It looks as if virtually each week for the final two years since ChatGPT launched, new giant language fashions (LLMs) from rival labs or from OpenAI itself have been launched. Enterprises are onerous pressed to maintain up with the large tempo of change, not to mention perceive find out how to adapt to it — which of those new fashions ought to they undertake, if any, to energy their workflows and the customized AI brokers they're constructing to hold them out?
Assist has arrived: AI functions observability startup Raindrop has launched Experiments, a brand new analytics characteristic that the corporate describes as the primary A/B testing suite designed particularly for enterprise AI brokers — permitting firms to see and examine how updating brokers to new underlying fashions, or altering their directions and power entry, will influence their efficiency with actual finish customers.
The discharge extends Raindrop’s present observability instruments, giving builders and groups a solution to see how their brokers behave and evolve in real-world circumstances.
With Experiments, groups can observe how modifications — comparable to a brand new instrument, immediate, mannequin replace, or full pipeline refactor — have an effect on AI efficiency throughout tens of millions of person interactions. The brand new characteristic is out there now for customers on Raindrop’s Professional subscription plan ($350 month-to-month) at raindrop.ai.
A Information-Pushed Lens on Agent Growth
Raindrop co-founder and chief expertise officer Ben Hylak famous in a product announcement video (above) that Experiments helps groups see “how actually something modified,” together with instrument utilization, person intents, and difficulty charges, and to discover variations by demographic components comparable to language. The purpose is to make mannequin iteration extra clear and measurable.
The Experiments interface presents outcomes visually, displaying when an experiment performs higher or worse than its baseline. Will increase in detrimental alerts would possibly point out larger job failure or partial code output, whereas enhancements in constructive alerts may replicate extra full responses or higher person experiences.
By making this knowledge simple to interpret, Raindrop encourages AI groups to strategy agent iteration with the identical rigor as trendy software program deployment—monitoring outcomes, sharing insights, and addressing regressions earlier than they compound.
Background: From AI Observability to Experimentation
Raindrop’s launch of Experiments builds on the corporate’s basis as one of many first AI-native observability platforms, designed to assist enterprises monitor and perceive how their generative AI methods behave in manufacturing.
As VentureBeat reported earlier this 12 months, the corporate — initially referred to as Daybreak AI — emerged to deal with what Hylak, a former Apple human interface designer, referred to as the “black field drawback” of AI efficiency, serving to groups catch failures “as they occur and clarify to enterprises what went flawed and why."
On the time, Hylak described how “AI merchandise fail continually—in methods each hilarious and terrifying,” noting that in contrast to conventional software program, which throws clear exceptions, “AI merchandise fail silently.” Raindrop’s unique platform targeted on detecting these silent failures by analyzing alerts comparable to person suggestions, job failures, refusals, and different conversational anomalies throughout tens of millions of each day occasions.
The corporate’s co-founders— Hylak, Alexis Gauba, and Zubin Singh Koticha — constructed Raindrop after encountering firsthand the issue of debugging AI methods in manufacturing.
“We began by constructing AI merchandise, not infrastructure,” Hylak advised VentureBeat. “However fairly rapidly, we noticed that to develop something critical, we would have liked tooling to grasp AI habits—and that tooling didn’t exist.”
With Experiments, Raindrop extends that very same mission from detecting failures to measuring enhancements. The brand new instrument transforms observability knowledge into actionable comparisons, letting enterprises take a look at whether or not modifications to their fashions, prompts, or pipelines truly make their AI brokers higher—or simply completely different.
Fixing the “Evals Go, Brokers Fail” Downside
Conventional analysis frameworks, whereas helpful for benchmarking, not often seize the unpredictable habits of AI brokers working in dynamic environments.
As Raindrop co-founder Alexis Gauba defined in her LinkedIn announcement, “Conventional evals don’t actually reply this query. They’re nice unit checks, however you’ll be able to’t predict your person’s actions and your agent is working for hours, calling tons of of instruments.”
Gauba stated the corporate persistently heard a typical frustration from groups: “Evals go, brokers fail.”
Experiments is supposed to shut that hole by displaying what truly modifications when builders ship updates to their methods.
The instrument permits side-by-side comparisons of fashions, instruments, intents, or properties, surfacing measurable variations in habits and efficiency.
Designed for Actual-World AI Conduct
Within the announcement video, Raindrop described Experiments as a solution to “examine something and measure how your agent’s habits truly modified in manufacturing throughout tens of millions of actual interactions.”
The platform helps customers spot points comparable to job failure spikes, forgetting, or new instruments that set off surprising errors.
It will also be utilized in reverse — ranging from a recognized drawback, comparable to an “agent caught in a loop,” and tracing again to which mannequin, instrument, or flag is driving it.
From there, builders can dive into detailed traces to seek out the basis trigger and ship a repair rapidly.
Every experiment gives a visible breakdown of metrics like instrument utilization frequency, error charges, dialog period, and response size.
Customers can click on on any comparability to entry the underlying occasion knowledge, giving them a transparent view of how agent habits modified over time. Shared hyperlinks make it simple to collaborate with teammates or report findings.
Integration, Scalability, and Accuracy
In response to Hylak, Experiments integrates straight with “the characteristic flag platforms firms know and love (like Statsig!)” and is designed to work seamlessly with present telemetry and analytics pipelines.
For firms with out these integrations, it may well nonetheless examine efficiency over time—comparable to yesterday versus immediately—with out extra setup.
Hylak stated groups usually want round 2,000 customers per day to supply statistically significant outcomes.
To make sure the accuracy of comparisons, Experiments displays for pattern measurement adequacy and alerts customers if a take a look at lacks sufficient knowledge to attract legitimate conclusions.
“We obsess over ensuring metrics like Activity Failure and Person Frustration are metrics that you just’d get up an on-call engineer for,” Hylak defined. He added that groups can drill into the particular conversations or occasions that drive these metrics, making certain transparency behind each combination quantity.
Safety and Information Safety
Raindrop operates as a cloud-hosted platform but additionally gives on-premise personally identifiable info (PII) redaction for enterprises that want extra management.
Hylak stated the corporate is SOC 2 compliant and has launched a PII Guard characteristic that makes use of AI to mechanically take away delicate info from saved knowledge. “We take defending buyer knowledge very severely,” he emphasised.
Pricing and Plans
Experiments is a part of Raindrop’s Professional plan, which prices $350 monthly or $0.0007 per interplay. The Professional tier additionally contains deep analysis instruments, subject clustering, customized difficulty monitoring, and semantic search capabilities.
Raindrop’s Starter plan — $65 monthly or $0.001 per interplay — gives core analytics together with difficulty detection, person suggestions alerts, Slack alerts, and person monitoring. Each plans include a 14-day free trial.
Bigger organizations can go for an Enterprise plan with customized pricing and superior options like SSO login, customized alerts, integrations, edge-PII redaction, and precedence help.
Steady Enchancment for AI Methods
With Experiments, Raindrop positions itself on the intersection of AI analytics and software program observability. Its concentrate on “measure fact,” as said within the product video, displays a broader push inside the business towards accountability and transparency in AI operations.
Moderately than relying solely on offline benchmarks, Raindrop’s strategy emphasizes actual person knowledge and contextual understanding. The corporate hopes it will enable AI builders to maneuver quicker, establish root causes sooner, and ship better-performing fashions with confidence.
