The biggest news to emerge from the AI world this week was undoubtedly that Elon Musk’s SpaceX had agreed to acquire AI coding startup Cursor in an all-stock deal valued at $60 billion.

But on the very same day, at an invite-only developer conference hosted by Cursor in San Francisco, Tomas Reimers took to the stage to unveil a fledgling project that could prove just as consequential for the developer tools industry.

Origin, as it’s called, is a Git-compatible code-hosting platform designed from the ground up for a world where AI agents — rather than humans — do the bulk of the work.

Reimers, it’s worth noting, is co-founder of Graphite, a code-review startup that Cursor revealed it was acquiring back in December (a deal that apparently closed in January). At the time, some commentators noted the deal’s implications for GitHub — among them was Gergely Orosz.

Orosz, author of The Pragmatic Engineer newsletter and an investor in Graphite, who wrote on LinkedIn: “I’m telling you: GitHub’s biggest competitor could soon be Cursor. Graphite — in my view — is the best AI code review + stacked diffs + PR workflow product out there. GitHub is already playing catch-up to Cursor/Graphite.”

Put simply, Graphite had already built workflow tools that GitHub was scrambling to replicate — and with Cursor’s resources behind it, the gap was only going to widen. And now with the might of SpaceX, a $2.5 trillion company, behind it, things could be about to get very interesting.

The Origin origin story

On stage in San Francisco ahead of Origin’s unveiling, Reimers pointed to Graphite’s customer base — which includes Shopify, Snowflake, Notion, and Figma — as evidence of a problem already well underway before Origin existed.

“When we were acquired by Cursor, we accelerated our most ambitious project — to rebuild that tooling from scratch.”

“Over the past few years, we noticed the trend as these companies adopt AI tooling,” Reimers said. “The tools that they relied on started to become unreliable. That’s because over the past few years, AI tooling has totally changed our industry. It’s enabled every developer to be a 10 to 100x developer, but that change has required fundamentally different tooling. That’s why, when we were acquired by Cursor, we accelerated our most ambitious project — to rebuild that tooling from scratch.”

Amid all the hullaballoo of SpaceX hitting the public markets, becoming one of the world’s most valuable companies overnight, and doling out a cool $60 billion for a four-year-old startup, it’s easy to appreciate why Origin might have slipped under the radar. But the infrastructure problem it’s setting out to solve is real.

It’s easy to appreciate why Origin might have slipped under the radar. But the infrastructure problem it’s setting out to solve is real.

GitHub, the world’s dominant code hosting platform by some distance, is having a rough time of it. As The New Stack reported in June, the platform has logged hundreds of incidents over the past 12 months, struggling to keep pace with the volume of code that AI agents are generating. The company says it’s now processing about 1.4 billion commits per month — up from 1 billion across all of 2025 — with agents alone generating more than 17 million pull requests per month.

The irony isn’t lost on anyone: GitHub helped kickstart the AI coding era with the launch of Copilot in 2021, and it’s now buckling under the weight of it. And for some, the cracks are already showing in their day-to-day habits.

Brian Douglas, GitHub’s former director of developer advocacy who recently launched his own AI infrastructure startup called Paper Compute, tells The New Stack that the shift is already underway.

“Agents are quickly killing the will for doing open source.”

“Agents are quickly killing the will for doing open source,” Douglas says. “I’d love to see what GitHub’s [monthly active user] numbers look like today, because I am sure there are a number of folks choosing to do code reviews elsewhere — or exclusively collaborating with agents to get the work to the last mile — which is at an all-time high.”

Douglas, for what it’s worth, counts himself among them, saying he now does much of his review and PR work directly in AI coding tools.

“As a GitHub power user, I find myself using it less, and relying more on Claude and Codex for review and PR interactions,” he says.

A post-GitHub world?

Origin remains in waitlist-only mode ahead of a planned fall launch, and those present at Compile reported enough detail to sketch its ambitions. Developer advocate and independent commentator Shawn Wang Yuexian, known as swyx, described it as a “long-awaited Git competitor, scalable for agent workloads, extensible with API and MCP, and with built-in merge conflict and CI failure agent resolution.”

Whatever Origin looks like in its launch guise, it’s clear the appetite for an alternative to the status quo is growing. The software development world has changed considerably since GitHub popularized the pull request model back in 2008 — a feature that Douglas calls its “best ever.” But the pull request was designed for a world where humans deliberately wrote and reviewed code, one change at a time. That world is receding fast.

“Right now, the velocity of projects being created is overwhelming GitHub, and engineers are not looking at the code.”

“Right now, the velocity of projects being created is overwhelming GitHub, and engineers are not looking at the code,” Douglas says. “So if the goal is to put it in the cloud so agents are managing the code, I think that is absolutely an opportunity for disruption.”

So, as AI agents push code at a rate no human reviewer can keep up with, the pull request risks becoming a formality — a box to tick rather than a meaningful quality gate. Which raises a deeper question about how the industry should measure the value of software work at all.

For Douglas, the answer lies in a different unit entirely. Commits and lines of code — the traditional proxies for developer output — tell you little in a world where an agent can generate thousands of lines in seconds. Tokens, by contrast, map directly to compute cost and, therefore, to the real effort and value generated. It’s a reframing that suits Cursor rather well.

“Tokens are a better metric than commits.”

“Tokens are a better metric than commits,” Douglas says. “They align to a dollar spent that correlates to the effort of work. Previously, we pretended lines of code were the metric, and that was proven incorrect. But tokens plus agent sessions equals customer value — and Cursor is positioned well to own a deeper part of the collaboration stack.”

Cursor, though, isn’t alone in that conviction, and a slew of tangential efforts to rebuild that infrastructure for the agentic era are emerging.

At its Transcend conference in London on June 10, GitLab announced a private beta of what it calls Next Generation Source Code Management — known internally as Project Switch. Unveiled on stage by GitLab chief product and marketing officer Manav Khurana, the new backend keeps the Git protocol intact but redesigns the underlying architecture entirely, allowing agents to query repositories server-side rather than cloning them in full.

GitLab says it delivers up to 50 times faster task execution per agent, with up to 3 times fewer tokens consumed. And notably, Anthropic is a design partner on the project.

“The most popular Git platforms in the world are buckling under the load, not just because of your teams cloning, branching, and merging code, but also dozens, in some cases hundreds, of agents working simultaneously and putting a lot of pressure on those systems,” Khurana said.

The day after GitLab’s Transcend announcement, Zed co-founder Nathan Sobo published details of DeltaDB, a project the company had first teased the previous fall. A more radical proposition than either Origin or Project Switch, DeltaDB replaces Git’s commit-based model entirely with a continuous stream of fine-grained deltas — every operation an agent performs, linked directly to the conversation that produced it. Sobo confirmed that a beta version is just weeks away.

HashiCorp co-founder Mitchell Hashimoto, meanwhile, has seen this coming. Back in December, he wrote on X: “The AI companies are on track to become GitHub faster than GitHub is becoming an AI company.”

The AI companies are on track to become GitHub faster than GitHub is becoming an AI company. I'm sure there's a lot of sycophants within GH/MS showing off PowerBI dashboards to argue against this for their own personal gain, but wake the fuck up.

— Mitchell Hashimoto (@mitchellh) December 19, 2025

When Origin was announced this week, he retweeted himself with a single line: “Cursor announced Origin today. More will come.”

*taps sign* Cursor announced Origin today. More will come. https://t.co/MwLN0Q7dHX

— Mitchell Hashimoto (@mitchellh) June 16, 2026

Hashimoto, as it happens, is an investor in another agent-native code hosting startup called East River Source Control (ERSC), which is building a Git-compatible platform designed to land thousands of commits per second.

The model is the moat

For Douglas, the convergence of competing efforts to rebuild version control from the ground up isn’t hugely surprising. In the past year, he points out, a similar dynamic played out with developer sandboxes — the environments where code gets written and tested — as companies like Docker, Cloudflare, and Vercel moved into that space because that’s where developers were spending their time.

The same gravitational pull is now acting on version control. The way developers work has changed fundamentally — where once they wrote code directly inside their editors, many now spend their time directing AI agents that do the writing for them. The IDE is no longer primarily a place to type; it’s increasingly a place to watch, review, and steer.

“I think all the folks who are part of the story have a shot, and we need to rethink our infrastructure to prepare for this.”

“Now, IDEs are suffering from the fact that developers have evolved to foundational model harnesses writing code, and they need to position themselves as the tool you open to watch agents write the code,” Douglas says. “I think all the folks who are part of the story have a shot, and we need to rethink our infrastructure to prepare for this.”

Underpinning all of this, though, is a commercial reality. Cursor has been building toward this position for some time, having launched its own first-party coding model, Composer, in 2025 and iterating on Composer 2.5 in May, giving it cheaper, in-house inference rather than relying entirely on costly API calls to Anthropic and OpenAI. Composer 2.5 costs a fraction of Claude Opus at equivalent tasks — as much as a tenfold difference on output tokens. Owning the model, in other words, is what makes owning the rest of the stack viable.

Introducing Composer 2.5, our most powerful model yet.

It's more intelligent, better at sustained work on long-running tasks, and more reliable at following complex instructions.

For the next week, we’re doubling the included usage of the model. pic.twitter.com/N87ojcXlOC

— Cursor (@cursor_ai) May 18, 2026

“It’s clear you can’t just insert an OpenAI key and expect hyper-growth or longevity in this market anymore,” Douglas says. “Instead, you need to own the model to win.”

Whether SpaceX’s firepower accelerates that ambition or complicates it remains to be seen. But the companies placing bets on the next era of software development aren’t waiting for GitHub to catch up.

The post Cursor, GitLab and Zed agree GitHub is breaking. They disagree on how to rebuild it. appeared first on The New Stack.

Inference is now the dominant force in enterprise AI — and with it has come an inconvenient reality: Data is almost always transported to compute. Every inference call moves sensitive enterprise information out of the systems where it lives and into external environments optimized for GPU throughput rather than data governance. This creates friction that compounds at scale: rising costs, expanding security exposure, and a growing tangle of data copies that drift out of sync with operational reality.

What enterprises actually want is different: to keep data and IP intact within the database rather than creating multiple copies and managing the resulting inconsistencies.

Research across more than 2,050 senior executives from major enterprises worldwide suggests that 95% of organizations intend to become their own AI and data platforms within the next 780 working days. Yet only 13% have successfully reached that goal. The organizations that have succeeded are achieving almost five times the return on investment of those still struggling to operationalize AI.

What separates the leaders from the followers is not model quality. It is infrastructure strategy.

The most successful organizations have adopted a sovereign-by-design approach. More than 75% are operating across multiple clouds and on-premises environments rather than relying on a single hyperscale provider. They are building AI around their own business, regulatory, and operational requirements rather than adapting those requirements to fit a cloud vendor’s architecture.

As AI moves from experimentation into production, CIOs are discovering that training models is relatively easy. Running them efficiently, securely and compliantly across thousands of operational workloads is where the real challenge begins.

The shift from training to inference

Training is a discrete event. Inference is an ongoing business process.

A model may be trained once, but it could be called millions of times each day. Every fraud assessment, insurance claim review, customer service interaction, medical recommendation, sanctions check, or predictive maintenance event relies on inference occurring against live operational data.

“What separates the leaders from the followers is not model quality. It is infrastructure strategy.”

This distinction fundamentally changes enterprise infrastructure requirements.

Training workloads prioritize compute density and GPU availability. Inference workloads prioritize latency, governance, reliability and cost control. They must operate where business data resides and where compliance requirements can be enforced.

For heavily regulated industries such as financial services, healthcare, telecommunications, energy and the public sector, inference cannot simply occur in whichever region offers the lowest compute cost. Data sovereignty requirements, audit obligations and security mandates often dictate exactly where workloads must execute.

The challenge therefore becomes much larger than AI itself. Organizations need an operating model capable of bringing together compute, data and governance without sacrificing flexibility.

Why neoclouds are emerging as a critical layer to cross the chasm to production

This is where neoclouds have become increasingly important.

Unlike traditional hyperscalers, neoclouds are purpose-built around AI infrastructure. Their focus is not delivering hundreds of generic cloud services but rather optimizing for GPU access, AI performance, and flexible consumption models.

For many enterprises, neoclouds offer a compelling answer to the growing demand for specialized AI compute. They provide access to the latest accelerator technologies while enabling organizations to scale workloads without the complexity often associated with large cloud environments.

“The future of AI architecture therefore depends on bringing models closer to data rather than moving data closer to models.”

However, neoclouds solve only one part of the enterprise AI equation.

AI does not create value in isolation. Models require context. They need access to customer records, transaction histories, operational workflows, policy documents, supply chain information and enterprise knowledge. Moving these assets into separate AI environments creates duplication, latency and governance challenges.

The future of AI architecture therefore depends on bringing models closer to data rather than moving data closer to models.

Why Postgres has become the enterprise AI foundation

As organizations look for a common platform that supports both operational and AI workloads, Postgres has emerged as a natural foundation.

Postgres already serves as the operational backbone for many of the world’s most important applications. It combines transactional reliability, extensibility, and scalability with the openness that enterprises increasingly demand. 70%+ of AI-related application development is happening on Postgres.  

What makes Postgres particularly relevant in the AI era is its ability to become more than a database. It can serve as a governed memory layer for AI systems, integrating operational data, application context, permissions, observability, and retrieval capabilities into a single architecture.

This dramatically reduces complexity.

Instead of maintaining separate infrastructures for transactional systems, vector stores, AI memory layers, and governance frameworks, organizations can consolidate around a trusted operational platform that already supports their mission-critical workloads.

For CIOs seeking to balance innovation with control, this architectural simplification represents a significant strategic advantage.

Why sovereignty matters more than ever

Sovereignty has become one of the defining themes of enterprise technology.

For banks, sovereignty means maintaining control over financial data and regulatory obligations. For healthcare organizations, it means protecting patient information while enabling innovation. For governments, it means ensuring national and citizen data remains under appropriate jurisdictional control.

The rise of AI has amplified these concerns.

Organizations increasingly need assurance that models, data, policies and operational controls can remain within designated environments while still benefiting from advances in AI technology.

This requirement is driving demand for sovereign AI architectures capable of operating across clouds, private infrastructure and on-premises environments.

The challenge is creating consistency across these environments without introducing operational complexity.

EDB Postgres AI: connecting sovereign data and sovereign AI

EDB Postgres AI addresses this challenge by bringing together operational Postgres, AI capabilities and hybrid infrastructure management into a unified platform.

Rather than forcing enterprises to choose between innovation and control, EDB Postgres AI enables organizations to deploy AI where their data already resides. Through capabilities spanning operational databases, analytics, agentic AI workloads and hybrid management, organizations can create a consistent operating model across sovereign environments.

This approach is particularly relevant for regulated industries where moving sensitive information into external AI services may introduce compliance, security or governance concerns.

By enabling inference close to operational data, organizations reduce data movement, improve performance, and strengthen their compliance posture. At the same time, they maintain the flexibility required to leverage emerging AI technologies and modern infrastructure models.

“By enabling inference close to operational data, organizations reduce data movement, improve performance, and strengthen their compliance posture.”

The result is a platform that aligns with the realities of enterprise AI rather than the assumptions of consumer AI.

“The reality is that the new AI at scale world needs a new infrastructure. That isn’t just the compute; it’s the governance, heuristic data access and level of observational and orchestration control that are absolute, governed, agile and work for humans and agents.”  Nancy Hensley, CPO, EDB

The new enterprise AI stack

The emerging enterprise AI architecture is increasingly built around complementary rather than competing technologies.

Infrastructure layer	Primary role	Strategic value
Neoclouds	Specialized AI compute and GPU infrastructure	Access to cutting-edge AI acceleration and flexible scaling
Public Hyperscalers	Broad cloud services and global reach	Ecosystem breadth and service diversity
Postgres	Operational data foundation	Trusted, governed and scalable enterprise data platform
EDB Postgres AI	Sovereign AI and hybrid management layer	Enables AI, analytics and operational workloads to run consistently across sovereign environments
Enterprise Governance	Security, compliance and policy controls	Ensures AI aligns with regulatory and business requirements

Together, these layers create an architecture capable of supporting the complete AI lifecycle—from experimentation and model training through production inference and continuous optimization.

The CIO imperative

The organizations realizing the greatest value from AI are no longer asking how to train better models. They are asking how to operationalize AI across the enterprise while maintaining control over cost, governance, and risk.

Their answer is increasingly consistent.

They are adopting multi-cloud and hybrid strategies rather than relying on a single cloud. They are prioritizing sovereign architectures rather than centralized data movement. They are building around open operational foundations rather than proprietary lock-in. Most importantly, they are recognizing that AI success depends on bringing intelligence to data, not data to intelligence.

Neoclouds provide the compute layer required for modern AI. Postgres provides the operational foundation required for trusted enterprise systems. EDB Postgres AI connects these worlds through a sovereign architecture designed for the realities of regulated industries.

As AI transitions from experimentation to operational necessity, the winning enterprises will be those that can make inference secure, governed, low-latency, and economically sustainable at scale.

In the next era of enterprise AI, the greatest business value will not come from model selection or raw GPU access. It will come from infrastructure strategy built around data — keeping intelligence close to where data already lives, governed, trusted, and ready to act.

The post Neoclouds, sovereign AI and Postgres: The new operating model for regulated enterprises appeared first on The New Stack.

For most of its 30-year history, Postgres has been viewed as a transactional database. Organizations trust it with customer records, financial transactions, and countless other operational workloads. Its reputation was built on reliability, strong transactional guarantees, and a vibrant open-source community that has spent decades refining the database without compromising its foundations.

However, some of the most important innovations in the Postgres ecosystem today have little to do with storing data. They have to do with reducing the need to move it around.

“Some of the most important innovations in the Postgres ecosystem today have little to do with storing data. They have to do with reducing the need to move it around.”

Database innovation has historically focused on performance, scalability, and reliability. Increasingly, the harder problem is interoperability: how operational data can be shared across analytical systems, AI applications, and downstream services without creating yet another pipeline or copy.

Why Postgres keeps showing up

The reality of modern software architecture is that data rarely stays in one place. Information created in operational systems quickly finds its way into warehouses, search platforms, machine learning environments, and AI applications. Every new system solves a legitimate business problem, but it also creates another destination for data and often another copy to maintain.

The costs of this approach extend beyond infrastructure spending alone. Every additional copy introduces latency, creates another potential source of inconsistency, and increases the operational burden of keeping systems synchronized. Many organizations now spend as much effort moving data as they do storing it.

“Many organizations now spend as much effort moving data as they do storing it.”

For many businesses, Postgres serves as the system of record for customer interactions, transactions, application state, and other business-critical information. As organizations expand their analytical, machine learning, and AI capabilities, they are not looking to create another source of truth; rather, they’re looking for better ways to work with the one they already trust.

That shift is changing how Postgres fits into modern architecture. Historically, Postgres was viewed primarily as the place where operational data originated before being copied into downstream systems. Increasingly, organizations want those systems to work more seamlessly with operational data while reducing the pipelines, copies, and synchronization processes required to support them.

Technologies such as logical replication, change data capture, and foreign data wrappers have helped Postgres participate more directly in larger data ecosystems. As a result, organizations are no longer asking only whether Postgres can store their data. They’re instead asking how easily it can connect to everything around it.

That shift, from evaluating databases primarily on storage and performance to evaluating them on interoperability, may be one of the most important changes happening in the Postgres ecosystem today.

AI is exposing old problems

The recent focus on AI has brought renewed attention to data movement. AI didn’t create the problem. If anything, it exposed a limitation that has been quietly growing for years. For decades, organizations built architectures around the idea that data would move between systems through pipelines and periodic synchronization. That model worked because most analytical workloads could tolerate some degree of delay.

AI is changing those expectations. Many AI applications depend on access to current operational context. The challenge is not that organizations lack data. In many cases, they already have it. The challenge is that the data is spread across multiple systems, each with its own copy, latency profile, and synchronization process.

“AI is forcing organizations to confront a broader question: How many copies of the same data are actually necessary? The answer increasingly appears to be fewer than most architectures maintain today.”

As a result, AI is forcing organizations to confront a broader question: How many copies of the same data are actually necessary? The answer increasingly appears to be fewer than most architectures maintain today. As expectations around freshness rise, reducing unnecessary data movement becomes just as important as accelerating it. The underlying challenge is not new. AI has simply made it harder to ignore.

What’s next

The database industry spent decades solving storage. Databases became more reliable, storage became cheaper, and infrastructure became dramatically easier to operate. The next challenge is not where data lives, but how easily it can be shared across systems without introducing additional pipelines, copies, and synchronization overhead. Increasingly, the goal is not simply moving data faster. It is reducing unnecessary movement altogether.

Postgres has a habit of outlasting predictions about its replacement. For years, members of the community have joked that every year is “the year of Postgres.” The joke works because it keeps turning out to be true. 

Three decades after its creation, Postgres continues to adapt to new workloads, new architectural patterns, and new ways of building applications.

That longevity is not an accident. Enterprises continue to rely on Postgres because it provides a stable and trusted foundation for operational data.  While that foundation is unlikely to change, the scope of what organizations expect Postgres to do will continue to expand.  

As new workloads continue to emerge, much of the innovation will come through extensions that expand Postgres’s capabilities without sacrificing the stability that made it successful. In that sense, the future of Postgres may not be about reinventing the database itself, but continuously expanding what can be built on top of it.

The post The database storage problem is solved. Here’s what comes next. appeared first on The New Stack.

AI consumes a lot of data, but all-you-can-eat data in the world of agentic intelligence eventually loses its flavor. Simply increasing the sheer volume of databases, data repositories and data volumes does not necessarily enrich any given AI function’s ability to reason. 

Context, on the other hand, does.

We know that agents are only as intelligent as the context they have access to. In order to codify the inclusion of context into algorithmic logic for AI, technology vendors have extolled the virtues of using a knowledge graph to make their data more useful for agentic purposes.

AWS knows this story all too well. The organization’s sprawling datacenter footprint hosts vast pools of context on behalf of its customers, but that context often sits in a raw and unstructured form across data lakes, data warehouses, data lakehouses, databases, and data streams. It also permeates through the rarely-documented institutional knowledge that agentic engines rarely get access to.

All of which explains why the cloud giant used its AWS New York Summit today to introduce AWS Context, a new service that automatically maps the relationships that exist across a firm’s existing data into a knowledge graph and provides agentic search so AI agents in the organization can access what are governed data relationships, business rules, and domain knowledge at runtime. 

But weaving all this together is hard work. Knowledge graphs need more than simple keyword matching to work; they require structural and semantic traversal. This means they need to make multiple hops across various information silos and repositories so that they can aggregate context and (for example) be able to explain why cybersecurity vulnerability A is a factor of system compromise B, which has a core dependency link to codebase C, which executes in application D and risks taking users X, Y and Z offline. So how is AWS doing this?

A data lake of nuance & information

Mai-Lan Tomsen Bukovec, AWS vice president of technology (data and analytics) tells The New Stack that AWS Context provides a “data lake of nuance and information that AI agents swim in” to reason correctly and make the right decisions for the business. 

“This is no different from how humans work. When we take action, we depend on our own context about the domain, prior decisions and their outcomes, and other information.” – Mai-Lan Tomsen Bukovec, AWS.

“This is no different from how humans work,” Tomsen Bukovec says. “When we take action, we depend on our own context about the domain, prior decisions and their outcomes, and other information. With AWS Context, AI agents have all the nuance of every form of data in their business in a knowledge graph and in open data formats. AWS Context will make the difference between an AI agent simply taking an action versus making the right decision.”

Given the option to embrace this new service, software engineers will need to set out a plan of action and work out what to do first. For AI developers and data science professionals, this throws up the question of what to prioritize first when preparing existing enterprise data for context-aware agents using AWS Context capabilities and how they can control what data is (and isn’t fed) into the mouth of the beast.

Mercifully, it appears, options for control appear to exist.

“If developers want to exclude information from AWS Context, they will have the ability to prevent certain datasets, like test data or sandbox environments, from being included with AWS Context,” explains Tomsen Bukovec. “Because AWS Context is continuously updated as relationships between data resources changes, AI agents have the latest context available without any intervention from AI developers – and the control to set guardrails to exclude content that agents should not take action upon.”

Should developers trust this technology?

AWS Context extends the same knowledge graph technology that runs Amazon Quick, the organization’s AI work assistant that “connects scattered work” across applications and resources, including Slack, Microsoft Teams and Outlook, CRMs, databases, and documents.

So, should software developers place their trust here? After all, even once captured and connected, not all business context is useful. Some contextualizations could be corrupted, weak, fragmented and not productively useful for the business? Is AWS at risk of encapsulating context without considering how the data that comprises it is is quantified in terms of business usefulness?  

AWS has thought of this factor.

Because AWS Context uses the same knowledge graph technology that powers Amazon Quick, it can learn from usage patterns to make every interaction smarter. With AWS Context, the company says it is extending what was a personal knowledge graph into an organizational one i.e. a shared, governed context layer that agents and applications in an organization can draw from.

“Developers can govern and shape a dynamic and intelligent context layer that AI agents depend on to make the right decisions – AI agents won’t just get smarter as the models improve – they will be smarter because they have a vast amount of curated context at their fingertips.” – Tomsen Bukovec.

“AWS Context provides a data lake of context in graph and open data format,” clarifies Tomsen Bukovec. “That means that AI developers everywhere can use capabilities at the data layer to govern and shape a dynamic and intelligent context layer that AI agents depend on to make the right decisions. With this change, AI agents won’t just get smarter as the models improve – they will be smarter because they have a vast amount of curated context at their fingertips.”

Curated knowledge beyond a user’s personal graph

Existing Amazon Quick users will see that when AWS Context is enabled, Quick’s agents gain access to the broader enterprise knowledge graph, including cross-system relationships, business rules, and curated context that go beyond what any single user’s personal graph can provide. 

Tomsen Bukovec has also said that AWS Context gets smarter the more agents use it. As agents query the graph, it observes which sources produce correct results, which join paths agents rely on, and which curated rules get applied. It ranks sources by actual usage and shares what it learns across an organization, so when one agent discovers a correct join path or resolves a schema ambiguity, other agents pick it up, without requiring a human to re-curate the graph.

Any agent you put into production raises a governance question: what data can it reach, and can you show exactly what it accessed and under whose authority? The organization has explained that AWS Context answers both by making every query identity-aware.

Each call is designed to inherit the calling user’s identity access management (IAM) and Lake Formation permissions, so an agent can only see and traverse the relationships its identity is authorized to access. Because access runs through identity, every interaction is auditable. Security and compliance teams can verify what an agent accessed and under what authority, using the same controls.

AWS Glue Data Catalog

Related news to the arrival of AWS Context today saw the company also announce the preview of business context and semantic search functions for AWS Glue Data Catalog, the company’s centralized metadata repository for all data assets across various data sources. The new functions are designed to make it easier for humans and AI agents to discover and understand data. 

Also in this product stream, AWS now offers offer a preview of skill assets in Glue Data Catalog, a service designed to allow “data producers” (a somewhat arbitrary term that AWS applies to anyone who creates data, but is most likely a DBA or developer) to create skill assets. 

Associating skill assets to data assets gives agents additional context and instructions they can retrieve progressively for working with specific data without re-teaching it to every agent one prompt at a time. 

A renaissance of context engineering

Will this new drive from AWS herald the birth (or perhaps renaissance, the industry has been talking about this approach for some time) of context engineering as a sub-discipline of data science? It may well do… and if it does, it will likely drag role-based multi-agent orchestration along into the fray with it as we weave ever more complex interrelationship structures through enterprise data stacks.

If AWS or indeed the other hyperscalers or major frontier model companies starts acquiring more multi-model graph structure companies and vector database specialists, that could be the sign that things are cementing around context engineering at large. 

In the meantime, developers setting sail on the contextualized data lake of nuance are advised to wear a life jacket.

The post “A data lake of nuance for AI agents to swim in”: AWS Context gets shipshape on reasoning  appeared first on The New Stack.

AI is already inside most enterprises’ IT stacks, but it’s had a somewhat shambolic and unsystematic early adolescence. Employees use personal tools, teams adopt different models, different company departments get forced into corners by vendors who push closed stacks, and agents are beginning to act inside systems that were built for people. 

That makes AI invisible, fragmented, and hard to change later. 

AI access and control platform company Tailscale announced on Tuesday the results of its work to address and redress these imbalances with new capabilities for Aperture, the company’s flagship toolset designed to provide a stable layer for managing AI across changing models, tools, data sources, and agents.

Designed to enable software developers to control and orchestrate the arguably almost too-dynamic state of AI, Aperture now offers a new chat interface, universal data connectors for both MCP and APIs, and sandbox support. 

What makes agents useful, also makes them risky

Avery Pennarun, CEO and co-founder of Tailscale tells The New Stack that the “same mechanics” that makes AI agents useful also make them risky i.e. they can do in seconds what would take a person dozens of clicks, commands, and context switches. 

But he advises that the risk factor here is not really a matter of pitting humans against agents and trying to place one above the other in terms of potential fragility. He says that the real risk is “giving any actor too much room” to act without clear boundaries.

“With agents, that risk moves faster,” Pennarun says. “With humans, the weak point is often the control model itself. If security depends on a developer approving a long stream of prompts, they will either get slowed down or hit approval fatigue and start approving things by reflex. That is not much of a security model.”

“Agents need boring infrastructure around them – robust identity management, limited access controls, carefully tracked logs, and sandboxes – that boring outer shell is what lets them do useful work without making every developer’s laptop the place where all the risk lands,” Avery Pennarun, Tailscale CEO.

Interestingly, agents need boring infrastructure

For Pennarun, the answer lies in making sure agents have what he calls “boring infrastructure around them”, by which he means robust identity management, limited access controls, carefully tracked logs, and (where necessary) sandboxes to execute in before they are exposed to mission-critical datasets, applications, or both.

“That boring outer shell is what lets them do useful work without making every developer’s laptop the place where all the risk lands,” Pennarun clarifies. “The answer is not agentic control or human control alone. Humans set the policy and boundaries up front. Infrastructure enforces them. Agents operate inside them.”

Aperture can be defined as a centralized AI gateway built to monitor and route LLM requests in a secure manner using Tailscale’s identity layer to automatically authenticate “users” (a cohort which we now obviously expand to include both humans and machines), eliminating the need to distribute API keys to authenticate with each AI model.

The gateway holds the API keys securely, meaning that when a developer (or a container) makes a request, Aperture verifies who they are via their Tailscale identity and then automatically routes requests to upstream LLM providers such as OpenAI, Anthropic, and Google without requiring changes to existing tools or workflows.

Yeah, we use AI, dunno where

Given the amount of work-related activity currently happening on personal and free AI accounts, we might suggest that concerns here are validated i.e. organizations today can not see, govern, or recover the information streams at this level. Research cited by Axios found companies typically have 67 generative AI tools running across their systems, with 90% lacking proper licensing or approval. 

Tailscale has reemphasized the fact that AI providers are bundling models, chat interfaces, data access, and execution environments into closed stacks. Those bundles can make the first deployment easier, but they can also leave organizations locked into one provider’s models, tools, and roadmap and pricing. In a market where model quality, speed, and cost keep changing, that lock-in can quickly become a disadvantage. 

“Aperture is built to give developers a practical way to manage AI without locking down their choices. It makes approved AI tools easier to use, connects them to internal data with identity preserved, and gives agents controlled environments to work in.”

“AI agents are also changing the risk model. They can write code, call tools, browse systems, manipulate files, and run commands. In many setups, they do that with the same permissions as the person running them, which can expose local files, credentials, and internal systems if something goes wrong,” said Pennarun and team.

What it means for developers: a controlled environment for agents to work in

Aperture is built to give developers a practical way to manage AI without locking down their choices. It makes approved AI tools easier to use, connects them to internal data with identity preserved, and gives agents controlled environments to work in. It also keeps the AI stack essentially modular, so teams can keep experimenting with new models, interfaces, tools, and providers without starting over.

The new chat interface is a browser-based way to use approved AI models through Aperture. The interface supports switching between configured LLM providers and works with Aperture data connectors and sandboxes. The universal data connectors help AI tools reach internal systems, documents, APIs, and operational data without forcing every team to build its own integration path.

Teams can use Aperture’s chat UI, coding agents, agent frameworks, or implement custom interfaces through OpenWebUI or LibreChat. Sandbox support (available in private alpha at the time of writing) is designed to give AI agents controlled environments where they can complete work without acting directly on a user’s laptop, workstation, or unmanaged system.

Aperture is designed to work with API keys from major LLM providers and with tools, agents, and interfaces that can be configured to route through Aperture. 

AI stacks inevitably, constantly and persistently change

With the frontier model race apparently unlikely to slow down any time soon, the fact that the best model, interface, sandbox, and data connection will all keep constantly changing… combined with the need to juggle these balls across multi-cloud deployment instances (poly-cloud even, where one app is split into different component parts across more than one hyperscaler), organizations looking to harness AI effectively and securely will surely face challenges. 

The central technology proposition with Tailscale Aperture is that it gives software developers a stable layer for identity, access, and control, so teams can keep changing tools without losing track of who is doing what.

The post “Agents need boring infrastructure around them”: Why we need to take an interest in ‘invisible’ AI appeared first on The New Stack.

The Linux Foundation has long transcended its roots as a steward of the Linux kernel, emerging as a “foundation of foundations” spanning everything from cloud infrastructure and security, to digital wallets, and maps.

But the organization has been on a particular tear of late, becoming home to numerous AI-focused foundations and projects in the past twelve months alone, spanning agent communication protocols, agent security and governance, AI asset exchange, while on the foundation side there’s the Agentic AI Foundation (AAIF), the Tokenomics Foundation, and — now — the Appia Foundation.

The all-new Appia Foundation sits under the auspices of the Joint Development Foundation (JDF), a Linux Foundation entity that provides the legal and administrative infrastructure for organizations producing technical specifications and standards rather than code.

Announced on Wednesday, Appia’s mission is to produce open, modular specifications that give organizations across the AI supply chain a consistent, verifiable way to demonstrate that their systems meet the trust and compliance obligations placed on them — whether those come from regulators, customers, or international standards bodies.

Google, Microsoft, and OpenAI are among the 13 inaugural members, alongside a slew of industrial heavyweights.

A problem to solve

In most industries, proving that something’s safe is fairly routine. A new apartment block gets signed off by inspectors before the first tenant arrives. A kettle carries a safety mark because someone qualified tested it. The checking is so embedded that nobody thinks about it. AI has no equivalent yet — no common, recognized way for anyone in the supply chain to show that a system meets the bar, in a form the next party can actually rely on.

An example offered by the Appia Foundation illustrates how quickly the problem can compound in real scenarios. An AI tool used to screen job applicants wasn’t built by one organization: a developer created the underlying model, a second company adapted it for candidate assessment, a vendor connected it to the hiring systems, and the company’s own HR team configured it for their specific hiring criteria. The recruiters relying on it need to trust it’s reliable, while the applicants it screens want to know it’s fair. The company’s leaders need confidence it’s lawful. Regulators want evidence of how it performs. Each party is asking the same question — can this be trusted?

Today, most claims about AI trustworthiness amount to self-declaration — a company’s word that its system is safe, fair, or compliant, with no standardized way for anyone else to verify it. Craig Shank, executive director of the Appia Foundation, tells The New Stack that as a global, multi-stakeholder endeavor, the foundation is focused squarely on the “practical mechanics” of verifying an AI system against defined criteria, rather than merely stating that it’s trustworthy.

“Our specifications will enable transparent, attributable and traceable technical records of who demonstrated what against which criteria and when.”

“Our membership reflects the entire international value chain — the providers who build the platforms, the enterprises deploying them across critical industries, and the independent bodies that test them,” Shank says. “Our specifications will enable transparent, attributable and traceable technical records of who demonstrated what against which criteria and when. This is the exact type of objective data that courts, counterparties and regulators will need to determine where responsibility lies.”

The 13 inaugural members span a broad spectrum of industry — model and platform providers including Google, Microsoft, OpenAI, and Arm; industrial deployers including Siemens, Mastercard, Ericsson, Schneider Electric, and Mitsubishi Electric; and the assessment and governance bodies that will ultimately do the checking, including testing and certification firm Nemko, AI governance tooling company Naaia, and AI risk insurer Armilla AI.

A checklist for the age of AI regulation

AI regulations around the world are already moving from principles to active enforcement, and organizations are under pressure to prove that an AI system is safe and accountable. International standards bodies like ISO/IEC have done the work of defining what that should look like in principle, but translating that into something a regulator, a customer, or a procurement team can verify is another matter entirely. That gap is what Appia is built to fill.

The foundation will develop what it calls “conformity specifications” — modular, publicly available documents that translate international AI standards into concrete, assessable criteria. Think of existing ISO standards as the building code, and Appia’s specifications as the inspector’s checklist: the practical means of showing that a given AI system conforms to them.

A key feature of how the specifications are designed is what Appia calls “evidence pass-through.” Because AI systems are rarely built by a single organization — a model provider, an integrator, a deployer, and others may all have a hand — the specifications are structured so that conformity evidence produced at one layer carries forward to the next. A company deploying a third-party model, for example, wouldn’t need to re-establish what the model’s developer already demonstrated; it would only need to show conformity for its own configuration and use. Each party demonstrates what relates to its role, and no more.

The foundation is also explicit about what its specifications do and do not produce. Conformity — a technical result showing that a system meets defined criteria — is distinct from compliance, which is the legal status of having met a regulatory obligation. Appia produces the former; whether that satisfies the latter is down to the relevant regulator or jurisdiction. The specifications build on standards that already exist and produce the criteria that assessment bodies need, leaving the assessment itself to those equipped to perform it.

Appia is, by its own admission, early. The specifications are being drafted now in working groups open to all members, with initial focus areas including architecture, policy, and mapping the specs to existing regulatory obligations, among them the EU AI Act.

Jim Zemlin, CEO of the Linux Foundation, says that as AI regulation hardens into enforceable law, the industry needs somewhere neutral to do the work of building shared verification infrastructure — and that Appia is that place.

“The Appia Foundation establishes a neutrally governed environment where the entire industry can collaborate on a common assessment framework,” Zemlin says in a statement. “By building this infrastructure in the open, we are helping organizations reduce complexity, lower operational costs and build trust.”

The post Google, Microsoft, and OpenAI join forces to help create AI’s missing trust layer appeared first on The New Stack.

Databricks wants to erase the divide between the databases that run a business and the systems that analyze it. At its Data + AI Summit in San Francisco on Tuesday, the company introduced an architecture it calls Lake Transactional/Analytical Processing, or LTAP, built to collapse that split for AI agents.

Databricks started going down this path a while ago but made it concrete when it bought the serverless Postgres startup Neon and, later, Mooncake Labs in 2025. The bet here is that AI agents, not people, will become the primary users of the enterprise data stack, and that the infrastructure beneath them has to be rebuilt for them.

A breakthrough 40 years in the making

“For decades, complicated data infrastructure was a tax that teams were forced to pay,” said Ali Ghodsi, co-founder and CEO of Databricks, in the announcement. “Then agents arrived. In a matter of months, organizations effectively doubled their workforce, just not with humans. Agents write code, make calls, and run loops at a pace human teams never could. The infrastructure that powered the last era of computing is now the bottleneck that no one can afford. LTAP removes it.”

LTAP, Ghodsi said in his conference keynote on Tuesday, is “a breakthrough the industry has been working on for 40 years. We think we finally pulled it off.”

Historically, companies have had to run two kinds of databases. Online transactional processing systems handle the live operations of a business, like orders, payments, and inventory, in row-based formats tuned for fast writes. Online analytical processing systems then use what is essentially the same data for reporting and analysis in column-based formats specifically tuned for large scans. The two were kept apart for performance and reliability, and enterprises bridged them with ETL pipelines and replicas..

Databricks argues that agents need a different system because they can read live transactional data, reasoning over historical context, and act on both of them at once.

Earlier attempts to merge the two layers never quite worked, the company says, because hybrid transactional and analytical processing (HTAP) systems carried high costs and proprietary lock-in, while “zero-ETL” tools amounted to hidden change data capture, still leaving two copies of the data and the problem of data going stale.

What is LTAP?

LTAP unifies transactional and analytical data in a single storage layer, governed once and stored in open formats on cloud object storage, while keeping separate compute engines for each kind of work.

The design builds directly on Lakebase, the Postgres-based operational database Databricks introduced in June 2025, which the company describes as a “new category” that separates compute from storage and places the data in the lake in open formats.

Now, the company is extending Lakebase for what it calls business-critical workloads, adding native vector and full-text search, real-time event ingestion through Zerobus, part of its Lakeflow Connect ingestion service, and Git-style branching that lets an agent copy a database to experiment and then discard it.

“Agents love to just branch out and experiment with the data, try something else, and they want to do it quickly,” Ghodsi said. “They don’t want to wait ten minutes on a database to come up.”

Lakehouse//RT

The second piece is Lakehouse//RT, a real-time analytics engine, powered by a vectorized engine Databricks calls Reyden, that runs directly on Delta and Iceberg tables in the lakehouse.

Companies have long stood up separate, specialized systems to get millisecond query speeds, duplicating data into a “serving layer” that sits alongside the lakehouse. Databricks says Lakehouse//RT removes that layer, delivering millisecond-level latency on lakehouse data with no extra copies, pipelines, or governance gaps.

Databricks stresses the engines high concurrency. Mehrshad Setayesh, SVP of engineering at PointClickCare, says Lakehouse//RT “ran more than a third faster on average than our prior warehouse on our healthcare dataset, with 10x faster queries,” and that it removed the company’s need for a dedicated real-time system alongside its lakehouse.

Mooncake and Neon to the rescue

LTAP’s main pitch is that a single copy of the data can be stored once in open formats without the need for complex data pipelines. The Lakebase architecture, the company wrote last year, shares one storage layer across transactional and analytical workloads “without moving or duplicating it.”

Lakebase’s analytical speed comes from Mooncake, the startup Databricks bought to accelerate it. Mooncake mirrors Postgres changes into the lakehouse in real time, which is how transactions and analytics run on the same fresh data.

“Postgres changes are mirrored in real time to the lakehouse,” the company wrote when it announced the deal. Mirroring produces a second, columnar copy of the data, which is what makes the analytical queries fast.

Security, governance, auditing, and high availability, the company wrote, “only need to be implemented and managed once, on a single open foundation.”

The branching is the feature of this that was built specifically for agents — and a feature that is core to Neon, too. Because the data sits on object storage, an agent can fork a full database, test against it, and discard it, the way it would a Git branch. Databricks says even petabyte-scale databases can be copied in seconds, while on a traditional database, provisioning an instance takes minutes or hours and cloning production risks taking it down.

As Ghodsi noted in his keynote, agents love Postgres, but they do need better tools to work with them — and maybe better databases, too. “In the next 12 months, we’re going to see more software written than ever in the history of mankind,” he said. “All that software that your organizations are going to write using LLMs and coding tools need the database behind the scenes.”

What else is new?

LTAP was only one part of the company’s three-hour keynote. Like so many other enterprise vendors, Databricks is also thinking about how to get agent sprawl — and cost — under control. Databricks’ answer is Unity AI Gateway, a single control point for every model, agent, MCP server, and skill running in an organization. Among other features, it offers spending dashboards, budgets that can be set per team or per user, rate limits, and single sign-on across MCP servers.

The company also introduced Genie One, a general-purpose agent for business teams, fed by Genie Ontology, a new layer that builds a ranked graph of a company’s data with a PageRank-style algorithm it calls OntoRank.

Ghodsi also highlighted OpenSharing, a new protocol for sharing data, models, and agent skills across platforms (you may remember its predecessor Delta Sharing, but it is now a project under the Linux Foundation).

Databricks also debuted CustomerLake, a customer data platform aimed at marketing teams and announced an agreement to acquire Panther, a Python-based security company, to feed its Lakewatch security information and event management service.

Databricks’ moat?

It’s the data layer, though, and its data science history, where Databricks can really differentiate. At this point, it feels like every enterprise vendor, no matter their expertise, is adding agent builders, agent orchestration and governance tools. Databricks can be a relatively neutral player in this space — something Ghodsi also stressed in a press conference after the keynote.

But the company is also seemingly aware that while many of the other enterprise SaaS vendors can use their expertise and existing customer data that can feed AI agents as a moat, Databricks functions as more of a utility layer. It’s maybe no surprise then, that it is launching an industry-specific product like CustomerLake for the marketing industry that adds a pre-made product layer on data its customers already store on its platform.

The post Databricks wants to merge the two databases every company runs appeared first on The New Stack.

Today it was announced SpaceX will buy Anysphere, Inc., maker of AI coding agent Cursor, for $60 billion. 

The news comes a few days after SpaceX’s historic IPO listing, with the rocket and AI company stating in its SEC filing that the Cursor deal will likely close in Q3 2026. 

Developers can now wonder how the AI coding agent might change under the Musk umbrella.

April partnership brings June ownership

SpaceX and Cursor have been flirting about a potential acquisition for a couple of months now. 

Back in April, the pair inked a unique partnership, where Elon Musk’s company agreed to either pay $10 billion to the then-independent startup in a model-training collaboration or opt to buy the whole company later on for $60 billion. 

That day has now come. 

At the time, Cursor described its partnership with SpaceX as a way to accelerate its model training efforts, stating in a brief announcement blog post that Musk’s company would enable the startup to scale up intelligence via xAI’s Colossus infrastructure. 

For its part, SpaceX posted on X back in April that working with Cursor would allow it “to build the world’s most useful models.” 

SpaceX sets its sights on AI coding

It seems SpaceX has been eyeing Cursor’s talent for quite some time.

Even before the April partnership, back in March, Reuters reported that xAI had hired two engineers from Cursor. In fact, Peter Swimm, former principal product manager — Microsoft Copilot Studio, Microsoft, tells The New Stack he expects it’s largely engineering and AI talent that SpaceX hopes to gain from the new acquisition: 

What remains genuinely scarce is elite AI engineering talent and the teams that know how to build these systems at scale.

“The more interesting lens is to view it as an acqui-hire and talent consolidation play. The AI coding assistant market is crowded, features are converging rapidly, and long-term differentiation is proving difficult. What remains genuinely scarce is elite AI engineering talent and the teams that know how to build these systems at scale.” 

SpaceX may very well need that talent. As The New Stack wrote back in April, “SpaceX’s xAI has not had a coding hit since its grok-code-fast-1 model had its time in the sun.”

Though SpaceX’s recent IPO puts its valuation at an eye-watering $2+ trillion, its coding division has not been performing up to par, as Reuters reported in March when several aXI founders left the company. Cursor, meanwhile, rocketed to a $29.3 billion valuation at the end of 2025, scooping up $2.3 billion in Series D funding.

By bringing Cursor into its fold, SpaceX is likely hoping to score more engineering talent and level up its AI coding. 

What does it mean for developers? 

Swimm tells The New Stack he thinks Cursor users can expect better performance from the coding agent, assuming access to SpaceX’s deep resources.

What he says remains to be seen is whether the tool will “maintai[n] broad model support and ecosystem neutrality” or face sweeping changes à la Twitter when Musk morphed the social media company into X:

“For Cursor users, the question isn’t whether the product gets better. With significantly more resources behind it, it probably will. The question is whether it remains an independent platform optimized for developers or becomes another component in a larger corporate strategy.” 

Whoever owns the interface where developers spend eight hours a day gains visibility into how software gets built, which models get adopted, and ultimately where AI spending flows.

If that’s the case, he also predicts procurement evaluations will change, as enterprises may now assess the coding agent as one piece of Mr. Musk’s growing AI puzzle rather than an independent vendor. 

Bigger picture, Swimm says the SpaceX acquisition highlights where real strategic value likely now sits. He doesn’t see AI coding agents, themselves, as the gamechanger but the access they provide into developer workflows: 

“What it [the acquisition] does suggest is that access to developer workflows is becoming strategically valuable. Whoever owns the interface where developers spend eight hours a day gains visibility into how software gets built, which models get adopted, and ultimately where AI spending flows.” 

The post SpaceX acquires Cursor for $60 billion. Can it fix Musk’s coding division? appeared first on The New Stack.