasets: Curated test cases for agent workflows

• Trace grading: Automatic assessment of agent behavior

• Prompt optimization: Tools to iteratively improve prompts

• Third-party model support: Test agents across different models

The Agents SDK

Underneath AgentKit sits the Agents SDK (open-source, Python). It provides primitives for:

• Agents: The core abstraction for autonomous systems

• Handoffs: Passing control between agents or humans

• Guardrails: Safety constraints and validation logic

• Sessions: Managing state across multi-turn interactions

• Tracing: Logging and debugging agent behavior

Developers can use the SDK directly for maximum control, or use AgentKit for faster prototyping.

Developer Experience: Building Agents

Constructing an agent looks something like this:

1.    Define the goal: What should the agent accomplish?

2.    Select tools: What APIs, connectors, or data sources are needed?

3.    Configure the workflow: Use Agent Builder or write code with the SDK

4.    Test and evaluate: Run against datasets, review traces, iterate

5.    Deploy: Embed with ChatKit or integrate into ChatGPT

Debugging is critical. Agent workflows can fail in subtle ways. Tracing and evals help catch issues before production.

Use Cases and Early Examples

Box

Box is building an enterprise agent that combines internal document search with external web search. Using AgentKit and the Connector Registry, they've prototyped an assistant that can answer questions like "What did our VP of Sales say about Q3 targets, and how does that compare to industry benchmarks?"

Coinbase

Coinbase is experimenting with agents that interact with on-chain APIs. Using the Agents SDK and AgentKit, they're building workflows for trading, monitoring, and portfolio management.

Hypothetical Examples

• Personal assistant: Plan a trip, book flights, reserve hotels, add to calendar, send confirmations

• Research agent: Gather data from 50+ sources, synthesize insights, generate a report

• Customer support: Triage tickets, look up account info, escalate or resolve autonomously

• Finance workflows: Aggregate expenses, flag anomalies, generate invoices

Synthesis & Positioning: ChatGPT as Platform

How Apps and Agents Complement Each Other

Apps handle user-initiated, structured interactions. You invoke an app when you know what you want: create a playlist, search for homes, design a logo.

Agents handle longer-running, autonomous workflows. You set a goal and the agent figures out the steps: plan a vacation, research a market, manage a project.

The boundaries blur. An app might invoke an agent to handle a complex sub-task. An agent might invoke an app to render a UI or complete a transaction. The two pillars work together.

Platform Mechanics: Monetization, Distribution, Governance

Monetization

The SDK includes hooks for checkouts and subscriptions. Developers can charge for app usage directly within ChatGPT. OpenAI likely takes a revenue share, though details are still emerging.

Distribution

An app directory and marketplace are coming. Discovery will be key. Apps that integrate well, solve real problems, and maintain quality will rise. Others will struggle.

Governance

Security, privacy, and auditability are non-negotiable. OpenAI will need strong guardrails to prevent abuse, protect user data, and handle edge cases. Expect approval processes, rate limits, and monitoring.

Risks, Challenges, and Caveats

Building on a new platform always carries risk:

• Debugging complexity: Agent workflows are hard to troubleshoot in production

• Ecosystem lock-in: Developers invest in OpenAI's stack, reducing portability

• Safety and unintended behavior: Agents can do unexpected things, especially in high-stakes domains

• Scaling and latency: Can the infrastructure handle millions of concurrent agents?

• Regulatory constraints: Data access and privacy laws vary by region

These aren't dealbreakers, but they require careful navigation.

What It Means for Developers & Product Builders

How to Get Started

1.    Sign up for preview access to the Apps SDK and AgentKit

2.    Explore samples: OpenAI provides templates and tutorials

3.    Build something small: Start with an MVP app or lightweight agent prototype

Project Ideas & Roadmap

• MVP app: A mini-widget inside ChatGPT (e.g., weather, stock lookup, quick calculator)

• Lightweight agent: Multi-step data aggregator (e.g., pull info from 3 APIs, synthesize, display)

• Hybrid flow: App that hands off to an agent for complex tasks

Skills and Infrastructure to Invest In

• Tool wrapping: Packaging APIs and connectors for agent use

• Prompt engineering: Crafting effective instructions for agents

• Agent orchestration: Chaining tools, managing state, handling failures

• Observability: Tracing, logging, debugging workflows

• Security and governance: Monitoring, guardrails, compliance

Competitive Mindshare & Strategic Bets

Not every domain will benefit equally. Early winners might include:

• Productivity tools: Scheduling, task management, research

• eCommerce: Shopping assistants, product search, checkout flows

• Finance: Portfolio management, expense tracking, trading

• Customer support: Triage, knowledge lookup, ticket resolution

• Content creation: Writing, design, media generation

AI-first products will shift from standalone apps to embedded experiences. The architecture changes: instead of building your own interface and routing to a model, you build inside ChatGPT and leverage its distribution.

Looking Ahead: What to Watch & Predictions

Ecosystem Growth & Marketplace

As submission pipelines open, expect rapid growth in the app directory. Developer incentives and revenue share models will shape which apps thrive. Quality control and curation will be critical.

Enhanced Agent Capabilities

Agents will get better at reasoning, memory, and coordination. Expect:

• Longer-term memory across sessions

• Multi-agent collaboration (agents working together)

• Improved evaluation and monitoring tools

Infrastructure and Performance

Platform maturity requires:

• Lower latency for real-time interactions

• Scaling support for high-traffic apps

• Fallback logic and offline modes

• Better error handling and recovery

Cross-Modal and Multi-Modal Integration

Vision, audio, and video agents are coming. Imagine:

• An agent that watches a video, transcribes it, and generates a summary

• A design agent that critiques mockups and suggests improvements

• A browser agent that navigates websites and extracts data

Ethical, Safety, and Governance Evolution

As agents become more autonomous, guardrails become more important:

• Approval workflows: Requiring human sign-off for high-stakes actions

• Audit logs: Transparent records of agent behavior

• Compliance frameworks: Meeting privacy and regulatory requirements

Conclusion

ChatGPT is no longer just a place to ask questions. It's becoming a platform where applications live, agents operate, and users accomplish complex tasks without leaving the interface.

The Dev Day 2025 announcements matter because they provide concrete building blocks. The Apps SDK and AgentKit are not vaporware. They're real, documented, and available for experimentation. Early adopters are already building.

For developers and product builders, this is an invitation. The ecosystem is forming. The platform is open. The opportunity is significant.

What now? Start small. Build an app. Prototype an agent. See what works. The platform will evolve, but the direction is clear: ChatGPT is shifting from chatbot to enterprise platform.