It looks as if nearly 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 exhausting pressed to maintain up with the huge tempo of change, not to mention perceive methods 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 purposes observability startup Raindrop has launched Experiments, a brand new analytics function that the corporate describes as the primary A/B testing suite designed particularly for enterprise AI brokers — permitting corporations to see and evaluate how updating brokers to new underlying fashions, or altering their directions and power entry, will impression their efficiency with actual finish customers.
The discharge extends Raindrop’s present observability instruments, giving builders and groups a approach to see how their brokers behave and evolve in real-world circumstances.
With Experiments, groups can observe how adjustments — comparable to a brand new software, immediate, mannequin replace, or full pipeline refactor — have an effect on AI efficiency throughout hundreds of thousands of consumer interactions. The brand new function is obtainable now for customers on Raindrop’s Professional subscription plan ($350 month-to-month) at raindrop.ai.
A Information-Pushed Lens on Agent Improvement
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 software utilization, consumer intents, and challenge charges, and to discover variations by demographic components comparable to language. The objective is to make mannequin iteration extra clear and measurable.
The Experiments interface presents outcomes visually, exhibiting when an experiment performs higher or worse than its baseline. Will increase in unfavorable indicators would possibly point out greater process failure or partial code output, whereas enhancements in optimistic indicators may mirror extra full responses or higher consumer 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 programs behave in manufacturing.
As VentureBeat reported earlier this year, the corporate — initially generally known 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 consistently—in methods each hilarious and terrifying,” noting that not like conventional software program, which throws clear exceptions, “AI merchandise fail silently.” Raindrop’s authentic platform targeted on detecting these silent failures by analyzing indicators comparable to consumer suggestions, process failures, refusals, and different conversational anomalies throughout hundreds of thousands of each day occasions.
The corporate’s co-founders— Hylak, Alexis Gauba, and Zubin Singh Koticha — constructed Raindrop after encountering firsthand the problem of debugging AI programs in manufacturing.
“We began by constructing AI merchandise, not infrastructure,” Hylak instructed VentureBeat. “However fairly rapidly, we noticed that to develop something critical, we wanted tooling to know 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 software transforms observability knowledge into actionable comparisons, letting enterprises check whether or not adjustments to their fashions, prompts, or pipelines truly make their AI brokers higher—or simply totally different.
Fixing the “Evals Move, Brokers Fail” Drawback
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 exams, however you’ll be able to’t predict your consumer’s actions and your agent is working for hours, calling tons of of instruments.”
Gauba mentioned the corporate constantly heard a standard frustration from groups: “Evals cross, brokers fail.”
Experiments is supposed to shut that hole by exhibiting what truly adjustments when builders ship updates to their programs.
The software 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 approach to “evaluate something and measure how your agent’s habits truly modified in manufacturing throughout hundreds of thousands of actual interactions.”
The platform helps customers spot points comparable to process 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, software, or flag is driving it.
From there, builders can dive into detailed traces to search out the foundation trigger and ship a repair rapidly.
Every experiment offers a visible breakdown of metrics like software 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
Based on Hylak, Experiments integrates immediately with “the function flag platforms corporations know and love (like Statsig!)” and is designed to work seamlessly with present telemetry and analytics pipelines.
For corporations with out these integrations, it could possibly nonetheless evaluate efficiency over time—comparable to yesterday versus at the moment—with out further setup.
Hylak mentioned groups usually want round 2,000 customers per day to supply statistically significant outcomes.
To make sure the accuracy of comparisons, Experiments screens for pattern dimension adequacy and alerts customers if a check lacks sufficient knowledge to attract legitimate conclusions.
“We obsess over ensuring metrics like Job 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, guaranteeing transparency behind each combination quantity.
Safety and Information Safety
Raindrop operates as a cloud-hosted platform but in addition gives on-premise personally identifiable info (PII) redaction for enterprises that want further management.
Hylak mentioned the corporate is SOC 2 compliant and has launched a PII Guard function that makes use of AI to routinely take away delicate info from saved knowledge. “We take defending buyer knowledge very critically,” he emphasised.
Pricing and Plans
Experiments is a part of Raindrop’s Professional plan, which prices $350 per 30 days or $0.0007 per interplay. The Professional tier additionally contains deep analysis instruments, matter clustering, customized challenge monitoring, and semantic search capabilities.
Raindrop’s Starter plan — $65 per 30 days or $0.001 per interplay — gives core analytics together with challenge detection, consumer suggestions indicators, Slack alerts, and consumer 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 assist.
Steady Enchancment for AI Methods
With Experiments, Raindrop positions itself on the intersection of AI analytics and software program observability. Its deal with “measure reality,” as said within the product video, displays a broader push inside the business towards accountability and transparency in AI operations.
Somewhat than relying solely on offline benchmarks, Raindrop’s strategy emphasizes actual consumer knowledge and contextual understanding. The corporate hopes it will enable AI builders to maneuver quicker, determine root causes sooner, and ship better-performing fashions with confidence.
