IT System Analyst

Another tech company hit: Couchbase to experience layoffs in Austin post-buyout. Texas is seeing another round of tech-sector cuts, with Couchbase joining the list of companies trimming staff in the state. Couchbase, a California-based cloud database firm with offices in Austin, is laying off 11 employees in Travis County, according to a notice filed with the Texas Workforce Commission. The reductions are scheduled to take effect January 12. The company stated in its filing that the cuts are permanent. The affected employees, most of whom work on sales and corporate teams, were not represented by a union and do not have bumping rights, according to the state agency, which said it received the notice on November 13. The Austin workforce update comes as local and statewide job-loss notices continue to accumulate. New filings published by the Texas Workforce Commission show United Supermarkets reported 126 affected workers in Lubbock, and Dometic Corporation reported 54 affected workers in Grand Prairie, with both layoffs beginning in January. The agency posted the filings, which include employer-provided timelines and job counts. Couchbase's move arrives roughly two months after the finalization of the software company's $1.5 billion acquisition by Haveli Investments, an Austin private equity firm, the Austin-American-Statesman reported. The deal took Couchbase private, with shareholders receiving $24.50 per share. The job cuts in Texas follow additional layoffs in California; the company announced last week that it was eliminating about 50 positions from its San Jose headquarters, according to a report published by the Austin American-Statesman. Federal WARN law generally requires companies with at least 100 employees to give 60 days' notice before certain large-scale layoffs or facility closures. State agencies that publish WARN notices emphasize that information in these postings reflects what companies report directly. Couchbase lists its Austin office at 9050 North Capital of Texas Highway. The Couchbase website indicates that the company is a cloud database provider serving more than 30 percent of the Fortune 100, with a mission to "simplify how organizations develop, deploy, and run modern applications." Company materials highlight a global footprint that includes offices in Bangalore, London, and Singapore. The new Texas layoffs add to a growing list of workforce reductions by technology, manufacturing, and retail employers across the state in November, based on public filings. Your support ensures Dallas Express remains an alternative to legacy media - independent, fearless, and paywall-free.

Unleash real-time Agentic AI With Streaming Agents on Confluent Cloud and Couchbase. Couchbase is thrilled to be partnered with Confluent today as they announce the new features for Streaming Agents on Confluent Cloud and a new Real-Time Context Engine. Unifying data processing and agentic AI workflows, Streaming Agents enable developers to build, deploy, and orchestrate event-driven agents using fully-managed Apache Flink(R) and Apache Kafka(R) on a unified platform. Today's new capabilities take this further by helping teams build trustworthy agents faster and more easily, gain enhanced observability, and improve AI decision-making with real-time context. As a Confluent Partner, Couchbase see firsthand why the introduction of Streaming Agents was so critical. At its core, every AI problem is a data problem. When data is stale, incomplete, or inaccessible, even the most sophisticated agents and large language models (LLMs) can't deliver reliable results. This is exactly what the market demanded: a solution for building scalable multi-agent systems that are event-driven, replayable, and grounded in fresh, contextualized data. Developers needed a single platform that didn't just provide isolated tools, but one that enabled them to easily source data, reliably move from prototype to production, and gain the necessary observability to debug, evaluate, and iterate on what's actually happening inside their agents. Streaming agents power intelligent, context-aware automation. Embedded in data streams with access to the latest, most complete and accurate view of operational events, Streaming Agents effectively act as the "eyes and ears" of the business. They're designed to handle high-volume real-time data and evolving context, making them ideal for enterprise use cases where fresh information, accuracy, and observability are critical. By continuously monitoring data streams and using context from diverse sources, Streaming Agents can make intelligent, informed decisions and automate actions that drive better outcomes. Let's explore this last customer support use case in more detail. Imagine a global enterprise streaming customer chat events, CRM updates, product inventory signals, and service agent feedback into Confluent Cloud. A Streaming Agent built on Flink: * * * Consumes the live chat event * Enriches it with context (customer history, latest purchase, open tickets) * Embeds the chat via an embedding model * Performs a millisecond vector search lookup in Couchbase 8.0 to find semantically relevant past conversations, knowledge base articles, and support actions * Invokes a tool via MCP in real time (e.g., ticket update API, service scheduling tool) * Generates a response via an LLM with RAG support * Feeds the result back into Couchbase (update of conversation state) and Kafka topic for audit/analytics The result: a customer support agent that is context-aware, real-time, semantic+vector powered, automated, and fully observable. The underlying data stack on Couchbase ensures the freshest content and semantic retrieval, the Confluent streaming engine ensures event-driven flow, tool orchestration, and production-grade scale. What's new in the Q4'25 release. With Streaming Agents, every engineer can use familiar Flink APIs to build secure and trustworthy agents, with native support for Model Inference, Tool Calling with MCP, Embeddings for RAG, Built-in ML Functions, External Tables and Search, and Connections. Confluent is continuing to expand on these capabilities and deliver more streamlined developer experiences. * * * Agent definition - Quickly build agents in just a few lines of code and unlock more sophisticated tasks with better outcomes by iteratively evaluating and adapting tool calling. * Observability and debugging - Gain visibility into all agent actions, easily diagnose issues to accelerate resolution, and reliably recover from failure. * Real-time context engine - Using MCP (Model Context Protocol), serve fresh context to Streaming Agents as well as any other AI agent and application to improve decision-making and the quality of outputs. Synergy with Couchbase 8.0: A unified data platform for Agentic AI. While the streaming-agent framework provides the orchestration and logic for agentic AI, the underlying data platform is just as critical. That's where Couchbase steps in, and the timing could not be better with the recent launch of Couchbase 8.0. Here's what you should know: * * * Hyperscale vector indexing: Couchbase 8.0 introduces supporting billion-scale vector search workloads with millisecond latency and tunable recall accuracy. Independent benchmarking showed more than 19,000 queries per second at 28 ms latency (66% recall) and strong results at higher recall settings. * Unified workload support: Vector search is not an add-on - it's part of the same platform that handles key-value access, JSON documents, distributed caching, search (vector, text, GEO.), analytics, and mobile sync for offline-first apps. That means operational, AI/agentic, and analytical workloads coexist without stitching multiple data silos. * Real-time context, freshness, and trust: Agentic AI depends on timely, accurate context. If the vector retrieval layer is stale, disconnected or high-latency, the downstream agents suffer. Couchbase 8.0 strengthens the ability to provide fresh embeddings, real-time document updates, and live index refreshes, which are core to the streaming agent pattern. Streaming agents + Couchbase = Real-time Agentic AI at scale. Here's how the partnership plays out, and why Couchbase believe it provides a compelling foundation for next-gen enterprise agentic systems: * * * Real-time ingestion & streaming context: With Confluent Cloud running Kafka + Flink + Streaming Agents, operational events are captured, processed, enriched and transformed in real time. * Streaming Agents embed AI workflows: Developers use Flink APIs to embed ML functions, call tools, invoke LLMs or other models, vectorize unstructured content, join streaming context with external tables, and orchestrate workflows. * Vector search feed from Couchbase: The latest data, embeddings, document updates and context live in Couchbase. Streaming Agents can link to the Couchbase vector index to supply semantic + contextual retrieval to agents, thus powering RAG, contextual chatbots, real-time decision logic, anomaly investigation, etc. * Closed-loop, adaptive agentic systems: The streaming pipeline can feed back agent outcomes and updates into Couchbase. Over time, agents learn, adjust, and the context store updates. The unified platform supports production-scale, operational applications, not just one-off ML pipelines. Additional resources. * * * Explore the Couchbase blog on the release of Couchbase 8.0: Unified Data Platform for Hyperscale AI Applications. * Read Confluent's blog Introducing Streaming Agents on Confluent Cloud. * Learn more about Confluent's Streaming Agents and try the Streaming Agents Quickstart. * Visit the documentation for the fully-managed Couchbase connector for Confluent Cloud. Get started with your free Couchbase Capella account here.

Benchmark testing demonstrates Couchbase vector search is over 350 times faster, with 93% recall accuracy, than competitor at billion-vector scale. Couchbase Hyperscale Vector Index (HVI) Delivers Breakthrough Performance Against MongoDB in Independent Billion-Scale Benchmark SAN JOSE, Calif., Oct. 23, 2025 /PRNewswire/ - Couchbase, Inc., the developer data platform for critical applications in our AI world, today announced results from a Couchbase benchmark test using an independent tool that demonstrates Couchbase's Hyperscale Vector Index (HVI) delivers exceptional performance advantages over competitive solutions at billion-vector scale. The comprehensive testing, conducted using the industry-standard VectorDBBench methodology, shows Couchbase achieving over 700 queries per second (QPS) with sub-second latency and higher accuracy, which is 350 times faster than MongoDB Atlas under identical conditions. The rigorous head-to-head comparison tested Couchbase and MongoDB against datasets of 100 million vectors with 768 dimensions, and 1 billion vectors with 128 dimensions, measuring QPS, response latency and recall accuracy across multiple retrieval configurations. The benchmark results reveal dramatic performance differences that become even more pronounced as applications require higher recall accuracy levels. "When you're building AI applications at scale, performance isn't just a nice-to-have; it determines whether your application provides value that makes it worth adopting," said BJ Schaknowski, CEO of Couchbase. "We see this with customers every day. They're making infrastructure decisions right now that will either enable their AI initiatives or create bottlenecks they'll struggle with for years. These benchmark results show what we already know: at billion-vector scale, architectural choices have massive implications for what you can actually deliver as GenAI-powered applications. Couchbase gives enterprises the performance and accuracy they need without the traditional trade-off between the two - all at lower total cost of ownership." Key Benchmark Findings The testing demonstrates that Couchbase's architecture enables organizations to simultaneously achieve both exceptional performance and high accuracy in vector search applications. At optimized speed settings, Couchbase delivered 19,057 QPS with 28-millisecond latency at 66% recall accuracy, while MongoDB managed only 6 QPS at 57% recall with 62.6-second latency, representing a 3,176 times performance advantage for Couchbase. When configured for high-accuracy retrieval, Couchbase maintained 703 QPS with sub-second latency of 369 milliseconds at 93% recall accuracy. In contrast, MongoDB's performance dropped to just 2 QPS with over 40 seconds of latency at 89% recall - giving Couchbase a 350 times throughput advantage while also delivering higher accuracy.