Couchbase, Inc. today announced the general availability of the AI Data Plane, a unified data infrastructure layer purpose-built for enterprise AI agents that gives enterprises persistent agent memory, real-time context retrieval, and consistent data access from cloud to edge and into their lakehouse architectures.
The release collapses the fragmented data services that have stalled agent deployments into a single, governed layer in which operational data, context, and memory operate as a single fabric. This enables organizations to move from brittle prototypes to production-grade agents that deliver more consistent decisions, richer customer experiences, and measurable efficiency gains at enterprise scale.
The AI Data Plane integrates Agent Memory, an Agent Catalog for discoverable agent tooling, an enterprise-supported, self-managed MCP server for standardized model-context protocol integration, and an LLM cache that reduces redundant inference calls. It consolidates previously separate Couchbase deployment models into a single architecture that operates identically across both Couchbase Capella and self-managed environments, and is complemented by new Enterprise Analytics 2.2 capabilities for Apache Iceberg-based lakehouse federation along with a new Trino adapter. By bringing these components into a single enterprise-supported platform backed by Couchbase’s world-class engineering and support organization, the AI Data Plane gives platform teams one operational surface for the data services their agents depend on, replacing a sprawl of point solutions that each introduce their own latency, failure modes, and operational overhead.
“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, AI, Automation, Data & Analytics, IDC. “IDC expects that 80% of agentic AI use cases will require real-time, contextual, and widely accessible data, so the architecture has to support that. Approaches that make agent memory and context retrieval first-class capabilities of the database itself, like Couchbase’s AI Data Plane, address this directly. By unifying vectors, documents, cache, and operational data in a single distributed platform, from cloud to edge, Couchbase reduces the integration tax that has been slowing down real-world agent deployments and gives organizations a more governable, scalable foundation for the next wave of AI-powered applications.” (Final, Approved)
Agent Memory Changes Everything
CIOs evaluating their AI infrastructure strategies need a unified data platform that governs memory, context, and retrieval across the full agent lifecycle, not another point solution to integrate and maintain. Couchbase Agent Memory addresses this directly, providing a unified persistence layer as a single service within the operational data platform rather than forcing teams to stitch together separate caching, vector, and document stores. Because it is framework-agnostic and validated against LangGraph, CrewAI, and LlamaIndex, engineering teams can switch or combine orchestration frameworks without rebuilding their memory infrastructure.
The gap between what agents can reason about and what they can remember across sessions has emerged as a critical bottleneck as enterprises move from AI prototypes to production agent deployments. Simpler AI agents can succeed with vector search, which Couchbase provides at billion-scale, but production-grade agents require far more, including the ability to store conversational context, retrieve structured operational data, and maintain state across sessions and restarts, all with sub-millisecond latency at the point of decision.
“What matters most for enterprise-grade conversational AI agents is that data retrieval is very fast, very consistent, and seamless. When you’re running human-to-AI agent interactions, everything behind the scenes needs to be predictable and consistent to provide natural interaction,” said Patrick Ferriter, SVP of Product at Agora. “That’s what we’re solving together with Couchbase, and it’s why we chose them as a partner for the data layer for our conversational AI platform. Every one of our conversational AI use cases requires efficient data retrieval to feed the pipeline for AI agents, whether that’s outbound sales, customer service, physical AI, or something entirely new. 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.” (approved)
Why Today’s AI Architectures Break at the Edge
The shift from single-prompt AI applications to multi-step, autonomous agent architectures has exposed a fundamental mismatch between how agents work and how most data infrastructure is built, especially at the edge. Agents operate across sessions, accumulate context over time, and need to act on both structured operational data and unstructured embeddings simultaneously in environments that span cloud, edge, and devices. The Couchbase AI Data Plane is architected to meet the full set of data requirements imposed by production agents across these environments.
The throughput gap is equally critical, with agentic workloads demanding 10x to 100x more from the data layer than traditional applications. Every agent action triggers context retrieval, memory writes, and state synchronization in rapid succession across potentially thousands of concurrent sessions. The Couchbase AI Data Plane is engineered for that scale, leveraging a proven memory-first architecture that already supports tens of millions of transactions per second at sub-millisecond latencies for the world’s most demanding enterprises.
The AI Data Plane builds on Couchbase’s existing multi-model architecture, which already handles JSON documents, key-value, SQL++ queries, full-text search, eventing, and vector search within a single distributed system. Agent Memory extends this foundation with session persistence and context retrieval, while the MCP server and Agent Catalog provide the integration and discoverability layers required for production agent deployments.
“The database layer is where agentic AI either scales or stalls. Most of the industry is still treating agent memory as an afterthought,” said Barry Morris, Chief Product and Strategy Officer at Couchbase. “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. Agent Memory gives them a unified, framework-agnostic persistence layer that operates identically in cloud and self-managed environments from cloud to edge, and runs at the latency their agents actually need. That’s what it takes to move from pilot to production—and the vendors who understand this will define the infrastructure category for the next decade of AI.”
Enterprise Analytics 2.2 with Trino Adapter and Apache Iceberg Lakehouse Federation
Couchbase also announced today Enterprise Analytics 2.2, a major expansion of the platform’s analytics capabilities that opens operational data in Couchbase to the broader lakehouse ecosystem while strengthening the query engine itself. Enterprise Analytics 2.2 introduces Apache Iceberg lakehouse federation, allowing teams to query real-time operational analytics from Couchbase alongside existing open Iceberg-based lakehouse tables, without complex ETL or data duplication. As enterprises increasingly adopt Iceberg for its open governance, performance, and ecosystem benefits, Couchbase enables them to derive greater value from those investments by integrating Iceberg tables into the same platform that serves their agentic workloads.
The release also delivers a new Trino adapter that provides in-place SQL access to Couchbase operational data from Trino-based platforms, including AWS Athena, Amazon EMR, Google Dataproc, and Starburst. This eliminates the need for enterprises building AI and analytics workflows that span operational and lakehouse environments to extract and replicate live data into separate analytical stores before querying. Together, Iceberg federation and the Trino adapter represent a decisive step toward the open, interoperable data architecture that production AI at enterprise scale demands, providing a single governed query surface spanning operational and lakehouse data, rather than a sprawl of disconnected copies.
Core analytics enhancements include Google Cloud Storage support, JWT authentication, Oracle and SQL Server change data capture, asynchronous long-running queries, an index advisor, index-only query plans, SQL++ UPDATE statement support, and corresponding SDK updates across Java, .NET, Python, JavaScript, and Go.
Capella iQ Enhancements
Capella iQ, the platform’s natural-language query assistant, now supports multi-model provider selection with AWS Bedrock and OpenAI, governed by organization-level policies. Administrators can control which models are available to which teams, so inference costs and data residency requirements stay within organizational guardrails without slowing down individual developers.
Edge, Mobile, and Distributed Application Updates
As AI agents become part of the operational workforce, the data they depend on has to follow the work, and that work is increasingly happening on devices, in the field, and at the network edge rather than behind a desk or inside a data center. Couchbase is the platform that actually goes there, extending the AI Data Plane so that agents running in mobile and edge environments can access replicated data and perform local vector search, even when connectivity is intermittent or nonexistent.
New innovations include:
Couchbase Lite (CBL) 4.1: Provides native, OOTB peer-to-peer sync over Bluetooth with auto-switching to Wi-Fi for enhanced reliability and collaboration in disconnected edge environments. A new, modernized Android API with native Kotlin @Serializable support eliminates boilerplate data mapping and enables efficient, reactive UI updates through direct serialization and delta-based change detection, while new C++ API bindings offer a simpler alternative for building high-performance embedded applications.
Edge Server 1.1: Client-level access control enables fine-grained local permissions, CORS support for browser-based edge applications, simplified credential rotation for distributed device fleets, and expanded platform support for Windows and ARM architectures.
React Native 1.1: Enterprise-grade support with Turbo module integration gives cross-platform mobile teams direct access to Couchbase Lite performance without bridging overhead.
Sync Gateway 4.1 on App Services: Enables managed synchronization for applications requiring real-time data consistency across cloud, edge, and mobile tiers.
Azure XDCR over Private Link: Cross-datacenter replication over private connectivity on Azure, lets enterprises can replicate operational data between regions without exposing traffic to the public internet.
Availability
All products listed above are available immediately. Pricing and packaging details are available at www.couchbase.com/pricing (may need correction).
