Are data center architects ready to rethink their approach and embrace smarter, network-driven strategies to meet the demands of the future?

In a packed hall at Fort Mason Center in San Francisco, against a backdrop of the Golden Gate Bridge, OpenAI CEO Sam Altman laid out a bold vision to remake the digital world. The company that brought generative AI to the mainstream with a simple chatbot is now building the foundations for its next act: a comprehensive computing platform designed to move beyond the screen and browser, with legendary designer Jony Ive enlisted to help shape its physical form.

At its third annual DevDay, OpenAI unveiled a suite of tools that signals a strategic pivot from a model provider to a full-fledged ecosystem. The message was clear: the era of simply asking an AI questions is over. The future is about commanding AI to perform complex tasks, build software autonomously, and live inside every application, a transition Altman framed as moving from "systems that you can ask anything to, to systems that you can ask to do anything for you." 

The day’s announcements were a three-pronged assault on the status quo, targeting how users interact with software, how developers build it, and how businesses deploy intelligent agents. But it was the sessions held behind closed doors, away from the public livestream, that revealed the true scope of OpenAI’s ambition — a future that includes new hardware, a relentless pursuit of computational power, and a philosophical quest to redefine our relationship with technology.

From chatbot to operating system: The new 'App Store'

The centerpiece of the public-facing keynote was the transformation of ChatGPT itself. With the new Apps SDK, OpenAI is turning its wildly popular chatbot into a dynamic, interactive platform, effectively an operating system where developers can build and distribute their own applications.

“Today, we're going to open up ChatGPT for developers to build real apps inside of ChatGPT,” Altman announced during the keynote presentation to applause. “This will enable a new generation of apps that are interactive, adaptive and personalized, that you can chat with.”

Live demonstrations showcased apps from partners like Coursera, Canva, and Zillow running seamlessly within a chat conversation. A user could watch a machine learning lecture, ask ChatGPT to explain a concept in real-time, and then use Canva to generate a poster based on the conversation, all without leaving the chat interface. The apps can render rich, interactive UIs, even going full-screen to offer a complete experience, like exploring a Zillow map of homes.

For developers, this represents a powerful new distribution channel. “When you build with the Apps SDK, your apps can reach hundreds of millions of chat users,” Altman said, highlighting a direct path to a massive user base that has grown to over 800 million weekly active users. 

In a private press conference later, Nick Turley, head of ChatGPT, elaborated on the grander vision. "We never meant to build a chatbot," he stated. "When we set out to make ChatGPT, we meant to build a super assistant and we got a little sidetracked. And one of the tragedies of getting a little sidetracked is that we built a great chatbot, but we are the first ones to say that not all software needs to be a chatbot, not all interaction with the commercial world needs to be a chatbot."

Turley emphasized that while OpenAI is excited about natural language interfaces, "the interface really needs to evolve, which is why you see so much UI in the demos today. In fact, you can even go full screen and chat is in the background." He described a future where users might "start your day in ChatGPT, just because it kind of has become the de facto entry point into the commercial web and into a lot of software," but clarified that "our incentive is not to keep you in. Our product is to allow other people to build amazing businesses on top and to evolve the form factor of software."

The rise of the agents: Building the 'do anything' AI

If apps are about bringing the world into ChatGPT, the new "Agent Kit" is about sending AI out into the world to get things done. OpenAI is providing a complete "set of building blocks... to help you take agents from prototype to production," Altman explained in his keynote. 

Agent Kit is an integrated development environment for creating autonomous AI workers. It features a visual canvas to design complex workflows, an embeddable chat interface ("Chat Kit") for deploying agents in any app, and a sophisticated evaluation suite to measure and improve performance.

A compelling demo from financial operations platform Ramp showed how Agent Kit was used to build a procurement agent. An employee could simply type, "I need five more ChatGPT business seats," and the agent would parse the request, check it against company expense policies, find vendor details, and prepare a virtual credit card for the purchase — a process that once took weeks now completed in minutes. 

This push into agents is a direct response to a growing enterprise need to move beyond AI as a simple information retrieval tool and toward AI as a productivity engine that automates complex business processes. Brad Lightcap, OpenAI's COO, noted that for enterprise adoption, "you needed this kind of shift to more agentic AI that could actually do things for you, versus just respond with text outputs." 

The future of code and the Jony Ive bBombshell

Perhaps the most profound shift is occurring in software development itself. Codex, OpenAI's AI coding agent, has graduated from a research preview to a full-fledged product, now powered by a specialized version of the new GPT-5 model. It is, as one speaker put it, "a teammate that understands your context." 

The capabilities are staggering. Developers can now assign Codex tasks directly from Slack, and the agent can autonomously write code, create pull requests, and even review other engineers' work on GitHub. A live demo showed Codex taking a simple photo of a whiteboard sketch and turning it into a fully functional, beautifully designed mobile app screen. Another demo showed an app that could "self-evolve," reprogramming itself in real-time based on a user's natural language request. 

But the day's biggest surprise came in a closing fireside chat, which was not livestreamed, between Altman and Jony Ive, the iconic former chief design officer of Apple. The two revealed they have been collaborating for three years on a new family of AI-centric hardware.

Ive, whose design philosophy shaped the iPhone, iMac, and Apple Watch, said his creative team’s purpose "became clear" with the launch of ChatGPT. He argued that our current relationship with technology is broken and that AI presents an opportunity for a fundamental reset.

“I think it would be absurd to assume that you could have technology that is this breathtaking, delivered to us through legacy products, products that are decades old,” Ive said. “I see it as a chance to use this most remarkable capability to full-on address a lot of the overwhelm and despair that people feel right now.”

While details of the devices remain secret, Ive spoke of his motivation in deeply human terms. “We love our species, and we want to be useful. We think that humanity deserves much better than humanity generally is given,” he said. He emphasized the importance of "care" in the design process, stating, "We sense when people have cared... you sense carelessness. You sense when somebody does not care about you, they care about money and schedule." 

This collaboration confirms that OpenAI's ambitions are not confined to the cloud; it is actively exploring the physical interface through which humanity will interact with its powerful new intelligence.

The Unquenchable Thirst for Compute

Underpinning this entire platform strategy is a single, overwhelming constraint: the availability of computing power. In both the private press conference and the un-streamed Developer State of the Union, OpenAI’s leadership returned to this theme again and again.

“The degree to which we are all constrained by compute... Everyone is just so constrained on being able to offer the services at the scale required to get the revenue that at this point, we're quite confident we can push it pretty far,” Altman told reporters. He added that even with massive new hardware partnerships with AMD and others, "we'll be saying the same thing again. We're so convinced... There's so much more demand." 

This explains the company’s aggressive, multi-billion-dollar investment in infrastructure. When asked about profitability, Altman was candid that the company is in a phase of "investment and growth." He invoked a famous quote from Walt Disney, paraphrasing, "We make more money so we can make more movies." For OpenAI, the "movies" are ever-more-powerful AI models.

Greg Brockman, OpenAI’s President, put the ultimate goal in stark economic terms during the Developer State of the Union. "AI is going to become, probably in the not too distant future, the fundamental driver of economic growth," he said. "Asking ‘How much compute do you want?’ is a little bit like asking how much workforce do you want? The answer is, you can always get more out of more." 

As the day concluded and developers mingled at the reception, the scale of OpenAI's project came into focus. Fueled by new models like the powerful GPT-5 Pro and the stunning Sora 2 video generator, the company is no longer just building AI. It is building the world where AI will live — a world of intelligent apps, autonomous agents, and new physical devices, betting that in the near future, intelligence itself will be the ultimate platform.

Some of the largest providers of large language models (LLMs) have sought to move beyond multimodal chatbots — extending their models out into "agents" that can actually take more actions on behalf of the user across websites. Recall OpenAI's ChatGPT Agent (formerly known as "Operator") and Anthropic's Computer Use, both released over the last two years.

Now, Google is getting into that same game as well. Today, the search giant's DeepMind AI lab subsidiary unveiled a new, fine-tuned and custom-trained version of its powerful Gemini 2.5 Pro LLM known as "Gemini 2.5 Pro Computer Use," which can use a virtual browser to surf the web on your behalf, retrieve information, fill out forms, and even take actions on websites — all from a user's single text prompt.

"These are early days, but the model’s ability to interact with the web – like scrolling, filling forms + navigating dropdowns – is an important next step in building general-purpose agents," said Google CEO Sundar Pichai, as part of a longer statement on the social network, X.

The model is not available for consumers directly from Google, though.

Instead, Google partnered with another company, Browserbase, founded by former Twilio engineer Paul Klein in early 2024, which offers virtual "headless" web browser specifically for use by AI agents and applications. (A "headless" browser is one that doesn't require a graphical user interface, or GUI, to navigate the web, though in this case and others, Browserbase does show a graphical representation for the user).

Users can demo the new Gemini 2.5 Computer Use model directly on Browserbase here and even compare it side-by-side with the older, rival offerings from OpenAI and Anthropic in a new "Browser Arena" launched by the startup (though only one additional model can be selected alongside Gemini at a time).

For AI builders and developers, it's being made as a raw, albeit propreitary LLM through the Gemini API in Google AI Studio for rapid prototyping, and Google Cloud's Vertex AI model selector and applications building platform.

The new offering builds on the capabilities of Gemini 2.5 Pro, released back in March 2025 but which has been updated significantly several times since then, with a specific focus on enabling AI agents to perform direct interactions with user interfaces, including browsers and mobile applications.

Overall, it appears Gemini 2.5 Computer Use is designed to let developers create agents that can complete interface-driven tasks autonomously — such as clicking, typing, scrolling, filling out forms, and navigating behind login screens.

Rather than relying solely on APIs or structured inputs, this model allows AI systems to interact with software visually and functionally, much like a human would.

Brief User Hands-On Tests

In my brief, unscientific initial hands-on tests on the Browserbase website, Gemini 2.5 Computer Use successfully navigate to Taylor Swift's official website as instructed and provided me a summary of what was being sold or promoted at the top — a special edition of her newest album, "The Life of A Showgirl."

In another test, I asked Gemini 2.5 Computer Use to search Amazon for highly rated and well-reviewed solar lights I could stake into my back yard, and I was delighted to watch as it successfully completed a Google Search Captcha designed to weed out non-human users ("Select all the boxes with a motorcycle.") It did so in a matter of seconds.

However, once it got through there, it stalled and was unable to complete the task, despite serving up a "task competed" message.

I should also note here that while the ChatGPT agent from OpenAI and Anthropic's Claude can create and edit local files — such as PowerPoint presentations, spreadsheets, or text documents — on the user’s behalf, Gemini 2.5 Computer Use does not currently offer direct file system access or native file creation capabilities.

Instead, it is designed to control and navigate web and mobile user interfaces through actions like clicking, typing, and scrolling. Its output is limited to suggested UI actions or chatbot-style text responses; any structured output like a document or file must be handled separately by the developer, often through custom code or third-party integrations.

Performance Benchmarks

Google says Gemini 2.5 Computer Use has demonstrated leading results in multiple interface control benchmarks, particularly when compared to other major AI systems including Claude Sonnet and OpenAI’s agent-based models.

Evaluations were conducted via Browserbase and Google’s own testing.

Some highlights include:

• Online-Mind2Web (Browserbase): 65.7% for Gemini 2.5 vs. 61.0% (Claude Sonnet 4) and 44.3% (OpenAI Agent)

• WebVoyager (Browserbase): 79.9% for Gemini 2.5 vs. 69.4% (Claude Sonnet 4) and 61.0% (OpenAI Agent)

• AndroidWorld (DeepMind): 69.7% for Gemini 2.5 vs. 62.1% (Claude Sonnet 4); OpenAI's model could not be measured due to lack of access

• OSWorld: Currently not supported by Gemini 2.5; top competitor result was 61.4%

In addition to strong accuracy, Google reports that the model operates at lower latency than other browser control solutions — a key factor in production use cases like UI automation and testing.

How It Works

Agents powered by the Computer Use model operate within an interaction loop. They receive:

• A user task prompt

• A screenshot of the interface

• A history of past actions

The model analyzes this input and produces a recommended UI action, such as clicking a button or typing into a field.

If needed, it can request confirmation from the end user for riskier tasks, such as making a purchase.

Once the action is executed, the interface state is updated and a new screenshot is sent back to the model. The loop continues until the task is completed or halted due to an error or a safety decision.

The model uses a specialized tool called computer_use, and it can be integrated into custom environments using tools like Playwright or via the Browserbase demo sandbox.

Use Cases and Adoption

According to Google, teams internally and externally have already started using the model across several domains:

• Google’s payments platform team reports that Gemini 2.5 Computer Use successfully recovers over 60% of failed test executions, reducing a major source of engineering inefficiencies.

• Autotab, a third-party AI agent platform, said the model outperformed others on complex data parsing tasks, boosting performance by up to 18% in their hardest evaluations.

• Poke.com, a proactive AI assistant provider, noted that the Gemini model often operates 50% faster than competing solutions during interface interactions.

The model is also being used in Google’s own product development efforts, including in Project Mariner, the Firebase Testing Agent, and AI Mode in Search.

Safety Measures

Because this model directly controls software interfaces, Google emphasizes a multi-layered approach to safety:

• A per-step safety service inspects every proposed action before execution.

• Developers can define system-level instructions to block or require confirmation for specific actions.

• The model includes built-in safeguards to avoid actions that might compromise security or violate Google’s prohibited use policies.

For example, if the model encounters a CAPTCHA, it will generate an action to click the checkbox but flag it as requiring user confirmation, ensuring the system does not proceed without human oversight.

Technical Capabilities

The model supports a wide array of built-in UI actions such as:

• click_at, type_text_at, scroll_document, drag_and_drop, and more

• User-defined functions can be added to extend its reach to mobile or custom environments

• Screen coordinates are normalized (0–1000 scale) and translated back to pixel dimensions during execution

It accepts image and text input and outputs text responses or function calls to perform tasks. The recommended screen resolution for optimal results is 1440x900, though it can work with other sizes.

API Pricing Remains Almost Identical to Gemini 2.5 Pro

The pricing for Gemini 2.5 Computer Use aligns closely with the standard Gemini 2.5 Pro model. Both follow the same per-token billing structure: input tokens are priced at $1.25 per one million tokens for prompts under 200,000 tokens, and $2.50 per million tokens for prompts longer than that.

Output tokens follow a similar split, priced at $10.00 per million for smaller responses and $15.00 for larger ones.

Where the models diverge is in availability and additional features.

Gemini 2.5 Pro includes a free tier that allows developers to use the model at no cost, with no explicit token cap published, though usage may be subject to rate limits or quota constraints depending on the platform (e.g. Google AI Studio).

This free access includes both input and output tokens. Once developers exceed their allotted quota or switch to the paid tier, standard per-token pricing applies.

In contrast, Gemini 2.5 Computer Use is available exclusively through the paid tier. There is no free access currently offered for this model, and all usage incurs token-based charges from the outset.

Feature-wise, Gemini 2.5 Pro supports optional capabilities like context caching (starting at $0.31 per million tokens) and grounding with Google Search (free for up to 1,500 requests per day, then $35 per 1,000 additional requests). These are not available for Computer Use at this time.

Another distinction is in data handling: output from the Computer Use model is not used to improve Google products in the paid tier, while free-tier usage of Gemini 2.5 Pro contributes to model improvement unless explicitly opted out.

Overall, developers can expect similar token-based costs across both models, but they should consider tier access, included capabilities, and data use policies when deciding which model fits their needs.

For more than a decade, conversational AI has promised human-like assistants that can do more than chat. Yet even as large language models (LLMs) like ChatGPT, Gemini, and Claude learn to reason, explain, and code, one critical category of interaction remains largely unsolved — reliably completing tasks for people outside of chat.

Even the best AI models score only in the 30th percentile on Terminal-Bench Hard, a third-party benchmark designed to evaluate the performance of AI agents on completing a variety of browser-based tasks, far below the reliability demanded by most enterprises and users. And task-specific benchmarks like TAU-Bench airline, which measures the reliability of AI agents on finding and booking flights on behalf of a user, also don't have much higher pass rates, with only 56% for the top performing agents and models (Claude 3.7 Sonnet) — meaning the agent fails nearly half the time.

New York City-based Augmented Intelligence (AUI) Inc., co-founded by Ohad Elhelo and Ori Cohen, believes it has finally come with a solution to boost AI agent reliability to a level where most enterprises can trust they will do as instructed, reliably.

The company’s new foundation model, called Apollo-1 — which remains in preview with early testers now but is close to an impending general release — is built on a principle it calls stateful neuro-symbolic reasoning.

It's a hybrid architecture championed by even LLM skeptics like Gary Marcus, designed to guarantee consistent, policy-compliant outcomes in every customer interaction.

“Conversational AI is essentially two halves,” said Elhelo in a recent interview with VentureBeat. “The first half — open-ended dialogue — is handled beautifully by LLMs. They’re designed for creative or exploratory use cases. The other half is task-oriented dialogue, where there’s always a specific goal behind the conversation. That half has remained unsolved because it requires certainty.”

AUI defines certainty as the difference between an agent that “probably” performs a task and one that almost “always” does.

For example, on TAU-Bench Airline, it performs at a staggering 92.5% pass rate, leaving all the other current competitors far behind in the dust — according to benchmarks shared with VentureBeat and posted on AUI's website.

Elhelo offered simple examples: a bank that must enforce ID verification for refunds over $200, or an airline that must always offer a business-class upgrade before economy.

“Those aren’t preferences,” he said. “They’re requirements. And no purely generative approach can deliver that kind of behavioral certainty.”

AUI and its work on improving reliability was previously covered by subscription news outlet The Information, but has not received widespread coverage in publicly accessible media — until now.

From Pattern Matching to Predictable Action

The team argues that transformer models, by design, can’t meet that bar. Large language models generate plausible text, not guaranteed behavior. “When you tell an LLM to always offer insurance before payment, it might — usually,” Elhelo said. “Configure Apollo-1 with that rule, and it will — every time.”

That distinction, he said, stems from the architecture itself. Transformers predict the next token in a sequence. Apollo-1, by contrast, predicts the next action in a conversation, operating on what AUI calls a typed symbolic state.

Cohen explained the idea in more technical terms. “Neuro-symbolic means we’re merging the two dominant paradigms,” he said. “The symbolic layer gives you structure — it knows what an intent, an entity, and a parameter are — while the neural layer gives you language fluency. The neuro-symbolic reasoner sits between them. It’s a different kind of brain for dialogue.”

Where transformers treat every output as text generation, Apollo-1 runs a closed reasoning loop: an encoder translates natural language into a symbolic state, a state machine maintains that state, a decision engine determines the next action, a planner executes it, and a decoder turns the result back into language. “The process is iterative,” Cohen said. “It loops until the task is done. That’s how you get determinism instead of probability.”

A Foundation Model for Task Execution

Unlike traditional chatbots or bespoke automation systems, Apollo-1 is meant to serve as a foundation model for task-oriented dialogue — a single, domain-agnostic system that can be configured for banking, travel, retail, or insurance through what AUI calls a System Prompt.

“The System Prompt isn’t a configuration file,” Elhelo said. “It’s a behavioral contract. You define exactly how your agent must behave in situations of interest, and Apollo-1 guarantees those behaviors will execute.”

Organizations can use the prompt to encode symbolic slots — intents, parameters, and policies — as well as tool boundaries and state-dependent rules.

A food delivery app, for example, might enforce “if allergy mentioned, always inform the restaurant,” while a telecom provider might define “after three failed payment attempts, suspend service.” In both cases, the behavior executes deterministically, not statistically.

Eight Years in the Making

AUI’s path to Apollo-1 began in 2017, when the team started encoding millions of real task-oriented conversations handled by a 60,000-person human agent workforce.

That work led to a symbolic language capable of separating procedural knowledge — steps, constraints, and flows — from descriptive knowledge like entities and attributes.

“The insight was that task-oriented dialogue has universal procedural patterns,” said Elhelo. “Food delivery, claims processing, and order management all share similar structures. Once you model that explicitly, you can compute over it deterministically.”

From there, the company built the neuro-symbolic reasoner — a system that uses the symbolic state to decide what happens next rather than guessing through token prediction.

Benchmarks suggest the architecture makes a measurable difference.

In AUI’s own evaluations, Apollo-1 achieved over 90 percent task completion on the τ-Bench-Airline benchmark, compared with 60 percent for Claude-4.

It completed 83 percent of live booking chats on Google Flights versus 22 percent for Gemini 2.5-Flash, and 91 percent of retail scenarios on Amazon versus 17 percent for Rufus.

“These aren’t incremental improvements,” said Cohen. “They’re order-of-magnitude reliability differences.”

A Complement, Not a Competitor

AUI isn’t pitching Apollo-1 as a replacement for large language models, but as their necessary counterpart. In Elhelo’s words: “Transformers optimize for creative probability. Apollo-1 optimizes for behavioral certainty. Together, they form the complete spectrum of conversational AI.”

The model is already running in limited pilots with undisclosed Fortune 500 companies across sectors including finance, travel, and retail.

AUI has also confirmed a strategic partnership with Google and plans for general availability in November 2025, when it will open APIs, release full documentation, and add voice and image capabilities. Interested potential customers and partners can sign up to receive more information when it becomes available on AUI's website form.

Until then, the company is keeping details under wraps. When asked about what comes next, Elhelo smiled. “Let’s just say we’re preparing an announcement,” he said. “Soon.”

Toward Conversations That Act

For all its technical sophistication, Apollo-1’s pitch is simple: make AI that businesses can trust to act — not just talk. “We’re on a mission to democratize access to AI that works,” Cohen said near the end of the interview.

Whether Apollo-1 becomes the new standard for task-oriented dialogue remains to be seen. But if AUI’s architecture performs as promised, the long-standing divide between chatbots that sound human and agents that reliably do human work may finally start to close.

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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.