BUISNESS TECH | Couchbase launches AI Data Plane for enterprise AI agents
The platform is designed to reduce the complexity of managing separate vector databases, document stores, caches, and operational databases commonly used to build enterprise AI applications.
Enterprise software company Couchbase has launched a new data platform designed to help enterprises deploy artificial intelligence (AI) agents in production by providing a unified layer for managing operational data, memory, and real-time context.
The AI Data Plane combines several AI infrastructure components into a single platform intended to simplify enterprise AI deployments across cloud, edge, self-managed, and hybrid environments.
The platform integrates persistent Agent Memory, an Agent Catalog for AI tools, a self-managed Model Context Protocol (MCP) server, and a large language model (LLM) cache that reduces redundant inference requests. It also unifies Couchbase’s cloud and self-managed deployment models under a common architecture.
According to Couchbase, the platform is designed to reduce the complexity of managing separate vector databases, document stores, caches, and operational databases commonly used to build enterprise AI applications.
“Most enterprises quickly discover that moving from chat-style pilots to production-grade agentic systems is really a data problem, not just a model problem,” said Devin Pratt, research director for AI, Automation, Data & Analytics at IDC.
Pratt said IDC expects 80% of agentic AI use cases to require real-time, contextual, and widely accessible data, making unified data architectures increasingly important for enterprise deployments.
Couchbase said its Agent Memory feature provides a persistence layer that enables AI agents to retain conversational context, retrieve operational data, and maintain state across sessions without relying on multiple storage systems. The feature has been validated with LangGraph, CrewAI, and LlamaIndex.
Patrick Ferriter, senior vice president of product at Agora, said predictable and consistent data retrieval is essential for conversational AI systems.
“What matters most for enterprise-grade conversational AI agents is that data retrieval is very fast, very consistent, and seamless,” Ferriter said. “We’ve had a multi-year relationship with Couchbase, and as we’ve scaled into agentic workloads, this was a natural extension to our partnership.”
Alongside the AI Data Plane, Couchbase also introduced Enterprise Analytics 2.2, which adds Apache Iceberg lakehouse federation and a new Trino adapter. The update enables organizations to query operational data alongside Apache Iceberg-based lakehouse tables without duplicating data through extract, transform, and load (ETL) processes.
The Trino adapter also provides SQL access to Couchbase operational data from platforms such as AWS Athena, Amazon EMR, Google Dataproc, and Starburst.
The company expanded Capella iQ, its natural language query assistant, with support for AWS Bedrock and OpenAI models. Organizations can define policies governing which AI models development teams are allowed to use.
Couchbase also announced updates for edge and mobile deployments. Couchbase Lite 4.1 introduces peer-to-peer synchronization over Bluetooth with automatic Wi-Fi switching, while Edge Server 1.1 adds client-level access controls and support for Windows and Arm-based systems. Other updates include enhancements to React Native integration, Sync Gateway, and Azure cross-datacenter replication over Private Link.
“We built the AI Data Plane because our customers told us that stitching together separate vector, caching, and document stores for every agent was the single biggest drag on their production timelines,” said Barry Morris, chief product and strategy officer at Couchbase.
Couchbase said all announced products are available immediately.
Full disclosure: All news articles published on the TechSabado website are written by human journalists, unless otherwise specified. Final text editing is partially assisted by artificial intelligence (AI).
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