AI researchers and labs have advanced by leaps and bounds in evaluating AI models for everything from safety and compliance to sycophancy and alignment. But it appears companies and developers are faced with a new, specific need: making sure that their AI system behaves as intended for their specific product or service.

In a bid to make that testing process simpler, Microsoft on Tuesday took the wraps off ASSERT, short for Adaptive Spec-driven Scoring for Evaluation and Regression Testing.

The open-source framework, Microsoft says, makes evaluating application-specific AI behavior easy by using AI to turn high-level, natural-language descriptions of goals, policies, or intended behaviors into thorough, scored tests that can be investigated.

ASSERT takes plain-language descriptions of an AI model’s expected behavior and policies, turns them into a structured set of acceptable and unacceptable behaviors, generates problem scenarios and test cases, runs them against the target system, and scores the results. It can also record the paths the AI system takes, including intermediate actions and tool calls, so developers can inspect where failures happen.

Devs can provide system context, tools, and constraints, too, if they want to further customize what the evaluations cover.

For example, a developer could specify that a document research AI agent shouldn’t send emails to people outside the company, limit confidential information to C-level executives, and provide concise summaries with prior context in mind. ASSERT will use those rules to generate test cases that check whether the system follows those rules on an ongoing basis.

The framework, according to Microsoft, fills a gap that broader, more general evaluations cannot when AI models are intended to behave in a manner that is shaped by an application or product’s context, policies, and tools.

“One of the things we’ve learned is that evaluations are absolutely critical to making good decisions,” said Sarah Bird, chief product officer of Responsible AI at Microsoft. “Because if you don’t understand the behavior of the AI system, it’s really hard to know if it’s meeting your organization’s bar […] What we found is that if you really want to have a trustworthy system, you should evaluate many more dimensions that are application-specific.”

Bird said ASSERT can be used to evaluate systems when they’re being built, after deployment, and even for continuous monitoring.

The release comes amidst a gradual but broader shift in the AI industry. As models grow more capable, researchers are focusing on repeatable testing and regression checks, with Stanford’s HELM, MLCommons’ AILuminate, and evaluation groups like METR rolling out benchmarks to measure how models behave under different conditions.

Meta is testing a new “Series” feature for Reels that’s designed to make it easier to keep up with serialized content on Instagram and Facebook, the company told TechCrunch.

Select creators will now be able to create a series using their Reels, both new and old. Each Reel will become an episode in a bigger story with a dedicated hub on their profile, mirroring an experience that’s already available on TikTok.

The launch of the new feature indicates that Meta is embracing longer-term viewing, rather than quick scrolling sessions, as is normally the case with short-form videos. With organized series, the social media giant is looking to bring audiences back repeatedly and build stronger viewing habits. As for creators, the new feature offers a more organized way to release episodic videos, like tutorials and challenges.

Meta told TechCrunch that it’s considering ways to monetize the new feature, but didn’t share specifics on what that could look like.

It’s worth noting that Instagram rival TikTok launched a Series feature back in 2023, allowing creators to post collections of premium content behind a paywall that viewers can purchase for access. It’s possible that Meta could follow a similar playbook with its own series feature in the future.

Meta says it’s already seeing serialized content take off, so its newest test feature is meant to make it simpler to catch every episode. For example, if a creator is doing a “10 days of healthier baking” series, they can bundle all ten videos into a single series. The series would appear in a new tab on the creator’s profile with its own page so people can find all the episodes, watch them in order, and pick up where they left off.

When viewers discover episodes of a series while scrolling their feed or the Reels tab, they will see the option to tap into the full series to see other episodes. Users also have the option to save a series to watch later or stay up-to-date.

For the testing phase, Meta is working with select creators and content producers who have already shared serialized content on Instagram and Facebook.