The company said the tools will help data centers optimize AI workloads by boosting speed and efficiency while better handling intense AI traffic.

These seven MCP servers bridge the gap between AI capabilities and real-world infrastructure management, turning complex workflows into simple natural language commands.

Echelon, an artificial intelligence startup that automates enterprise software implementations, emerged from stealth mode today with $4.75 million in seed funding led by Bain Capital Ventures, targeting a fundamental shift in how companies deploy and maintain critical business systems.

The San Francisco-based company has developed AI agents specifically trained to handle end-to-end ServiceNow implementations — complex enterprise software deployments that traditionally require months of work by offshore consulting teams and cost companies millions of dollars annually.

"The biggest barrier to digital transformation isn't technology — it's the time it takes to implement it," said Rahul Kayala, Echelon's founder and CEO, who previously worked at AI-powered IT company Moveworks. "AI agents are eliminating that constraint entirely, allowing enterprises to experiment, iterate, and deploy platform changes with unprecedented speed."

The announcement signals a potential disruption to the $1.5 trillion global IT services market, where companies like Accenture, Deloitte, and Capgemini have long dominated through labor-intensive consulting models that Echelon argues are becoming obsolete in the age of artificial intelligence.

Why ServiceNow deployments take months and cost millions

ServiceNow, a cloud-based platform used by enterprises to manage IT services, human resources, and business workflows, has become critical infrastructure for large organizations. However, implementing and customizing the platform typically requires specialized expertise that most companies lack internally.

The complexity stems from ServiceNow's vast customization capabilities. Organizations often need hundreds of "catalog items" — digital forms and workflows for employee requests — each requiring specific configurations, approval processes, and integrations with existing systems. According to Echelon's research, these implementations frequently stretch far beyond planned timelines due to technical complexity and communication bottlenecks between business stakeholders and development teams.

"What starts out simple often turns into weeks of effort once the actual work begins," the company noted in its analysis of common implementation challenges. "A basic request form turns out to be five requests stuffed into one. We had catalog items with 50+ variables, 10 or more UI policies, all connected. Update one field, and something else would break."

The traditional solution involves hiring offshore development teams or expensive consultants, creating what Echelon describes as a problematic cycle: "One question here, one delay there, and suddenly you're weeks behind."

How AI agents replace expensive offshore consulting teams

Echelon's approach replaces human consultants with AI agents trained by elite ServiceNow experts from top consulting firms. These agents can analyze business requirements, ask clarifying questions in real-time, and automatically generate complete ServiceNow configurations including forms, workflows, testing scenarios, and documentation.

The technology delivers a significant advancement from general-purpose AI tools. Rather than providing generic code suggestions, Echelon's agents understand ServiceNow's specific architecture, best practices, and common integration patterns. They can identify gaps in requirements and propose solutions that align with enterprise governance standards.

"Instead of routing every piece of input through five people, the business process owner directly uploaded their requirements," Kayala explained, describing a recent customer implementation. "The AI developer analyzes it and asks follow-up questions like: 'I see a process flow with 3 branches, but only 2 triggers. Should there be a 3rd?' The kinds of things a seasoned developer would ask. With AI, these questions came instantly."

Early customers report dramatic time savings. One financial services company saw a service catalog migration project that was projected to take six months completed in six weeks using Echelon's AI agents.

What makes Echelon's AI different from coding assistants

Echelon's technology addresses several technical challenges that have prevented broader AI adoption in enterprise software implementation. The agents are trained not just on ServiceNow's technical capabilities but on the accumulated expertise of senior consultants who understand complex enterprise requirements, governance frameworks, and integration patterns.

This approach differs from general-purpose AI coding assistants like GitHub Copilot, which provide syntax suggestions but lack domain-specific expertise. Echelon's agents understand ServiceNow's data models, security frameworks, and upgrade considerations—knowledge typically acquired through years of consulting experience.

The company's training methodology involves elite ServiceNow experts from consulting firms like Accenture and specialized ServiceNow partner Thirdera. This embedded expertise enables the AI to handle complex requirements and edge cases that typically require senior consultant intervention.

The real challenge isn't teaching AI to write code — it's capturing the intuitive expertise that separates junior developers from seasoned architects. Senior ServiceNow consultants instinctively know which customizations will break during upgrades and how simple requests spiral into complex integration problems. This institutional knowledge creates a far more defensible moat than general-purpose coding assistants can offer.

The $1.5 trillion consulting market faces disruption

Echelon's emergence reflects broader trends reshaping the enterprise software market. As companies accelerate digital transformation initiatives, the traditional consulting model increasingly appears inadequate for the speed and scale required.

ServiceNow itself has grown rapidly, reporting over $10.98 billion in annual revenue in 2024, and $12.06 billion for the trailing twelve months ending June 30, 2025, as organizations continue to digitize more business processes. However, this growth has created a persistent talent shortage, with demand for skilled ServiceNow professionals — particularly those with AI expertise — significantly outpacing supply.

The startup's approach could fundamentally alter the economics of enterprise software implementation. Traditional consulting engagements often involve large teams working for months, with costs scaling linearly with project complexity. AI agents, by contrast, can handle multiple projects simultaneously and apply learned knowledge across customers.

Rak Garg, the Bain Capital Ventures partner who led Echelon's funding round, sees this as part of a larger shift toward AI-powered professional services. "We see the same trend with other BCV companies like Prophet Security, which automates security operations, and Crosby, which automates legal services for startups. AI is quickly becoming the delivery layer across multiple functions."

Scaling beyond ServiceNow while maintaining enterprise reliability

Despite early success, Echelon faces significant challenges in scaling its approach. Enterprise customers prioritize reliability above speed, and any AI-generated configurations must meet strict security and compliance requirements.

"Inertia is the biggest risk," Garg acknowledged. "IT systems shouldn't ever go down, and companies lose thousands of man-hours of productivity with every outage. Proving reliability at scale, and building on repeatable results will be critical for Echelon."

The company plans to expand beyond ServiceNow to other enterprise platforms including SAP, Salesforce, and Workday — each creating substantial additional market opportunities. However, each platform requires developing new domain expertise and training models on platform-specific best practices.

Echelon also faces potential competition from established consulting firms that are developing their own AI capabilities. However, Garg views these firms as potential partners rather than competitors, noting that many have already approached Echelon about collaboration opportunities.

"They know that AI is shifting their business model in real-time," he said. "Customers are placing immense pricing pressure on larger firms and asking hard questions, and these firms can use Echelon agents to accelerate their projects."

How AI agents could reshape all professional services

Echelon's funding and emergence from stealth marks a significant milestone in the application of AI to professional services. Unlike consumer AI applications that primarily enhance individual productivity, enterprise AI agents like Echelon's directly replace skilled labor at scale.

The company's approach — training AI systems on expert knowledge rather than just technical documentation — could serve as a model for automating other complex professional services. Legal research, financial analysis, and technical consulting all involve similar patterns of applying specialized expertise to unique customer requirements.

For enterprise customers, the promise extends beyond cost savings to strategic agility. Organizations that can rapidly implement and modify business processes gain competitive advantages in markets where customer expectations and regulatory requirements change frequently.

As Kayala noted, "This unlocks a completely different approach to business agility and competitive advantage."

The implications extend far beyond ServiceNow implementations. If AI agents can master the intricacies of enterprise software deployment—one of the most complex and relationship-dependent areas of professional services — few knowledge work domains may remain immune to automation.

The question isn't whether AI will transform professional services, but how quickly human expertise can be converted into autonomous digital workers that never sleep, never leave for competitors, and get smarter with every project they complete.

OpenAI’s annual developer conference on Monday was a spectacle of ambitious AI product launches, from an app store for ChatGPT to a stunning video-generation API that brought creative concepts to life. But for the enterprises and technical leaders watching closely, the most consequential announcement was the quiet general availability of Codex, the company's AI software engineer. This release signals a profound shift in how software—and by extension, modern business—is built.

While other announcements captured the public’s imagination, the production-ready release of Codex, supercharged by a new specialized model and a suite of enterprise-grade tools, is the engine behind OpenAI’s entire vision. It is the tool that builds the tools, the proven agent in a world buzzing with agentic potential, and the clearest articulation of the company's strategy to win the enterprise.

The general availability of Codex moves it from a "research preview" to a fully supported product, complete with a new software development kit (SDK), a Slack integration, and administrative controls for security and monitoring.This transition declares that Codex is ready for mission-critical work inside the world’s largest companies.

"We think this is the best time in history to be a builder; it has never been faster to go from idea to product," said OpenAI CEO Sam Altman during the opening keynote presentation. "Software used to take months or years to build. You saw that it can take minutes now to build with AI." 

That acceleration is not theoretical. It's a reality born from OpenAI’s own internal use — a massive "dogfooding" effort that serves as the ultimate case study for enterprise customers.

Inside GPT-5-Codex: The AI model that codes autonomously for hours and drives 70% productivity gains

At the heart of the Codex upgrade is GPT-5-Codex, a version of OpenAI's latest flagship model that has been "purposely trained for Codex and agentic coding." The new model is designed to function as an autonomous teammate, moving far beyond simple code autocompletion.

"I personally like to think about it as a little bit like a human teammate," explained Tibo Sottiaux, an OpenAI engineer, during a technical session on Codex. "You can pair a program with it on your computer, you can delegate to it, or as you'll see, you can give it a job without explicit prompting."

This new model enables "adaptive thinking," allowing it to dynamically adjust the time and computational effort spent on a task based on its complexity.For simple requests, it's fast and efficient, but for complex refactoring projects, it can work for hours.

One engineer during the technical session noted, "I've seen the GPT-5-Codex model work for over seven hours productively... on a marathon session." This capability to handle long-running, complex tasks is a significant leap beyond the simple, single-shot interactions that define most AI coding assistants.

The results inside OpenAI have been dramatic. The company reported that 92% of its technical staff now uses Codex daily, and those engineers complete 70% more pull requests (a measure of code contribution) each week. Usage has surged tenfold since August. 

"When we as a team see the stats, it feels great," Sottiaux shared. "But even better is being at lunch with someone who then goes 'Hey I use Codex all the time. Here's a cool thing that I do with it. Do you want to hear about it?'" 

How OpenAI uses Codex to build its own AI products and catch hundreds of bugs daily

Perhaps the most compelling argument for Codex’s importance is that it is the foundational layer upon which OpenAI’s other flashy announcements were built. During the DevDay event, the company showcased custom-built arcade games and a dynamic, AI-powered website for the conference itself, all developed using Codex.

In one session, engineers demonstrated how they built "Storyboard," a custom creative tool for the film industry, in just 48 hours during an internal hackathon. "We decided to test Codex, our coding agent... we would send tasks to Codex in between meetings. We really easily reviewed and merged PRs into production, which Codex even allowed us to do from our phones," said Allison August, a solutions engineering leader at OpenAI. 

This reveals a critical insight: the rapid innovation showcased at DevDay is a direct result of the productivity flywheel created by Codex. The AI is a core part of the manufacturing process for all other AI products.

A key enterprise-focused feature is the new, more robust code review capability. OpenAI said it "purposely trained GPT-5-Codex to be great at ultra thorough code review," enabling it to explore dependencies and validate a programmer's intent against the actual implementation to find high-quality bugs.Internally, nearly every pull request at OpenAI is now reviewed by Codex, catching hundreds of issues daily before they reach a human reviewer.

"It saves you time, you ship with more confidence," Sottiaux said. "There's nothing worse than finding a bug after we actually ship the feature." 

Why enterprise software teams are choosing Codex over GitHub Copilot for mission-critical development

The maturation of Codex is central to OpenAI’s broader strategy to conquer the enterprise market, a move essential to justifying its massive valuation and unprecedented compute expenditures. During a press conference, CEO Sam Altman confirmed the strategic shift.

"The models are there now, and you should expect a huge focus from us on really winning enterprises with amazing products, starting here," Altman said during a private press conference. 

OpenAI President and Co-founder Greg Brockman immediately added, "And you can see it already with Codex, which I think has been just an incredible success and has really grown super fast." 

For technical decision-makers, the message is clear. While consumer-facing agents that book dinner reservations are still finding their footing, Codex is a proven enterprise agent delivering substantial ROI today. Companies like Cisco have already rolled out Codex to their engineering organizations, cutting code review times by 50% and reducing project timelines from weeks to days.

With the new Codex SDK, companies can now embed this agentic power directly into their own custom workflows, such as automating fixes in a CI/CD pipeline or even creating self-evolving applications. During a live demo, an engineer showcased a mobile app that updated its own user interface in real-time based on a natural language prompt, all powered by the embedded Codex SDK. 

While the launch of an app ecosystem in ChatGPT and the breathtaking visuals of the Sora 2 API rightfully generated headlines, the general availability of Codex marks a more fundamental and immediate transformation. It is the quiet but powerful engine driving the next era of software development, turning the abstract promise of AI-driven productivity into a tangible, deployable reality for businesses today.

Presented by Zendesk

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Zendesk powers nearly 5 billion resolutions every year for over 100,000 customers around the world, with about 20,000 of its customers (and growing) using its AI services. Zendesk is poised to generate about $200 million in AI-related revenue this year, double than some of its largest competitors, while investing $400 million dollars in R&D. Much of that research is focused on upgrading the Zendesk Resolution Platform, a complete AI-first solution for customer service, employee service, and contact center teams, announced at Relate this past March.

During AI Summit, Chief Executive Officer Tom Eggemeier, along with members of the Zendesk team, took to the stage to announce several major advancements, including voice AI agents, video calling, and screen sharing for Zendesk Contact Center, and improved IT asset management, as well as the introduction of next-generation analytics, in the wake of its acquisition of HyperArc.

"We have built the only platform that is purpose-built for service and purpose-built for AI," Eggemeier said. "That focus is why we lead in AI for all types of service. And it is why we can deliver what no one else can for every service need you have in your organization."

New capabilities across use cases and companies

At its core, the Resolution Platform powers autonomous AI agents that solve complex issues in real time, leveraging leading LLMs like GPT-5, developed in collaboration with OpenAI, and supporting Model Context Protocol (MCP) to instantly access data, which streamlines workflows and improves autonomous problem-solving.

"Since our launch in March, we’ve been building fast, focused on making AI agents smarter, more flexible, and ready for even more channels," said Shashi Upadhyay, president of product, engineering, and AI at Zendesk. "And now, these AI agents are getting even better. They work across messaging, email, and now voice. They are getting smarter; able to handle multiple intents in a single message, detecting, remembering, and resolving many issues at once."

The only platform with native built-in QA, resolutions are automatically scored down to the conversation level, so teams can track resolution quality at scale. For startups, these insights are critical. They not only show what worked, but what needs fixing before it costs them time, reputation, or growth, and importantly, fit within a startup budget. That’s because Zendesk is the only company that charges only for successful resolutions, which are verified through the industry’s longest validation window, with two layers of quality checks.

Making the product CX admin a hero

Zendesk demonstrated the platform’s new features by highlighting a hypothetical wearable device company’s product launch. Service leaders at every stop along the product launch journey — from design to manufacturing — manage emerging issues with the support of the upgraded Resolution Platform.

For a global manufacturer that builds complex, state-of-the-art wearable tech, the pressure starts the moment a new product hits the market, tickets start pouring in, and a red-flagged backlog piles up.

"It is not a product issue, it is a resolution bottleneck," Upadhyay said. But, he added, "What once took days can now be resolved instantly."

The new Zendesk Admin Copilot is designed specifically to assist human agents, helping them spot what is not working, what to do next, and carry out changes quickly. It flags operational issues, like missing intent tags, broken internal processes, or routing conflicts that delay resolution. Copilot explains what is happening in plain language, recommends specific fixes, and with the admin’s approval, can make the changes itself. It's grounded in live Zendesk data, like tickets, triggers, and knowledge, so every recommendation is specific, current, and based on how the service operation actually runs.

Once the admin identifies the issue and implements a fix, the next step is ensuring everyone has access to the right knowledge to support it. For many organizations, that information lives outside of Zendesk. The newly launched Knowledge Connectors allows admins to pull in relevant content, like configuration guides or policy details, without needing to migrate anything so both human and AI agents have access to real-time instructions tied to the exact product version.

The admin also creates a smarter feedback loop with the new Action Builder, which automatically tags, summarizes, and sends notifications to the product team through Microsoft Teams.

And finally, Zendesk HyperArc will bring customers insights that combine AI and human analysis in a clear, narrative-driven view of what is happening and why, instead of siloed dashboards or static reports.

"With these innovations in place, change at the manufacturing plant cascades quickly, tickets are routed cleanly, support agents know what to say, engineering sees real signals instead of scattered anecdotes, and customers who just want the product to work get fast, reliable resolutions," Upadhyay said. "The CX Admin becomes the quiet hero of the manufacturer’s story."

Solutions for the retail CX leader

As a CX or contact center leader for a retail company, when a must-have wearable drops, how do you deliver service for your new hit product that feels personal and consistent when your team is stretched across multiple countries, channels, and customer expectations at once?

"Intelligent automation doesn’t just streamline operations — it enhances the customer experience across borders and channels," said Lisa Kant, senior vice president of marketing at Zendesk.

Zendesk’s Voice AI Agents are fully autonomous AI agents designed to understand natural speech, take action, and resolve issues without needing to escalate. They can verify identity, track orders, update deliveries, and answer setup questions in multiple languages, while keeping the brand experience consistent. Meanwhile, Video Calling lets a live agent spin up a video session, confirm the device is working, and walk the customer through setup or troubleshooting.

And because a help center is a critical part of delivering great service, especially when scaling fast across multiple countries and languages, Zendesk built Knowledge Builder, an AI-powered tool that helps teams build and maintain their help center content automatically. It analyzes real customer conversations and turns them into localized help articles for trending issues.

Giving IT leaders a strong edge

When a company adopts that new product, it becomes critical to resolve issues fast, to ensure employee productivity stays strong. Available with early access in November, Zendesk's new employee service offering, IT Asset Management (ITAM), natively integrates service and asset data together into the Zendesk service desk to help IT move from reactive troubleshooting to proactive service.

Now, when a vague “tablet not working” ticket comes in, Zendesk ITAM surfaces the device details right inside the ticket, so IT knows exactly what they are dealing with. Zendesk Copilot uses that same asset data to recommend model-specific troubleshooting steps. And with Knowledge Connectors, those steps can be pulled directly from SharePoint or Confluence without migration. If the fix does not work, the IT specialist confirms in seconds that the device is under warranty and issues a replacement without any back-and-forth.

With real-time visibility across every hardware asset, the IT leader can spot patterns before they become a flood of tickets, or failures at the point of care, so IT resolves issues faster and prevents problems before they happen.

"With Zendesk, IT is not just reacting to issues — it is setting the standard for how proactive employee service is delivered," Upadhyay said.

For more on the latest Zendesk updates and improvements, and to watch a conversation with Zendesk's special guest, co-founder of LinkedIn, Reid Hoffman, and more, watch the full videos here. And for the latest updates, detailed information, and product availability, visit Zendesk’s official announcements page.

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Gulf nations have been subject to US restrictions on advanced AI chip shipments since 2023 due to concerns about technology diversion to China.

We look at some of the latest data center developments announced over the past month.

Researchers at the University of Illinois Urbana-Champaign and Google Cloud AI Research have developed a framework that enables large language model (LLM) agents to organize their experiences into a memory bank, helping them get better at complex tasks over time.

The framework, called ReasoningBank, distills “generalizable reasoning strategies” from an agent’s successful and failed attempts to solve problems. The agent then uses this memory during inference to avoid repeating past mistakes and make better decisions as it faces new problems. The researchers show that when combined with test-time scaling techniques, where an agent makes multiple attempts at a problem, ReasoningBank significantly improves the performance and efficiency of LLM agents.

Their findings show that ReasoningBank consistently outperforms classic memory mechanisms across web browsing and software engineering benchmarks, offering a practical path toward building more adaptive and reliable AI agents for enterprise applications.

The challenge of LLM agent memory

As LLM agents are deployed in applications that run for long periods, they encounter a continuous stream of tasks. One of the key limitations of current LLM agents is their failure to learn from this accumulated experience. By approaching each task in isolation, they inevitably repeat past mistakes, discard valuable insights from related problems, and fail to develop skills that would make them more capable over time.

The solution to this limitation is to give agents some kind of memory. Previous efforts to give agents memory have focused on storing past interactions for reuse by organizing information in various forms from plain text to structured graphs. However, these approaches often fall short. Many use raw interaction logs or only store successful task examples. This means they can't distill higher-level, transferable reasoning patterns and, crucially, they don’t extract and use the valuable information from the agent’s failures. As the researchers note in their paper, “existing memory designs often remain limited to passive record-keeping rather than providing actionable, generalizable guidance for future decisions.”

How ReasoningBank works

ReasoningBank is a memory framework designed to overcome these limitations. Its central idea is to distill useful strategies and reasoning hints from past experiences into structured memory items that can be stored and reused.

According to Jun Yan, a Research Scientist at Google and co-author of the paper, this marks a fundamental shift in how agents operate. "Traditional agents operate statically—each task is processed in isolation," Yan explained. "ReasoningBank changes this by turning every task experience (successful or failed) into structured, reusable reasoning memory. As a result, the agent doesn’t start from scratch with each customer; it recalls and adapts proven strategies from similar past cases."

The framework processes both successful and failed experiences and turns them into a collection of useful strategies and preventive lessons. The agent judges success and failure through LLM-as-a-judge schemes to obviate the need for human labeling.

Yan provides a practical example of this process in action. An agent tasked with finding Sony headphones might fail because its broad search query returns over 4,000 irrelevant products. "ReasoningBank will first try to figure out why this approach failed," Yan said. "It will then distill strategies such as ‘optimize search query’ and ‘confine products with category filtering.’ Those strategies will be extremely useful to get future similar tasks successfully done."

The process operates in a closed loop. When an agent faces a new task, it uses an embedding-based search to retrieve relevant memories from ReasoningBank to guide its actions. These memories are inserted into the agent’s system prompt, providing context for its decision-making. Once the task is completed, the framework creates new memory items to extract insights from successes and failures. This new knowledge is then analyzed, distilled, and merged into the ReasoningBank, allowing the agent to continuously evolve and improve its capabilities.

Supercharging memory with scaling

The researchers found a powerful synergy between memory and test-time scaling. Classic test-time scaling involves generating multiple independent answers to the same question, but the researchers argue that this “vanilla form is suboptimal because it does not leverage inherent contrastive signal that arises from redundant exploration on the same problem.”

To address this, they propose Memory-aware Test-Time Scaling (MaTTS), which integrates scaling with ReasoningBank. MaTTS comes in two forms. In “parallel scaling,” the system generates multiple trajectories for the same query, then compares and contrasts them to identify consistent reasoning patterns. In sequential scaling, the agent iteratively refines its reasoning within a single attempt, with the intermediate notes and corrections also serving as valuable memory signals.

This creates a virtuous cycle: the existing memory in ReasoningBank steers the agent toward more promising solutions, while the diverse experiences generated through scaling enable the agent to create higher-quality memories to store in ReasoningBank. 

“This positive feedback loop positions memory-driven experience scaling as a new scaling dimension for agents,” the researchers write.

ReasoningBank in action

The researchers tested their framework on WebArena (web browsing) and SWE-Bench-Verified (software engineering) benchmarks, using models like Google’s Gemini 2.5 Pro and Anthropic’s Claude 3.7 Sonnet. They compared ReasoningBank against baselines including memory-free agents and agents using trajectory-based or workflow-based memory frameworks.

The results show that ReasoningBank consistently outperforms these baselines across all datasets and LLM backbones. On WebArena, it improved the overall success rate by up to 8.3 percentage points compared to a memory-free agent. It also generalized better on more difficult, cross-domain tasks, while reducing the number of interaction steps needed to complete tasks. When combined with MaTTS, both parallel and sequential scaling further boosted performance, consistently outperforming standard test-time scaling.

This efficiency gain has a direct impact on operational costs. Yan points to a case where a memory-free agent took eight trial-and-error steps just to find the right product filter on a website. "Those trial and error costs could be avoided by leveraging relevant insights from ReasoningBank," he noted. "In this case, we save almost twice the operational costs," which also improves the user experience by resolving issues faster.

For enterprises, ReasoningBank can help develop cost-effective agents that can learn from experience and adapt over time in complex workflows and areas like software development, customer support, and data analysis. As the paper concludes, “Our findings suggest a practical pathway toward building adaptive and lifelong-learning agents.”

Yan confirmed that their findings point toward a future of truly compositional intelligence. For example, a coding agent could learn discrete skills like API integration and database management from separate tasks. "Over time, these modular skills... become building blocks the agent can flexibly recombine to solve more complex tasks," he said, suggesting a future where agents can autonomously assemble their knowledge to manage entire workflows with minimal human oversight.

Many organizations would be hesitant to overhaul their tech stack and start from scratch.

Not Notion.

For the 3.0 version of its productivity software (released in September), the company didn’t hesitate to rebuild from the ground up; they recognized that it was necessary, in fact, to support agentic AI at enterprise scale.

Whereas traditional AI-powered workflows involve explicit, step-by-step instructions based on few-shot learning, AI agents powered by advanced reasoning models are thoughtful about tool definition, can identify and comprehend what tools they have at their disposal and plan next steps.

“Rather than trying to retrofit into what we were building, we wanted to play to the strengths of reasoning models,” Sarah Sachs, Notion’s head of AI modeling, told VentureBeat. “We've rebuilt a new architecture because workflows are different from agents.”

Re-orchestrating so models can work autonomously

Notion has been adopted by 94% of Forbes AI 50 companies, has 100 million total users and counts among its customers OpenAI, Cursor, Figma, Ramp and Vercel.

In a rapidly evolving AI landscape, the company identified the need to move beyond simpler, task-based workflows to goal-oriented reasoning systems that allow agents to autonomously select, orchestrate, and execute tools across connected environments.

Very quickly, reasoning models have become “far better” at learning to use tools and follow chain-of-thought (CoT) instructions, Sachs noted. This allows them to be “far more independent” and make multiple decisions within one agentic workflow. “We rebuilt our AI system to play to that," she said.

From an engineering perspective, this meant replacing rigid prompt-based flows with a unified orchestration model, Sachs explained. This core model is supported by modular sub-agents that search Notion and the web, query and add to databases and edit content.

Each agent uses tools contextually; for instance, they can decide whether to search Notion itself, or another platform like Slack. The model will perform successive searches until the relevant information is found. It can then, for instance, convert notes into proposals, create follow-up messages, track tasks, and spot and make updates in knowledge bases.

In Notion 2.0, the team focused on having AI perform specific tasks, which required them to “think exhaustively” about how to prompt the model, Sachs noted. However, with version 3.0, users can assign tasks to agents, and agents can actually take action and perform multiple tasks concurrently.

“We reorchestrated it to be self-selecting on the tools, rather than few-shotting, which is explicitly prompting how to go through all these different scenarios,” Sachs explained. The aim is to ensure everything interfaces with AI and that “anything you can do, your Notion agent can do.”

Bifurcating to isolate hallucinations

Notion’s philosophy of “better, faster, cheaper,” drives a continuous iteration cycle that balances latency and accuracy through fine-tuned vector embeddings and elastic search optimization. Sachs’ team employs a rigorous evaluation framework that combines deterministic tests, vernacular optimization, human-annotated data and LLMs-as-a-judge, with model-based scoring identifying discrepancies and inaccuracies.

“By bifurcating the evaluation, we're able to identify where the problems come from, and that helps us isolate unnecessary hallucinations,” Sachs explained. Further, making the architecture itself simpler means it’s easier to make changes as models and techniques evolve.

“We optimize latency and parallel thinking as much as possible,” which leads to “way better accuracy,” Sachs noted. Models are grounded in data from the web and the Notion connected workspace.

Ultimately, Sachs reported, the investment in rebuilding its architecture has already provided Notion returns in terms of capability and faster rate of change.

She added, “We are fully open to rebuilding it again, when the next breakthrough happens, if we have to.”

Understanding contextual latency

When building and fine-tuning models, it’s important to understand that latency is subjective: AI must provide the most relevant information, not necessarily the most, at the cost of speed.

“You'd be surprised at the different ways customers are willing to wait for things and not wait for things,” Sachs said. It makes for an interesting experiment: How slow can you go before people abandon the model?

With pure navigational search, for instance, users may not be as patient; they want answers near-immediately. “If you ask, ‘What's two plus two,’ you don't want to wait for your agent to be searching everywhere in Slack and JIRA,” Sachs pointed out.

But the longer the time it's given, the more exhaustive a reasoning agent can be. For instance, Notion can perform 20 minutes of autonomous work across hundreds of websites, files and other materials. In these instances, users are more willing to wait, Sachs explained; they allow the model to execute in the background while they attend to other tasks.

“It's a product question,” said Sachs. “How do we set user expectations from the UI? How do we ascertain user expectations on latency?”

Notion is its biggest user

Notion understands the importance of using its own product — in fact, its employees are among its biggest power users.

Sachs explained that teams have active sandboxes that generate training and evaluation data, as well as a “really active” thumbs-up-thumbs-down user feedback loop. Users aren’t shy about saying what they think should be improved or features they’d like to see.

Sachs emphasized that when a user thumbs down an interaction, they are explicitly giving permission to a human annotator to analyze that interaction in a way that de-anonymizes them as much as possible.

“We are using our own tool as a company all day, every day, and so we get really fast feedback loops,” said Sachs. “We’re really dogfooding our own product.”

That said, it’s their own product they’re building, Sachs noted, so they understand that they may have goggles on when it comes to quality and functionality. To balance this out, Notion has trusted "very AI-savvy" design partners who are granted early access to new capabilities and provide important feedback.

Sachs emphasized that this is just as important as internal prototyping.

“We're all about experimenting in the open, I think you get much richer feedback,” said Sachs. “Because at the end of the day, if we just look at how Notion uses Notion, we're not really giving the best experience to our customers.”

Just as importantly, continuous internal testing allows teams to evaluate progressions and make sure models aren't regressing (when accuracy and performance degrades over time). "Everything you're doing stays faithful," Sachs explained. "You know that your latency is within bounds."

Many companies make the mistake of focusing too intensely on retroactively-focused evans; this makes it difficult for them to understand how or where they're improving, Sachs pointed out. Notion considers evals as a "litmus test" of development and forward-looking progression and evals of observability and regression proofing.

“I think a big mistake a lot of companies make is conflating the two,” said Sachs. “We use them for both purposes; we think about them really differently.”

Takeaways from Notion's journey

For enterprises, Notion can serve as a blueprint for how to responsibly and dynamically operationalize agentic AI in a connected, permissioned enterprise workspace.

Sach’s takeaways for other tech leaders:

• Don’t be afraid to rebuild when foundational capabilities change; Notion fully re-engineered its architecture to align with reasoning-based models.

• Treat latency as contextual: Optimize per use case, rather than universally.

• Ground all outputs in trustworthy, curated enterprise data to ensure accuracy and trust.

  She advised: “Be willing to make the hard decisions. Be willing to sit at the top of the frontier, so to speak, on what you're developing to build the best product you can for your customers.”

In a packed theater at Fort Mason, after a whirlwind keynote of product announcements, OpenAI CEO Sam Altman sat down with Sir Jony Ive, the legendary designer behind Apple's most iconic products. The conversation, held exclusively for the 1,500 developers in attendance and not part of the public livestream, offered the clearest glimpse yet into the philosophy and ambition behind their secretive collaboration to build a new "family" of AI-powered devices.

The partnership, solidified by OpenAI's staggering $6.5 billion acquisition of Ive's hardware startup Io in May, has been the subject of intense speculation.While concrete product details remained under wraps, the discussion pivoted away from specifications and toward a profound, almost therapeutic mission: to fix our broken relationship with technology.

For nearly 45 minutes, Ive, in his signature thoughtful cadence, articulated a vision that feels like both a continuation of and a repentance for his life's work. The man who designed the iPhone, a device that arguably defined the modern era of personal computing, is now on a quest to cure the very anxieties it helped create.

Jony Ive's post-Apple mission, clarified by ChatGPT

The collaboration, Ive explained, was years in the making, but it was the launch of ChatGPT that provided a sudden, clarifying purpose for his post-Apple design collective, LoveFrom.

"With the launch of ChatGPT, it felt like our purpose for the last six years became clear," Ive said. "We were starting to develop some ideas for an interface based on the capability of the technology these guys were developing... I've never in my career come across anything vaguely like the affordance, like the capability that we're now starting to sense."

This capability, he argued, demands a fundamental rethinking of the devices we use, which he described as "legacy products" from a bygone era. The core motivation, he stressed, is not about corporate agendas but about a sense of duty to humanity.

"The reason we're doing this is we love our species and we want to be useful," Ive said. "We think that humanity deserves much better than humanity generally is given."

An 'obscene understatement': Jony Ive's quest to cure our tech anxiety

The most striking theme of the conversation was Ive's candid critique of the current state of technology — the very ecosystem he was instrumental in building. He described our current dynamic with our devices as deeply flawed, a problem he now sees AI as the solution to, not an extension of.

"I don't think we have an easy relationship with our technology at the moment," Ive began, before adding, "When I said we have an uncomfortable relationship with our technology, I mean, that's the most obscene understatement."

Instead of chasing productivity, the primary goal for this new family of devices is emotional well-being. It's a radical departure from the efficiency-obsessed ethos that dominates Silicon Valley.

When asked about his ambitions for the new devices, Ive prioritized emotional well-being over simple productivity. "I know I should care about productivity, and I do," he said, but his ultimate goal is that the tools "make us happy and fulfilled, and more peaceful and less anxious, and less disconnected."

He framed it as a chance to reject the current, fraught relationship people have with their technology. "We have a chance to... absolutely change the situation that we find ourselves in," he stated. "We don't accept this has to be the norm."

Buried in brilliance: why '15 to 20 compelling ideas' have become Ive's biggest challenge

While the vision is clear, the path is fraught with challenges. Reports have surfaced about technical hurdles and philosophical debates delaying the project. Ive himself gave voice to this struggle, admitting the sheer pace of AI's progress has been overwhelming. The rapid advancement has generated a torrent of possibilities, making the crucial act of focusing incredibly difficult.

"The momentum is so extraordinary... it has led us to generate 15 to 20 really compelling product ideas. And the challenge is trying to focus," Ive confessed."I used to be good at that, and I've lost some confidence, because the choices are, it'll be easy if you really knew there were three good ones... it's just not like that."

This admission provides context to reports that the team is grappling with unresolved issues around the device's "personality" and computing infrastructure. The goal, according to one source, is to create an AI companion that is "accessible but not intrusive," avoiding the pitfalls of a "weird AI girlfriend."

Beyond the screen: Ive's design philosophy for an 'inevitable' AI device

While no devices were shown, the conversation and prior reports offer clues. The project involves a "family of devices," not a single gadget.It will likely be a departure from the screen-centric world we inhabit. Reports suggest a "palm-sized device without a screen" that relies on cameras and microphones to perceive its environment.

Ive argued that it would be "absurd" to assume that today's breathtaking AI technology should be delivered through "products that are decades old." The goal is to create something that feels entirely new, yet completely natural.

"It should seem inevitable. It should seem obvious, as if there wasn't possibly another rational solution to the problem," Ive said, echoing a design philosophy often attributed to his time with Steve Jobs.

He also spoke of bringing a sense of joy and whimsy back to technology, pushing back against a culture he feels has become overly serious.

"In terms of the interfaces we design, if we can't smile honestly, if it's just another deeply serious sort of exclusive thing, I think that would do us all a huge disservice," he remarked.

The chat concluded without a product reveal, leaving the audience with a philosophical blueprint rather than a technical one. The central narrative is clear: Jony Ive, the designer who put a screen in every pocket, is now betting on a screenless future, powered by OpenAI's formidable intelligence, to make us all a little less anxious and a little more human.