{"id":17748,"date":"2025-12-10T05:30:35","date_gmt":"2025-12-10T13:30:35","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=17748"},"modified":"2025-12-09T10:11:56","modified_gmt":"2025-12-09T18:11:56","slug":"capella-ai-services-build-enterprise-grade-agents","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/pt\/capella-ai-services-build-enterprise-grade-agents\/","title":{"rendered":"Capella AI Services: Build Enterprise-Grade Agents"},"content":{"rendered":"

Production-Ready AI Agents<\/span><\/h2>\n

Building agentic AI applications that can make real business decisions is a complex undertaking. Developers often find themselves juggling multiple disparate tools to manage different data types, ensure data privacy, and maintain control over a rapidly evolving AI stack. This fragmentation creates significant operational and financial risk and architectural complexity, keeping powerful AI applications stuck in the prototype phase.<\/span><\/p>\n

Today, we are excited to announce the availability of Capella AI Services, a suite of capabilities that extend our database platform and are designed to solve these challenges. Capella with AI Services creates a single unified AI database platform enabling enterprises to build, deploy, and govern agentic AI applications with the security, performance, and reliability required for production environments. By integrating data management, model integration, agentic operations and governance within our core database platform, Couchbase streamlines the entire AI application data life cycle.<\/span><\/p>\n

This post will explore how Capella AI Services empower you to move beyond experimentation and deploy intelligent, trustworthy agents at scale.<\/span><\/p>\n

From Experiment to Enterprise: A Unified Database Platform for AI<\/span><\/h2>\n

Developers struggle to move agentic applications into production for several reasons. Privacy concerns with third-party LLMs, hallucinations from general-purpose models lacking enterprise context, the mysterious costs of running models in production, and the sheer complexity of managing AI tools are compounding challenges. Also, integrating diverse knowledge stores, data types and unstructured data across multiple systems is a major hurdle in offering usable context information to models.<\/span><\/p>\n

Couchbase addresses these issues with a unified platform that handles operational, vector, caching, analytic, and agent governance data all at once, and at scale. This approach eliminates the need for fragmented, frail, and expensive architectures with more tools than you can count using both hands. The benefits include reduced latency, improved system trustworthiness, a streamlined Retrieval-Augmented Generation (RAG) data life cycle, improved agent governance, and better cost transparency.\u00a0<\/span><\/p>\n

Everything Developers Need in One Place<\/span><\/h2>\n

Capella AI Services provide a complete toolkit to simplify and accelerate the development of agentic applications. Let\u2019s look at the core components that make this possible.<\/span><\/p>\n

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Servi\u00e7o de modelo<\/span><\/h2>\n

Security and performance are paramount when deploying AI models. Our Model Service allows for the secure deployment of embedding and large language models. Through our partnership with NVIDIA AI Enterprise, you can leverage GPU-accelerated, low-latency inference that keeps models co-located with your data. This architecture minimizes security exposure and maximizes performance. Furthermore, our built-in semantic caching and async processing options improve the performance and efficiency of the LLM. Deployments are also streamlined with a few clicks. Additionally, developers can create “guardrails” around AI interactions, verifying outputs against enterprise data and business rules before any subsequent action is executed, reducing risks of unexpected behaviors.<\/span><\/p>\n

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Data Processing Service<\/span><\/h2>\n

Preparing contextual enterprise data for AI interactions is often complex. The Data Processing Service provides native support for structured, semi-structured, and unstructured data. It includes built-in RAG pipeline capabilities for preprocessing, chunking, and vectorization, removing a significant layer of complexity from your development workflow. Teams no longer need to write and maintain custom code for data ingestion. Now it can be automated in minutes.\u00a0<\/span><\/p>\n

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Contextually accurate AI responses depend on effective vector search. Our Vectorization Service automates the creation of vectors and their indexes directly from your operational data, ensuring your AI agents have fast access to the right information. As data changes, vectors and indexes are automatically updated. We offer three styles of vector indexing, to support a variety of RAG and agentic use cases.\u00a0<\/span><\/p>\n