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Redis Labs blog
Primary focus: Technology
Today, we are announcing an organizational change at Redis, including a reduction of approximately 200 roles globally and a realignment of roles, teams, and priorities across the company. This is a difficult decision because it affects people who hav...
Your AI agent issued the refund. It read the customer's tier, checked the return window, confirmed the policy, and processed it in seconds. The problem: the return window had closed four minutes earlier when a batch job updated the order status, and t...
You built a research AI agent in the LangGraph framework. Another team shipped a customer-service agent in CrewAI. A third team wired up tools through the OpenAI Agents SDK. Now leadership asks: can these things work together? For most teams, the hone...
Type songs for a rainy Sunday morning into a music app and you'll get results that match the mood, even though none of those words appear in the track titles. That kind of result is often powered by vector embeddings, numerical representations of me...
Most teams pick a chunk size once and never touch it again. Copy the settings a tutorial used (usually 512-token chunks with 50 tokens of overlap between them), and ship it. Then the corpus grows, the queries get stranger, and that early choice quietl...
AI agents don't have one memory requirement. They have three: recalling what happened seconds ago, retrieving what they learned weeks ago, and tracking what they're doing right now. Most databases handle one of those patterns well, which is why so man...
You ask a chatbot to summarize a long email thread, and it hands back a tidy paragraph. Useful, but that's a single-step system—one prompt, one response, done. A multi-step AI agent works differently. Ask it the same question, and it can check your or...
Running a large language model isn't cheap. Every response burns GPU time, memory, and money, and those costs grow as your app grows. Quantization is one of the most common tricks for making models cheaper and faster to run, which is why you'll see it...
It's 3 AM and your agent is confidently booking the wrong flight, calling a deprecated API, and burning through tokens on a retry loop no one wrote. It passed every staging test last week. That gap between works in staging and survives production ...
Tail latency is the handful of requests that take far longer than the rest, hiding inside an average that looks perfectly fine. Your dashboard shows a 50ms average, everyone seems happy, and then support tickets start piling up about the app feeling s...
