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.

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.