Microsoft has open-sourced an AI evaluation framework that converts natural-language requirements into executable tests, expanding its push into enterprise AI governance as organizations struggle to validate agent behavior before production deployments systematically.

The framework, called ASSERT (Adaptive Spec-driven Scoring for Evaluation and Regression Testing), generates evaluation scenarios, datasets, metrics, and scorecards from written specifications, product requirements, and governance documents, Microsoft said in a blog post announcing the release.

“Agents fail in ways that are hard to see,” Microsoft wrote in the blog post. “They drift from policy, produce unsafe outputs in edge cases, and behave differently in production than they did in testing. Generic benchmarks do not catch these failures because they are not built around your policies, your agent, or your use case.”

Rather than requiring developers to manually create evaluation suites, ASSERT translates written intent into reusable tests that can be integrated into AI development pipelines, the company said in the blog post.

With ASSERT, Microsoft is entering an increasingly competitive AI evaluation market that already includes platforms such as LangChain’s LangSmith, Braintrust, Patronus AI, Galileo, Arize AI’s Phoenix, and Promptfoo, which help enterprises benchmark, monitor, and validate large language model applications.

Behavioral testing remains immature

The release comes as enterprises rapidly expand AI agent deployments while formal evaluation practices remain the exception rather than the rule.

“Most organizations, in fact, 99% of them, do not evaluate any AI agents pre-production,” said Anushree Verma, senior director analyst at Gartner.

According to Verma, the industry’s next competitive advantage will depend less on advances in reasoning models than on how effectively organizations simulate and stress-test AI agents before deployment.

“The next competitive moat in agentic AI is not about the sophistication of reasoning models or the underlying architecture,” she said. “It will be about the depth and realism of the training environment through agentic simulation, particularly for mission-critical deployments.”

Gartner estimates that by 2029, more than 75% of domain-specific agents designed without agentic simulation in regulated industries will fail to deliver value.

Forrester sees enterprises moving toward behavioral evaluation but says most organizations have yet to make it a formal production requirement.

“Most enterprises are still in an intermediate stage where behavioral evaluation is inconsistently applied rather than treated as a formal production gate,” said Biswajeet Mahapatra, principal analyst at Forrester.

According to Forrester data, more than 45% of organizations are already using AI agents, and another 25% are piloting them, yet many continue to struggle with scaling because of immature governance and limited operational rigor.

“The net is that behavioral evaluation is becoming important, but for most organizations it is still ad hoc or tool-driven rather than a standardized release gate enforced across the lifecycle,” Mahapatra said.

AI judges still need human oversight

Microsoft said ASSERT uses large language models as judges, with model-generated evaluations agreeing with human reviewers 80% to 90% of the time in the company’s internal validation.

That level of agreement can help automate large portions of AI testing, but should not be treated as a standalone governance mechanism, Mahapatra said.

“An 80% to 90% agreement rate with human reviewers indicates strong alignment but is not sufficient as a standalone control for governance or compliance,” he said.

Instead, enterprises should adopt layered oversight where AI evaluates AI at scale while humans retain supervisory accountability for high-risk, regulated, or ambiguous scenarios. Buyers should also watch for bias, consistency issues, and overreliance on a single model acting as both generator and evaluator, he added.

Open source reduces lock-in, not governance risk

Microsoft released ASSERT under the MIT open-source license, allowing organizations to inspect, modify, and integrate the framework into existing AI development workflows.

But open sourcing a framework does not eliminate questions around evaluation neutrality, Mahapatra said.

“Open sourcing under an MIT license reduces lock-in concerns and enables broad interoperability across model ecosystems,” he said. “However, it does not fully eliminate trust or conflict-of-interest questions because the originating vendor still influences how evaluation criteria, scoring logic, and definitions of acceptable behaviour are encoded.”

Instead of relying on a single evaluation framework, enterprises should validate AI systems against multiple evaluation approaches and retain ownership of internal evaluation policies, he said.

Between 2029 and 2032, every currently supported long-term support (LTS) version of Java will reach end-of-support within a single three-year window: Java 17 in 2029, Java 8 in 2030, Java 21 in 2031, and Java 11 in 2032.

On paper, this looks like a manageable upgrade cycle. In practice, it creates a collision of timelines that most enterprises have failed to forecast. Organizations attempting to modernize incrementally—moving application by application, version by version—are operating on a model that the calendar has already rendered obsolete.

The primary danger here is the illusion of time. Traditional modernization plans rely on sequential upgrades and controlled pacing. However, when every major Java version expires in the same compressed window, sequential planning collapses. By the time this becomes obvious, organizations will be forced into reactive mode, making rushed decisions under extreme pressure.

The modernization illusion

For organizations planning traditional stepwise upgrades—Java 8 to Java 11 to Java 17 to Java 21—this convergence elevates a routine maintenance task into a structural crisis. Enterprises with large Java estates will be forced to upgrade multiple applications across multiple versions simultaneously to maintain security compliance and business continuity. Waiting until the late 2020s to act guarantees a modernization process under emergency conditions.

While modern Java versions maintain strong backward compatibility, they cannot offset the drag of what enterprises are carrying forward: decades of accumulated technical debt.

In large Java environments, technical debt is pervasive. It exists as unused libraries, obsolete logic, forgotten dependencies, and dormant features—quietly inflating the size, risk, and complexity of every modernization effort. In many organizations, a significant portion of the codebase no longer executes in production, yet it still consumes developer attention, security oversight, and planning effort.

As codebases grow older and larger, this drag compounds. What looks like a simple version upgrade on a roadmap becomes a massive operational burden in practice.

Why incremental planning fails

Most modernization strategies assume that upgrades can be sequenced and absorbed gradually. That assumption is now dangerous. When multiple Java versions reach end-of-support in the same narrow window, enterprises don’t face a single modernization project—they face parallel modernization across their entire estate.

This shifts the challenge from engineering complexity to organizational capacity.

Consider a typical enterprise with 100 developers. If even a fraction of their time is spent maintaining, investigating, or working around unused and obsolete code, the organization burns meaningful engineering capacity on work that delivers no business value. Multiply that across dozens or hundreds of applications, and the bottleneck becomes clear: modernization is limited by people, not frameworks.

Parallel modernization requires parallel capacity—something most organizations haven’t budgeted for.

This explains why traditional approaches struggle to scale. Tools that analyze code in isolation cannot distinguish what actually matters in production. Without clear visibility into what code is relevant, organizations default to caution, effectively converting their timelines into risk.

The real bottleneck: developer capacity

The Java modernization crunch is a crisis of resource allocation, not a technology problem.

Every hour developers spend maintaining obsolete code or investigating unused dependencies is an hour lost to modernization. When organizations face simultaneous upgrades across multiple applications, human capacity becomes the limiting factor. Sequential planning and parallel modernization require the time and capacity most enterprises no longer have.

Organizations that delay action are consuming their flexibility rather than preserving it. Each year of inaction increases the volume of code that must be moved, reviewed, secured, and modernized within the same fixed window. By the time deadlines become unavoidable, the only remaining options are compression, shortcuts, and uncomfortable trade-offs.

A different way to think about readiness

The organizations that navigate this transition successfully will prioritize clarity over immediate upgrades.

Modernization at scale requires an accurate understanding of what actually matters in production before attempting to move it forward. Without that visibility, every upgrade effort inherits unnecessary complexity, consumes excess capacity, and introduces avoidable risk.

The goal is not simply adopting better tools, but reducing the structural load enterprises carry into modernization. Leaner systems modernize faster. Simpler estates scale better. Complexity compounds under time pressure.

The timeline is already set

The Java modernization crunch is a timing problem that is already locked in.

Enterprises that treat the next few years as business-as-usual will discover that sequential plans cannot survive compressed timelines. Those that confront technical debt now—before the pressure hits—will find the coming transition difficult but manageable. Those that don’t will face rushed decisions and permanent trade-offs.

By the time 2029 arrives, the window for gradual modernization will have closed. The calendar won’t wait for us to be ready.

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New Tech Forum provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Send all inquiries to doug_dineley@foundryco.com.

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Modern AI systems have evolved beyond the simple chatbots that quickly became popular. Now they use semantic tools to manage workflows and link machines to machines, providing a flexible and effective framework for the next generation of business automation. What you used to build in Microsoft’s Power Platform or construct inside Biztalk is now an agent, built around large language models (LLMs) that can parse both your data and the APIs that you want to use your data with, orchestrating workflows with a level of autonomy that traditional tooling can’t match.

That shift has offered new opportunities, much like those that came with business platforms like Microsoft Dynamics and Salesforce. Here, tools built to solve one set of business problems could be turned into applications that could be sold to other companies. What worked for you to solve one of your problems could now be an added revenue stream, sold through platform marketplaces that helped customers manage installations and customizations.

Agents are business applications now

Modern agents are much like those business applications. Often developed to solve a specific need, but quicky adopted by organizations and refactored to apply enterprise standards (using tools like the Agent Governance Toolkit and frameworks like Microsoft’s Agent Framework), they’re rapidly maturing and are ready to be shared more widely. The process of sharing needs to be curated and controlled, and, if possible, tied to a revenue stream.

There’s certainly some urgency here. Until recently, subsidized tokens have kept costs artificially low. Now companies like GitHub and Anthropic are moving to a more sustainable (for them) pricing model, increasing the cost of inferencing and squeezing companies’ AI budgets. As a result, switching AI projects away from a cost to a revenue source is high on CIOs’ agendas. If those tuned and trained agents can be sold on a marketplace, then that token budget can be justified.

Microsoft has always been a company built on partner relationships, starting with individual developers and working all the way up to the largest software companies and consultancies. That reach is key to helping partners extract as much value as possible from their agents, as it allows Microsoft to integrate its partner sales tools into its own products and services, as well as into other platforms.

Extending the Microsoft Marketplace for AI developers

We’re already familiar with many of Microsoft’s marketplaces, built into individual tools like Teams, into platforms like Microsoft 365 or Visual Studio, or into the Windows Store. Now the company is doing the same for AI developers, extending Microsoft Marketplace to software agents. Announced at Build 2026, the updated Microsoft Marketplace provides ways to publish code—apps and agents—developed across all of Microsoft’s development platforms, including Copilot Studio, opening the marketplace up to traditional and non-traditional developers alike.

Perhaps the most important aspect of this new Marketplace is its own intelligence, using context to expose your code to the right audience. If you’ve developed an agent for use with Microsoft 365, it will be exposed inside the Microsoft 365 Copilot Agent Store; for Visual Studio, in the Visual Studio Marketplace; or for Teams, in the Microsoft Marketplace. All of these are different views on the same back end, using AI to ensure that the relevant agents are displayed.

Replacing search with Intelligent Discovery

This is extended by another new service, Intelligent Discovery, which adds natural language support to search, using AI to infer user intent and highlight the most relevant tools. Building on the familiar metaphor of the search bar, the initial smart search model offers a freeform way to explore the Marketplace. While there are suggested prompts, they’re not necessary. The search tooling allows you to generate comparisons between tools using your own criteria, with the Marketplace AI generating views based on your requirements.

Microsoft’s aim here is to shift discovery from keywords to use cases, so that buyers can quickly get the tools they need without having to evaluate different solutions, before completing a purchase. By handing that aspect of the buying process over to Marketplace’s AIs, customers can go straight to trials or even to buying agents and applications.

For a tool like this to be successful it needs to be trustworthy. By building it on top of the same development frameworks as your agents, Microsoft can take advantage of the AI guardrails built into Microsoft Foundry as well as low-level tooling like the Agent Governance Framework. Restricting the intelligent search to the Marketplace catalogue reduces the risk of hallucination, as output is grounded in Marketplace data and metadata.

A developer-friendly marketplace

Microsoft is providing tooling to help developers get their listings right. According to Cyril Belikoff, Microsoft’s vice president of Commercial Cloud and AI, “We actually have a separate AI tool that we give to software companies to optimize their listings, called a listing optimizer, funny enough, and that listing optimizer reviews their listing and then provides them with particular guidance on how to best improve it, so that it can be best discoverable in today’s search world.”

You can expect the listing optimizer to be tuned to work with the new Marketplace tooling, but for now it still focuses on traditional search. As Marketplace is a B2B platform, there’s a lower risk of spam applications, but even so, Microsoft remains aware of the possibility of a new system being gamed, and will be rolling out Intelligent Discovery carefully, monitoring its performance as more customers get access over time.

Having a new discovery method is one thing; getting quality AI applications in the Marketplace is another. Microsoft is validating all code submitted, though the criteria will differ between target platforms. An agent built for Teams will be treated differently than one built on Microsoft 365’s WorkIQ. It’s an approach that allows Microsoft to support new standards as they become available.

Alongside its agent development tooling, Microsoft is rolling out a new set of guidelines and processes to help developers get ready to sell their agents. Hosted on GitHub, these offer code templates as well as a link to the App Advisor guidance tools.

Still gaps to fill

This first release of Intelligent Discovery is promising, but some key features are missing. With agent token costs an increasing problem for businesses, it would be nice to see tools that help predict costs, integrating with finops tooling. We’re living in an age of shadow AI, and putting the AI we used to buy with credit cards in Microsoft Marketplace is one way to shine a light on those shadows — bringing the necessary control and governance to AI purchases, and maybe even providing support for site licensing.

Microsoft Marketplace is becoming a useful resource for AI application developers. It encompasses the entire development life cycle: offering tools that can help you build agents, the models that you need to power your agents, and finally a way to monetize your work. There’s a longer-term opportunity here, for both the Marketplace and Intelligent Discovery to offer Model Context Protocol (MCP) interfaces, ensuring that tooling and tool discovery become part of the developer workflow, and making developers aware of new tools that might help solve a problem or simplify a task.

The ability for attackers to leverage automatic install script execution in npm will finally come to an end when expected changes arrive from GitHub in July. Coders will still be able to enable the function, but the default setting will block it. 

In V12, default settings are changing, GitHub said in its changelog, noting, “it turns an npm install behavior that runs automatically today into one you explicitly opt into.” 

Specifically, the post said, “allowScripts defaults to off: npm install will no longer execute preinstall, install or postinstall scripts from dependencies unless they are explicitly allowed in your project. This includes native node-gyp builds; a package with a binding.gyp and no explicit install script still gets blocked, because npm runs an implicit node-gyp rebuild for it. Prepare scripts from git, file, and link dependencies are blocked the same way.”

Analysts, consultants, and users generally applauded the change, but said that it would only narrow the exposure to supply chain attacks instead of eliminating it. 

Attacks likely to move elsewhere

Sonu Kapoor, maintainer for CVE Lite CLI in the OWASP Incubator Project, said that this change is likely to force the supply chain attacks that leveraged the automatic execution to move elsewhere.

“This does not eliminate npm supply chain risk, it removes a major automatic execution path,” Kapoor said. “Attackers can still move to other paths: malicious package code that runs at application runtime, compromised maintainer accounts, dependency confusion, typo-squatting, poisoned GitHub Actions workflows, malicious transitive dependencies, or stolen publishing tokens. This closes one very dangerous door, but it does not secure the whole house.”

Still, attacks leveraging the setting have been regularly used in supply chain attacks. 

However Alan Parkinson, director of secure medical device firm Threat Detective, said more sophisticated attackers have already moved beyond this hole. 

“The install script attack vector has been known for years,” Parkinson said. “Most security teams marked it as low risk and moved on to higher risk threats. What raised its profile wasn’t the technical exploitability changing, it was a run of high-profile victims and some threat actors openly chasing notoriety.”

He added, “the pre and post install scripts was never a clever attack vector to begin with. Running code from an install hook is crude and noisy, which is why it caused such visible damage. The more capable actors are already moving to other methods, so v12 mainly shuts the door on less sophisticated threat actors.”

Although GitHub declined an interview, Zach Steindler, a GitHub principal engineer, answered InfoWorld’s questions by email. He said the volume and pace of supply chain attacks forced the default settings change. 

“We’ve seen attackers target these capabilities to quickly propagate attacks from one compromised package to many. Years of security and usability research have shown that it’s not enough to make secure functionality available; the secure path has to be the default in order for it to be widely adopted,” Steindler said.

He added, “we believe that these changes are a great way to provide high impact secure defaults while still providing the option for some users to fall back on functionality they might need in some circumstances.”

Change overdue

Sanchit Vir Gogia, chief analyst at Greyhound Research, said that GitHub was the last of the repositories to make the setting default change. “Rivals moved first: Yarn, pnpm and Bun all block third party install scripts by default in their own ways,” Gogia said. “Npm is not inventing a new doctrine. It is finally adopting one.”

Steindler didn’t dispute Gogia’s comment. 

“It’s not easy being the stewards of the largest package repository in the world. Community consensus on what security capabilities should be standard, and when it’s okay to make breaking changes shifts over time. From our continual conversations with the community, it was clear it was time to make this change,” Steindler said.

“The recent attacks are alarming,” he noted, “but stewarding these package repositories is a multi-decade effort, not just a moment in time. As attacks evolve, so will our defensive security capabilities. We’re in this for the long haul.”

Gogia said that the change, although overdue, is a good one. 

“Npm is removing one of the most comfortable hiding places for software supply chain risk: code that executes the moment a developer types install,” Gogia said. “With npm v12, execution becomes something that must be approved, recorded in the project, and committed for review. That is not a design adjustment. It is a change in control philosophy.”

Bad defaults become infrastructure

Gogia had his own take on why GitHub waited so long.

“Npm waited because its risky default acquired a constituency. As far back as 2016, npm’s own position was that the convenience of install scripts outweighed the worm risk, with an opt-out flag for the cautious. The trade-off was a documented product decision, not an oversight,” he said.

“The trouble with bad defaults is that they become infrastructure,” he added. “Native module builds, browser installers such as Playwright and Cypress, Electron download flows and Husky hooks all grew around automatic execution. Turning it off became less a technical adjustment and more a constitutional reform.”

Liability changed hands

The real pressure for the change, however, came from regulators.

“The deeper answer is that the liability changed hands. Once regulation such as the EU Cyber Resilience Act and securities disclosure rules placed supply chain failure on corporate balance sheets, a documented unsafe default became indefensible,” Gogia said. 

Kapoor agreed that long-used procedures enabled this security hole to survive longer than it should have. 

“The reason this likely was not done long ago is compatibility,” he said. “Install scripts are not only used by attackers. Many legitimate packages use them to compile native modules, download platform-specific binaries, generate files, or complete setup steps. Changing the default breaks assumptions that have existed in the npm ecosystem for years. That is why these security changes often arrive slowly. The safer default is obvious from a security perspective, but painful from an ecosystem compatibility perspective.”

In addition, he noted, “the bigger point is that package managers are moving from implicit trust to explicit trust. That is the right direction. Developers should have to approve which dependencies are allowed to execute code during install. But approval cannot become a blind checkbox. Teams need visibility into which package wants to run a script, whether it is direct or transitive, why it is there, and whether it belongs in the project at all.”

Kapoor added that this change matters because install-time execution often happens in privileged environments with access to tokens, secrets, internal registries, build artifacts, or deployment paths. “Even if the script does not compromise production directly, it may be able to steal enough context to support the next stage of an attack,” he said.

Value in the pain

Cybersecurity consultant Brian Levine, executive director of FormerGov, agreed that the closing of this security hole is a very good thing. 

“It seems like virtually every major supply chain attack of the last decade has had the same original sin: code that ran automatically because the ecosystem let it. Npm finally closing that door by default is overdue, but it’s genuinely significant. This is the package manager for hundreds of billions of downloads a month,” Levine said.

“When npm changes its defaults, it changes the security posture of practically every enterprise dev environment on the planet. It may have been the last large code repository to still allow this kind of automated execution.”

Levine added that this change might not merely stop a security hole, but the new process may meaningfully improve security. 

“There’s actually something valuable buried in this migration pain. Having developers explicitly approve which packages can run code and commit that list to source control is a form of software supply chain governance that many organizations never had,” Levine said. “It creates an auditable record which is meaningful, especially for regulated industries.”