Do you know how your AI app is behaving? What about how that behavior might have changed from yesterday? For most teams, the ability to answer these remains out of reach, especially once an application is launched and being used by real users. While teams might be able to create a partial understanding of behavior for these applications during initial development through performance monitoring or benchmarks, their confidence in this understanding erodes by the time the apps are deployed at scale. And this lack of confidence ends up having a cascading impact on which applications actually end up making it to production.

To unlock the full potential of AI, you need to be confident in their apps’ behavior from development to deployment to continued usage. But why does this feel so out of reach for most of us today?

The confidence gap 

Software teams have been shipping with confidence for decades. These teams define how their applications should perform, and then use standard tests and benchmarks to measure and validate whether the code is working as intended across the development lifecycle. When a test fails, there’s clear processes for triage, root cause analysis, and resolution to get it back online quickly, with minimal production impact. 

Of course there’ve been efficiencies added over the years but, for the most part, this has been enough. The code itself doesn’t change that often, and it’s pretty straightforward to test whether the same outputs are produced from a set of inputs. Even as complexity has increased to handle things like ephemeral cloud services, larger scale distributed systems, and increasing dependencies—testing has kept pace to help teams build and deliver more impactful solutions that align with strategic business objectives and help maintain a competitive edge.  

AI broke the mold. Gone are the days of fixed inputs, defined outputs, and predictable changelogs. By design, AI systems are non-deterministic with the same input able to return a variety of potential outputs. Additionally, there are constant shifts across data, usage, models, prompts, etc. from one day to the next. Not only is it challenging to understand when these shifts happen, but also to trace the impact of these shifts back to specific components to resolve or understand the extent they’ve been propagated throughout these systems, especially when there are multiple models daisy chained together. 

AI requires teams to move beyond this fixed understanding of performance monitoring and testing. To reinject confidence and bridge the gap from development to production for AI, it’s time to move to adaptive behavioral testing.

Build confidence through adaptive behavioral testing 

With adaptive behavioral testing, you can establish a more complete understanding of behavior as a whole, and quantify that behavior into something you can test and measure continuously. But how do you get there?

First, it’s about removing uncertainty as you build your definition of desired behavior and adapt it for the future. You need to take into account a richer, more robust set of properties for your app—not just the inputs and outputs related to what it produced but all of the intermediates related to how it behaved to achieve those outputs. And rather than test these properties against a single threshold or summary statistic, you need to test based on their distributions or range of acceptability.

Evaluation metrics and benchmarks can be a great place to start, but alone aren’t enough. Your teams will never be able to create evals for every possible edge case that could happen in production. And even if that were possible, it would only represent what’s true today, not what could happen in the future. Plus, these benchmarks are designed to only measure against the end outputs, but stop short of measuring and understanding the behavior to get there.

By quantifying and understanding your app’s behavior more completely, you can then define desired behavior. This lets you detect when there are changes from that desired state. Further, you need to understand what causes the changes when they do happen. And then be able to resolve issues as they arise or adaptively adjust your definition of what’s acceptable for desired behavior. 

Finally, you need to be able to continuously improve your AI app, without degrading behavior. Teams can leverage the same adaptive tests to confidently roll out updates, swap in components, or add new features, without needing to take the app offline or kicking off lengthy new research and development cycles. 

This adaptive behavioral testing is what gives enterprise AI teams the coverage, clarity, and continuity necessary for productionizing AI apps and keeping them in production.

Productionize faster and maximize AI uptime

By having a continued understanding of consistent and reliable behavior, teams are able to build the confidence they need to ship higher value products faster, while minimizing risk to the business. Teams no longer need to build in a vacuum, and are able to account for the uncertainty of production usage while constantly adapting applications incrementally over time. Ultimately resulting in fewer production surprises and the ability to catch gradual shifts before users do. Plus, with a shared comprehensive view of desired behavior, the silos between development to production break down, resulting in faster and more predictable updates. 

What does this mean for you? You now have the bandwidth to tackle higher value AI use cases by leveraging the same repeatable and measurable processes to mitigate risk. Your business gets more impactful applications and your team can ship faster with confidence. 

Want to gain confidence in your AI applications? Reach out to Distributional to learn more.