By skyforbes 
Artificial Intelligence has finally reached the stage where it’s no longer simply text creation — choreographing actions, workflows, and interactive digital experiences.
The new generation of OpenAI developer tech — ChatGPT AgentKit and the ChatGPT Apps SDK — represents a shift in paradigm.
For the first time we can do more than just let AIs scan our pages and decide whether they link or just take our information. We can soon provide ways of changing how our data is displayed and also use AI to improve our internal workflows.
This article covers what the latest tools presented just a few days ago by Openai (the company behind Chatgpt) from an experienced technical business implementation point to see what this means for organizations and developers — and, more importantly, where we believe there’s the most potential, implicit danger, and practical use cases for you and your product/company.
So let’s take a look at the two different tools, what they are, what they do and how you could use them.
This article is part of a 2 part series:
- Part 1: How do AgentKit & Chatgpt Apps work and what’s their potential (this article here)
- Part 2: What are potential use cases for AgentKit & ChatGPT App SDK
ChatGPT AgentKit
The ChatGPT AgentKit turns ChatGPT into a workflow engine. Think of it as Zapier meets a smart assistant.
It allows developers (and even users who know their way around tech) to connect ChatGPT to external data sources and APIs, so the assistant can perform meaningful actions rather than just giving answers.
What Is It?
Imagine you’re in a large company with fragmented knowledge across Google Drive, SharePoint, Confluence, internal chats, and email threads.
Instead of manually searching for information, an AgentKit-powered ChatGPT could:
- Search across internal documents for relevant information.
- If no document exists, analyze your organization’s structure to determine who’s likely responsible.
- Automatically generate an email or internal chat message requesting the missing information.
It’s a pleasant blend of automation, reasoning, and human touch — atop the same ChatGPT interface people are already accustomed to.
This is of course just one example. Want more inspiration? Check our other dedicated article ChatGPT AgentBuilder and App SDK Case Studies and Idea Scenarios .
Or another example:
What if you have a Support ticket system via email and want to make sure all support tickets are assigned to the correct project boards.
The workflow below would classify the incoming support requests, check if it is very urgent based on the mail content (if so -> notify emergency team) and otherwise add it to the correct board, depending on whether we can find the correct board, client or fall back.
What Works Well — and What Doesn’t
What it works well for:
- Information retrieval and summarization from disparate data silos.
- Labor-intensive workflows — calendaring, summarizing, sending status reports.
- Cross-functional collaboration with context and nuance at stake.
Where it stumbles:
- Real-time system updates or workflows that require urgent precise timing.
- Highly regulated data environments where every API call must be dealt with.
- Routine data operations that still need backend infrastructure.
- Workflows with a lot of requests will get expensive because of ChatGPT calls
AgentKit’s strength is availability rather than pure muscle. It allows organizations to prototype and iterate workflows quickly without sitting around waiting for IT or developers to hard-code every step.
Alternatives and Comparisons
Before AgentKit, options such as Zapier, n8n, or Make (Integromat) dominated the automation scene. They’re great at connecting services but bad at reasoning.
AgentKit combines automation with intelligence — it doesn’t just “do” something; it understands why you’re doing it.
For instance, while Zapier can send an email when a file changes, ChatGPT AgentKit can first check whether that file is relevant to a current project or even summarize the update before sending it.
Our Take: Cool, Accessible, but Not (Yet) a Revolution
We think it’s incredible how OpenAI managed to popularize what Zapier and n8n popularized — but made it easier and more context-aware.
The promise isn’t in replacing what exists, but in improving it.
Businesses can now enable employees to change and create workflows without technical re-deployment — an innovation that liquidates digital landscapes.
Drawbacks? There must be:
- It’s ChatGPT-only (even though built on the open Model Context Protocol (MCP), which could allow future interoperability).
- Offering access to sensitive information introduces risk and compliance overhead.
- It’s in an early stage — get ready for shifting APIs and patchy coverage.
However, for most organizations, AgentKit is the first reachable step toward a deeply conversational workflow system.
ChatGPT Apps SDK
Where AgentKit is all about doing, the ChatGPT Apps SDK is all about showing. The SDK allows developers to build interactive, mini web applications in-line directly as part of ChatGPT conversations.
What It Is
Apps turn dry text answers into rich, visual, and contextual experiences. Instead of reading text responses, users can interact with data tables, visual widgets, and custom components in the chat itself.
Nowadays, the SDK is rolling out gradually — with broader public availability soon to follow. OpenAI’s ambition is to see ChatGPT not only as a chat app, but an entire app ecosystem.
(very smart move of them … It’s how android and ios cemented in their positions as the branch standards, that no one can copy)
A Live Example: Our “Donation Cause finder” Widget for impactory.org
One of our in-house proof-of-concepts was a custom-built ChatGPT app for one of our clients impactory.org.
This project is a wonderful platform, that makes donating to good causes extremly simpel.
ChatGPT does not just give a generic canned text reply. Rather, it displays a custom widget with suggested causes for a selected category — pulled directly from our APIs.
But importantly: The api itself is abstracted behind our MCP server, so Openai only gets the final HTML widget, but not the API Request or raw data itself! This is very important, because you often don’t want to give away your data sources!!
It’s a small feature with dramatic ramifications: ChatGPT is a new distribution and interaction channel for content — one where you can dictate how your data is presented and linked.
How Apps Are Triggered — Key Clarification
Completely contrary to some misperceptions, apps cannot be triggered automatically without the user’s consent.
- Users must first turn on the app or give it a specific name.
- ChatGPT may recommend suitable apps, but only if the user already has them and consents. (this will enfold the biggest potential later, hopefully)
- All apps must display a permissions screen and privacy policy before divulging any data.
- Access also varies by region and subscription level.
Therefore, whereas ChatGPT can recommend applications based on intent, developers can’t completely dictate when their app appears.
Why This Matters to Business
Search engines are falling behind as humans use AI assistants for quick answers.
Websites are fighting to maintain organic traffic as people no longer click links — they get what they’re looking for inside ChatGPT.
With ChatGPT Apps, you can regain visibility by offering structured, interactive answers within the chatbot — and direct users straight to your site, app, or product for further interaction.
SEO 2.0:
You’re no longer optimizing content for Google’s crawler.
You’re optimizing triggers and actions for AI assistants.
You get to decide how your data appears, what users can do with it, and where the conversation will lead.
Smart Tip: The “Trigger Strategy”
You can’t prevent triggering altogether, but you can influence it.
Design your app to react to the most appropriate intents — those matching what users are most likely to ask ChatGPT about your business.
If people are likely to ask “best insurance for freelancers,” you can build an app to trigger when triggered by name or context, offering your calculator or comparison tool.
This is not just reactionary SEO — it’s proactive AI experience design.
The Caveats
- Users must activate applications and include user opt-in. Applications will not turn themselves on.
- ChatGPT’s UI may limit how aggressively you can match users to outside pages.
- APIs must be latency-designed, quota-ed, and stable.
- The ecosystem remains in infancy stages — expect fragmentation among providers (Gemini, Anthropic, etc.) prior to standards solidifying.
- The SDK is rooted in MCP, an open protocol that could allow cross-platform interoperability, but widespread adoption remains unknown.
Our take? We applaud the innovation but hope for open interoperability across AI platforms. An open standard could transform AI-native web experiences into the next major frontier.
Genius Tips and Tricks
These are some tips in the real world that most people forget:
- Versioning and backward compatibility — the API and SDK contracts can evolve. Code defensively.
- Security and privacy — apps must explicitly define OAuth scopes and handle user data with care.
- Performance — reduce latency; the user expects instant responses.
- Fallback design — have your app degrade nicely if the user’s area or plan doesn’t support apps.
- Monitoring — logging, analytics, and tracing are needed to monitor usage and debug.
- Discovery and ranking — apps will soon be fighting for attention. Metadata and relevance will matter.
Cross-Link: Real-World Case Studies & Possibilities
If you’d like to view how all this is made possible, explore our detailed ChatGPT AgentBuilder and App SDK Case Studies and Idea Scenarios.
There, we dissect real-life scenarios like:
- Smart CRM Assistants that bring together AgentKit and Apps SDK to show aggregated customer profiles, compose personalized messages, and even extract relevant legal clauses.
- Smart Company Assistant Advanced helper within ChatGPT for companies, allowing to query and search not only for generic solutions, but internal resources, files, knowledge and the web in one tool.
- Public GEO Applications that help pages or brands regain SEO visibility by revealing structured content and directing users to their websites.
- Internal Employee Onboarding Agents that answer queries, create accounts automatically, and automate requests for templates or access.
- Smart Troubleshooter Assistants that route internal issues, fetch known workarounds, and send to the right person based on the company’s org structure.
Each of these shows how AgentKit and the Apps SDK can be combined to build end-to-end, AI-driven workflows — inside and outside your organization.
Try It Yourself
If you want to try these tools out yourself:
- AgentKit: Sign up at OpenAI, verify your organization, and request access. A few features are still in preview and need approval.
- ChatGPT Apps SDK: Visit the OpenAI Developer Docs to explore example repositories and follow setup guides.
Part 2
If you are interest.
There is a second part to this blog article:
What are potential use cases for AgentKit & ChatGPT App SDK
In Summary
We’re entering a new phase of AI — one where ChatGPT evolves from “answer engine” to action platform.
- AgentKit empowers organizations to automate workflows in a human-friendly, conversational way.
- ChatGPT Apps SDK enables businesses to build native experiences inside AI conversations.
We’re already experimenting with both technologies for innovative clients — from media outlets to data-driven enterprises — helping them discover how these tools can fit their business models and user experiences.
If you’re curious about how AgentKit or ChatGPT Apps could work for your organization, now’s the perfect time to explore.
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Thanks for reading 🙂