InfoQ Homepage A/B Testing Content on InfoQ
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DoorDash Uses Multi Agent LLMs to Clean up 60,000 Feature Flags
DoorDash built a multi-agent LLM system to automate stale feature flag cleanup across more than 60,000 flags and 623 repositories. The workflow combines live experimentation data through MCP, engineer approval, isolated Git worktrees, parallel agents, and automated validation. In an evaluation of 50 flags, 45 produced usable pull requests at an average of 13.8 minutes and $4.79 per cleanup.
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Agoda Replaces 72-Shard SQL Server Price Cache with DragonflyDB
Agoda migrated its 1.5 TB hotel Price Cache from 72 SQL Server shards to DragonflyDB to handle growing read and write volumes. The migration used staged dual reads, parity validation, gradual traffic shifting, and decentralized failover detection. Agoda reports an approximately eightfold reduction in P99 read latency, with two DragonflyDB clusters providing high availability.
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How Netflix Built GenPage: a Single GenAI Model to Build Personalized Homepages
GenPage is a generative AI system developed by Netflix to replace its traditional multi-stage recommendation pipeline by directly generating personalized user homepages. GenPage leverages user history and request context as a prompt to produce the entire page, resulting in improved user engagement and reduced serving latency.
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Inside Google’s System for Coordinated A/B Testing across its Global Service Fleet
Google has shared details of its fleet wide large scale A/B experimentation system designed to standardize experiment assignment, exposure logging, and configuration propagation across distributed services. The approach enables consistent measurement across products, reduces experiment conflicts, and improves reliability of data driven decision making at scale.
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QCon London 2026: Behind Booking.com's AI Evolution: the Unpolished Story
Jabez Eliezer Manuel, senior principal engineer at Booking.com, presented “Behind Booking.com's AI Evolution: the Unpolished Story” at QCon London 2026. Manuel discussed how Booking.com has evolved over the past 20 years and the challenges they faced on their journey to incorporate AI.
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Enhancing A/B Testing at DoorDash with Multi-Armed Bandits
While experimentation is essential, traditional A/B testing can be excessively slow and expensive, according to DoorDash engineers Caixia Huang and Alex Weinstein. To address these limitations, they adopted a "multi-armed bandits" (MAB) approach to optimize their experiments.
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Beyond Win Rates: How Spotify Quantifies Learning in Product Experiments
Spotify has introduced the Experiments with Learning (EwL) metric on top of its Confidence experimentation platform to measure how many tests deliver decision-ready insights, not just how many “win.” EwL captures both the quantity and quality of learning across product teams, helping them make faster, smarter product decisions at scale. The outcome must support one action: ship, abort, or iterate.
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Target Improves Add to Cart Interactions by 11 Percent with Generative AI Recommendations
Target has deployed GRAM, a GenAI-powered accessory recommendation system for the Home category, using large language models to prioritize product attributes and capture aesthetic cohesion. The system helps shoppers find compatible accessories, integrates human-in-the-loop curation, and achieved measurable improvements in engagement and conversion.
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Amazon API Gateway Adds Dynamic Routing Based on Headers and Paths
AWS's new dynamic routing rules for Amazon API Gateway empower developers to streamline API traffic management by routing requests based on HTTP headers without complex URL structures. This innovative feature simplifies API versioning, enables fine-grained control, enhances A/B testing, and improves request visibility, making API configurations more efficient and user-friendly.
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How Spotify Carries through Experiments at Scale for Spotify Home
Spotify runs more than 250 online experiments annually on its Spotify Home platform, which are used by dozen of different teams. To accomplish running experiments at such scale, Spotify uses a number of different tools, explains Spotify product manager Nik Goyle.
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How Lyft Detects Android Memory Leaks in Production
While modern tooling for Android and iOS enable memory leak detection using local builds, this is not enough to guarantee an app shows correct memory behavior in production, where it runs on a wide range of devices in diverse conditions. For this reason, Lyft engineers combine A/B testing and memory observability to detect which features leak memory.
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Safe Mode is Lyft's Approach to Handling Feature Flags Incidents
Feature flags are a useful tool to conduct A/B experiments and to roll out changes in a controlled way. To make sure that their use does not end up disappointing users when a change causes a crash or degrades the user experience, Lyft created Safe Mode, specifically aimed to prevent crash loops on launch.
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How Lyft Is Improving Their Experiments beyond A/B Testing
Lyft’s product manager John Kirn recently published an article about the challenges they face when conducting experiments. Existing experimentation techniques did not fully adapt to Lyft’s real-time business nature or mitigate network effects. Lyft’s Experimentation team deployed new ones, such as time and region split testing, and improved internal experimentation norms and techniques.
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BBC New Serverless Platform Improves Scalability and Performance
One year into the transition to their new WebCore serverless platform, the BBC has started to reap the benefits of an architecture that removes the burden on engineers to solve performance and operational challenges and allows them to focus on the value they deliver to customers.
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Airbnb: Using Guardrails to Identify Changes with Negative Impact across Teams
Airbnb rolled out an internal Experiment Guardrails system to identify potentially negative impacts of changes across different teams. Whenever a proposed change does not pass any of the guardrails, it is escalated for further analysis by affected teams and stakeholders, explains Airbnb data scientist Tatiana Xifara.