OpenAI launched an agent builder that the company hopes will eliminate fragmented tools and make it easier for enterprises to utilize OpenAI’s system to create agents.

AgentKit, announced during OpenAI’s DevDay in San Francisco, enables developers and enterprises to build agents and add chat capabilities in one place, potentially competing with platforms like Zapier.

By offering a more streamlined way to create agents, OpenAI advances further into becoming a full-stack application provider.

“Until now, building agents meant juggling fragmented tools—complex orchestration with no versioning, custom connectors, manual eval pipelines, prompt tuning, and weeks of frontend work before launch,” the company said in a blog post.

AgentKit includes:

• Agent Builder, which is a visual canvas where devs can see what they’ve created and versioning multi-agent workflows

• Connector Registry is a central area for admins to manage connections across OpenAI products. A Global Admin console will be a prerequisite to using this feature.

• ChatKit enables users to integrate chat-based agents into their user interfaces.

Eventually, OpenAI said it will build a standalone Workflows API and add agent deployment tabs to ChatGPT.

OpenAI also expanded evaluation for agents, adding capabilities such as datasets with automated graders and annotations, trace grading that runs end-to-end assessments of workflows, automated prompt optimization, and support for third-party agent measurement tools.

Developers can access some features of AgentKit, but OpenAI is gradually rolling out additional features, such as Agent Builder. Currently, Agent Builder is available in beta, while ChatKit and new evaluation capabilities are generally available. Connector Registry “is beginning its beta rollout to some API and ChatGPT Enterprise and Edu users.

OpenAI said pricing for AgentKit tools will be included in the standard API model pricing.

Agent Builder

To clarify, many agents are built using OpenAI’s models; however, enterprises often access GPT-5 through other platforms to create their own agents. However, AgentKit brings enterprises more into its ecosystem, ensuring they don’t need to tap other platforms as often.

Demonstrated during DevDay, the company stated that Agent Builder is ideal for rapid iteration. It also provides developers with visibility into how the agents are working.

During the demo, an OpenAI developer made an agent that reads the DevDay agenda and suggests panels to watch. It took her just under eight minutes.

Other model providers saw the importance of offering developer toolkits to build agents to entice enterprises to use more of their tools. Google came out with its Agent Development Kit in April, expanding multi-agent system building “in under 100 lines of code.” Microsoft, which runs the popular agent framework AutoGen, announced it is bringing agent creation to one place with its new Agent Framework.

OpenAI customers, such as the fintech company Ramp, stated in a blog post that its teams were able to build a procurement agent in a few hours instead of months.

“Agent Builder transformed what once took months of complex orchestration, custom code, and manual optimizations into just a couple of hours. The visual canvas keeps product, legal, and engineering on the same page, slashing iteration cycles by 70% and getting an agent live in two sprints rather than two quarters,” Ramp said.

AgentKit’s Connector Registry would also enable enterprises to manage and maintain data across workspaces, consolidating data sources into a single panel that spans both ChatGPT and the API. It will have pre-built connectors to Dropbox, Google Drive, SharePoint and Microsoft Teams. It also supports third-party MCP servers.

Another capability of Agent Builder is Guardrails, an open-source safety layer that protects against the leakage of personally identifiable information (PII), jailbreaks, and unintended or malicious behavior.

Bringing more chat

Since most agentic interactions involve chat, it makes sense to simplify the process for developers to set up chat interfaces and connect them with the agents they’ve just built.

“Deploying chat UIs for agents can be surprisingly complex—handling streaming responses, managing threads, showing the model thinking and designing engaging in-chat experiences,” OpenAI said.

The company said ChatKit makes it simple to embed chat agents on platforms and embed these into apps or websites.

However, some OpenAI competitors have begun thinking beyond the chatbot and want to offer agentic interactions that feel more seamless. Google’s asynchronous coding agent, Jules, has introduced a new feature that enables users to interact with the agent through the command-line interface, eliminating the need to open a chat window.

Responses

The response to AgentKit has mainly been positive, with some developers noting that while it simplifies agent building, it doesn’t mean that everyone can now build agents.

Several developers view Agent Kit not as a Zapier killer, but rather as a tool that complements the pipeline.

Zapier debuted a no-code tool for building AI agents and bots, called Zapier Central, in 2024.

OpenAI's annual conference for third-party developers, DevDay, kicked off with a bang today as co-founder and CEO Sam Altman announced a new "Apps SDK" that makes it "possible to build apps inside of ChatGPT," including paid apps, which companies can charge users for using OpenAI's recently unveiled Agentic Commerce Protocol (ACP).

In other words, instead of launching apps one-by-one on your phone, computer, or on the web — now you can do all that without ever leaving ChatGPT.

This feature allows the user to log-into their accounts on those external apps and bring all their information back into ChatGPT, and use the apps very similarly to how they already do outside of the chatbot, but now with the ability to ask ChatGPT to perform certain actions, analyze content, or go beyond what each app could offer on its own.

You can direct Canva to make you slides based on a text description, ask Zillow for home listings in a certain area fitting certain requirements, or ask Coursera about a specific lesson's content while dit plays on video, all from within ChatGPT — with many other apps also already offering their own connections (see below).

"This will enable a new generation of apps that are interactive, adaptive and personalized, that you can chat with," Altman said.

While the Apps SDK is available today in preview, OpenAI said it would not begin accepting new apps within ChatGPT or allow them to charge users until "later this year."

ChatGPT in-line app access is already rolling out to ChatGPT Free, Plus, Go and Pro users — outside of the European Union only for now — with Business, Enterprise, and Education tiers expected to receive access to the apps later this year.

Built atop common MCP standard

Built on the open source standard Model Context Protocol (MCP) introduced by rival Anthropic nearly a year ago, the Apps SDK gives third-party developers working independently or on behalf of enterprises large and small to connect selected data, "trigger actions, and render a fully interactive UI [user interface]" Altman explained during his introductory keynote speech.

The Apps SDK includes a "talking to apps" feature that allows ChatGPT and the underlying GPT-5 or other "o-series" models piloting it underneath to obtain updated context from the third-party app or service, so the model "always knows about exactly what you're user is interacting with," according to another presenter and OpenAI engineer, Alexi Christakis.

Developers can build apps that:

• appear inline in chat as lightweight cards or carousels

• expand to fullscreen for immersive tasks like maps, menus, or slides

• use picture-in-picture for live sessions such as video, games, or quizzes

Each mode is designed to preserve ChatGPT’s minimal, conversational flow while adding interactivity and brand presence.

Early integrations with Coursera, Canva, Zillow and more...

Christakis showed off early integrations of external apps built atop the Apps SDK, including ones from e-learning company Coursera, cloud design software company Canva, and real estate listings and agent connections search engine, Zillow.

Altman also announced Apps SDK integrations with additional partners not demoed officially during the keynote including: Booking.com, Expedia, Figma and Spotify and in documentation, said more upcoming partners are on deck: AllTrails, Peloton, OpenTable, Target, theFork, and Uber, representing lifestyle, commerce, and productivity categories.

The Coursera demo included an example of how the user onboards to the external app, including a new login screen for the app (Coursera) that appears within the ChatGPT chat interface, activated simply by a text prompt from the user asking: "Coursera can you teach me something about machine learning"?

Once logged in, the app launched within the chat interface, "in line" and can render anything from the web, including interactive elements like video.

Christakis explained and showed the Apps SDK also supports "picture-in-picture" and "fullscreen" views, allowing the user to choose how to interact with it.

When playing a Coursera video that appeared, he showed that it automatically pinned the video to the top of the screen so the user could keep watching it even as they continued to have a back-and-forth dialog in text with ChatGPT in the typical input/output prompts and responses below.

Users can then ask ChatGPT about content appearing in the video without specifying exactly what was said, as the Agents SDK pipes the information on the backend, server-side, from the connected app to the underlying ChatGPT AI model. So "can you explain more about what they're saying right now" will automatically surface the relevant portion of the video and provide that to the underlying AI model for it to analyze and respond to through text.

In another example, Christakis opened an older, existing ChatGPT conversation he'd had about his siblings' dog walking business and resumed the conversation by asking another third-party app, Canva, to generate a poster using one of ChatGPT's recommended business names, "Walk This Wag," along with specific guidance about font choice ("sans serif") and overall coloration and style ("bright and colorful.")

Instead of the user manually having to go and add all those specific elements to a Canva template, ChatGPT went and issued the commands and performed the actions on behalf of the user in the background.

After a few minutes, ChatGPT responded with several poster designs generated directly within the Canva app, but displayed them all in the user's ChatGPT chat session where they could see, review, enlarge and provide feedback or ask for adjustments on all of them.

Christakis then asked for ChatGPT to turn one of the slides into an entire slide deck so the founders of the dog walking business could present it to investors, which did it in the background over several minutes while he presented a final integrated app, Zillow.

He started a new chat session and asked a simple question: "based on our conversations, what would be a good city to expand the dog walking business."

Using ChatGPT's optional memory feature, it referenced the dog walk conversation and suggested Pittsburgh, which Christakis used as a chance to type in "Zillow" and "show me some homes for sale there," which called up an interactive map from Zillow with homes for sale and prices listed and hover-over animations, all in-line within ChatGPT.

Clicking a specific home also opened a fullscreen view with "most of the Zillow experience," entirely without leaving ChatGPT, including the ability to request home tours and contact agents and filtering by bedrooms and other qualities like outdoor space. ChatGPT pulls up the requested filtered Zillow search as well as provides a text-based response in-line explaining what it did and why.

The user can then ask follow-up questions about the specific property — such as "how close is it to a dog park?" — or compare it to other properties, all within ChatGPT.

It can also use apps in conjunction with its Search function, searching the web to compare the app information (in this case, Zillow) with other sources.

Safety, privacy, and developer standards

OpenAI emphasized that apps must comply with strict privacy, safety, and content standards to be listed in the ChatGPT directory. Apps must:

• serve a clear and valuable purpose

• be predictable and reliable in behavior

• be safe for general audiences, including teens aged 13–17

• respect user privacy and limit data collection to only what’s necessary

Every app must also include a clear, published privacy policy, obtain user consent before connecting, and identify any actions that modify external data (e.g., posting, sending, uploading).

Apps violating OpenAI’s usage policies, crashing frequently, or misrepresenting their capabilities may be removed at any time. Developers must submit from verified accounts, provide customer support contacts, and maintain their apps for stability and compliance.

OpenAI also published developer design guidelines, outlining how apps should look, sound, and behave. They must follow ChatGPT’s visual system — including consistent color palettes, typography, spacing, and iconography — and maintain accessibility standards such as alt text and readable contrast ratios.

Partners can show brand logos and accent colors but not alter ChatGPT’s core interface or use promotional language. Apps should remain “conversational, intelligent, simple, responsive, and accessible,” according to the documentation.

A new conversational app ecosystem

By opening ChatGPT to third-party apps and payments, OpenAI is taking a major step toward transforming ChatGPT from a chatbot into a full-fledged AI operating system — one that combines conversational intelligence, rich interfaces, and embedded commerce.

For developers, that means direct access to over 800 million ChatGPT users, who can discover apps “at the right time” through natural conversation — whether planning trips, learning, or shopping.

For users, it means a new generation of apps you can chat with — where a single interface helps you book a flight, design a slide deck, or learn a new skill without ever leaving ChatGPT.

As OpenAI put it: “This is just the start of apps in ChatGPT, bringing new utility to users and new opportunities for developers.”

There remain a few big questions, namely: 1. what happens to all the data from those third-party apps as they interface with ChatGPT and its users...does OpenAI get access to it and can it train upon it? 2. What happens to OpenAI's once much-hyped GPT Store, which had been in the past promoted as a way for third-party creators and developers to create custom, task-specific versions of ChatGPT and make money on them through a usage-based revenue share model?

We've asked the company about both issues and will update when we hear back.

When I write about the cognitive migration now underway, brought about by the rapid advance of gen AI, I do so from the perspective of someone who has spent four decades in the technology industry. My own journey runs from coding business applications in Fortran and COBOL to systems analysis and design, IT project management, enterprise systems consulting, computing hardware sales and technology industry communications. All of it has been centered in the U.S., although I have collaborated with colleagues and clients across Europe and Asia.

My writing carries an American, tech-industry vantage point, although I make attempts to see a broader perspective. Perhaps that is fitting, since much of the frontier development of AI remains clustered in Silicon Valley, Seattle, Boston and a handful of other Western hubs. But how does this migration look beyond America’s borders? For millions in the Global South, cognitive migration is less about the loss of white-collar prestige and more about the chance to leapfrog into new opportunities.

This divide is visible in the data. The 2025 Edelman Trust Barometer found that fewer than one in three Americans feel comfortable with businesses using AI, while in India, Indonesia and Nigeria nearly two-thirds express comfort. In the West, AI may be perceived to threaten job loss and displacement, and this view may be warranted. A study by the International Monetary Fund (IMF) found that 60% of jobs in advanced economies are exposed to the impact of AI due to the prevalence of cognitive-task-oriented jobs. The Wall Street Journal quoted Ford CEO Jim Farley: “AI will leave a lot of white-collar people behind.”

In the Global South, however, AI is often perceived as an opportunity to improve education, strengthen healthcare, modernize agriculture and drive development. One analysis argues that for the Global South, “AI holds tangible promise for nations historically excluded from the benefits of previous industrial revolutions.” Perhaps this explains the findings reported by Academia.edu that Global North newspapers publish more negative AI headlines, while Global South outlets emphasize opportunity. 

Yet the story is not so simple. Even where the potential for advancement is emphasized, there is often also worry about loss of work, ethics, algorithmic bias, access and technical capacity. As with earlier waves of globalization, gains and risks will be distributed unevenly.

AI as opportunity

There is a strong positive narrative around AI in the Global South, with many hopeful stories and promising results. In Nigeria, a World Bank-funded after-school tutoring program that used AI to tailor lessons to individual students produced striking results with nearly two years of learning gains in just six weeks. For communities with few qualified teachers, such gains are not incremental improvements. They can transform futures. 

Healthcare applications provide comparable stories. In India, Boston Consulting Group reports that AI diagnostic tools are being deployed in rural clinics with few doctors, offering screenings for conditions such as breast cancer or tuberculosis that might otherwise go undetected. These tools extend the reach of limited health resources and help detect conditions before it is too late.

The use of AI in agriculture also shows promise. In Kenya, the PlantVillage Nuru app developed with Penn State University uses AI to detect crop diseases through farmers’ smartphones, equipping them to spot and treat threats to their harvests early. For households that depend on subsistence farming, such tools can mean the difference between security and scarcity.

Yet many of these breakthroughs rely on Northern institutions, creating benefits but also exposing a fragile dependency. When outside funding or partnerships end, local efforts can stall. In this sense, leapfrogging risks being built on borrowed foundations.

Taken together, these examples illustrate why many in the Global South see AI as a chance to transform trajectories rather than repeat old patterns. Yet optimism tells only part of the story. Alongside these gains are deep structural challenges that complicate the journey, reminding us that this migration, like all others, carries benefits that include hidden costs.

Barriers to progress

Research also shows that AI adoption across the Global South is hindered by persistent gaps in infrastructure, data, skills and governance. Availability of reliable electricity and broadband remains uneven, local datasets are often scarce or biased and many countries face shortages of trained professionals to develop and oversee AI systems. 

Without strong regulatory frameworks, societies are also more exposed to privacy risks, exploitative labor practices and algorithmic bias. These realities mean that while AI holds promise as a development pathway, it can also deepen inequality if its benefits concentrate in urban centers and among elites, while leaving rural communities behind.

So why do surveys of trust show higher comfort with AI in the Global South than in the West? One explanation lies in expectations. In the U.S. and Europe, AI is often perceived as a threat to stable jobs and established professions. In Nigeria, India or Indonesia, by contrast, it is more likely to be framed as a tool for closing persistent gaps. 

Media narratives often reinforce the divergence in expectations. In the West, headlines emphasize automation anxiety, while in the Global South, AI is more often described as a development pathway. Add to this the fact that many people in the Global South report higher levels of trust in institutions overall, and the disparity begins to make sense. 

The same technology intersects with different baselines, diverse needs, distinct cultures and different stories, which shape whether AI is welcomed with suspicion or with hope. Yet beyond these perceptual differences lie material realities that complicate the optimistic narrative, particularly in how global AI development distributes both its benefits and its burdens.

Hidden costs

Every migration carries costs alongside gains, and the story of AI in the Global South is no different. While the overall AI narrative in the Global South leans positive, many celebrated breakthroughs depend on large workforces doing essential yet hidden tasks. Data annotation and content review are indispensable to the global AI economy, but the work is repetitive, emotionally taxing and poorly paid relative to the value it creates.

Other sectors face pressure from a different direction. In India and the Philippines, business process outsourcing and call centers employ millions of workers who support global clients. These roles depend on language, routine cognitive tasks and customer service, the very areas where AI chatbots and automated platforms are advancing fastest.

The shift is not immediate, but workers in these industries are already questioning whether the migration now underway will carry them forward or leave them behind. Is cognitive migration a single global phenomenon, or are we witnessing multiple migrations that only appear connected?

Many routes, shared destination

Is this the same cognitive migration unfolding everywhere, or are there separate journeys? On the surface, the story looks divided. In the U.S. and Europe, professionals worry about displacement from stable careers and a risk to their lifestyles. In India, Nigeria and Indonesia, AI is often presented as a chance to accelerate development and fill long-standing gaps. These appear to be distinct migrations.

Yet, the reality is more entangled. The story of AI in the Global South is not simply one of catching up, just as the story in the West is not simply one of decline. Migration is never only progress or only loss. It is both, with something gained and something given up. For teachers in Nigeria, the gain may be students advancing at unprecedented speed. For call center workers in India, the loss may be jobs once thought secure. For farmers in Kenya, the gain may be healthier crops and steadier harvests. For professionals in Europe or the United States, the loss may be careers reshaped or diminished by automation.

This variability in experience is not because AI technology is somehow different in one area or another, but because the lived experiences are diverse. The same systems can seem empowering in one place and threatening in another. 

An uneven passage

What lies ahead is still uncertain. But if migration teaches anything, it is that adaptation requires not only resilience but imagination. The task is not to deny what is lost or to celebrate only what is gained, but to recognize both and design wisely for what comes next.

This migration is not unfolding along a single path. It is fractured and revealing. The starting points differ, the routes are uneven, and the burdens are not equally shared. In the Global South, AI is often seen as a lever for progress, not a threat to status. But beneath the promise lie the same risks we face everywhere, including extraction without investment, automation without inclusion, innovation without safeguards and deployment without trust. These are not side effects. They are signals. If we ignore them, the cognitive future will be one more story written by the few for the few. 

As Indonesian policy advisor Tuhu Nugraha has argued in Modern Diplomacy: “As concerns rise globally about AI’s unchecked development potentially destabilizing economies or social cohesion, models from the Global South that emphasize inclusion, trust and reflection can help mitigate those risks before they explode into global backlash.” His warning reinforces the point that inclusion and trust must be part of the design of AI advancement and not assumed.

If we pay attention, the Global South may offer not just caution but clarity. The choice is not only whether to design wisely, but whose experience we treat as essential when we do. Because in the end, cognitive migration is not regional. It is a worldwide passage, and how we navigate it together will shape not just the future of AI, but the future of being human.

Gary Grossman is EVP of technology practice at Edelman.

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