{"data":{"news":{"items":[{"title":"Couchbase Launches the AI Data Plane, the Operational Data Foundation for the Agentic Enterpris","date":"2026-07-03 00:00:46","id":6358,"author":"Eric Ooi","blurb":"","blurb_image_uid":"https://dxfpracgmlczp.cloudfront.net/2026/07/1784273729337_Couchbase AI Data Plane.png","blurb_expanded_image_uid":null,"article":"\n\t\n\n\n

The Operational Data Platform for AI™, announced general availability of the AI Data Plane, a unified data infrastructure layer for enterprise AI agents. The Couchbase AI Data Plane TM gives enterprises persistent agent memory, real-time context retrieval, and consistent data access from cloud to edge and into their lakehouse architectures. By collapsing the fragmented data services that have stalled agent deployments into a single, governed layer, enterprises can move from pilots to production-grade agents that deliver more consistent decisions, richer customer experiences, and measurable efficiency gains at scale.

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The AI Data Plane unifies Agent Memory, an Agent Catalog for discoverable agent tooling, and an enterprise-supported, self-managed MCP server for standardized integration of model-context protocols. It consolidates previous Couchbase deployment models into a single architecture that runs across Couchbase Capella and self-managed environments, and is complemented by new Enterprise Analytics 2.2 capabilities for Apache Iceberg-based lakehouse federation in addition to a Trino adapter (currently expected to launch in Q3 calendar 2026). The enterprise-supported platform is backed by Couchbase’s engineering and support organization, eliminating point solutions and giving platform teams a single operational surface for the data services their agents depend on.

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\"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.\"

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Persistent Agent Memory

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CIOs shaping 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 delivers this by providing a unified persistence layer as a single service within the operational data platform instead of forcing teams to stitch together separate caching, vector, and document stores. Because it is framework-agnostic and validated with LangGraph, CrewAI, and LlamaIndex, engineering teams can switch or combine orchestration frameworks without rebuilding their memory layer.

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As enterprises move from prototypes to production agents, the gap between what agents can reason about and what they can remember across sessions has become a critical bottleneck. Simple agents can succeed with vector search, which Couchbase offers at billion scale, but production-grade agents require 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.

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“What matters most for enterprise-grade conversational AI agents is that data retrieval is fast, 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.\"

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Built for Agentic AI at the Edge

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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 must act on structured operational data and unstructured embeddings simultaneously across cloud, edge, and devices. The Couchbase AI Data Plane is architected to meet this full set of data requirements.

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Throughput is equally critical, since agentic workloads can demand orders of magnitude more from the data layer than traditional applications do. Every agent action triggers context retrieval, memory writes, and state synchronization in rapid succession, often across thousands of concurrent sessions. The AI Data Plane is engineered for this scale, leveraging Couchbase’s proven scale-out memory-first architecture, which already supports tens of millions of transactions per second with sub-millisecond latency for some of the world’s most demanding enterprises.

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The AI Data Plane builds on Couchbase’s distributed multi-model architecture, which supports JSON documents, key-value, SQL for JSON queries, full-text search, eventing, and vector search in 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 observability layers required for production agent deployments.

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Enterprise Analytics 2.2: Lakehouse Federation with Iceberg and Trino

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Couchbase also announced Enterprise Analytics 2.2, a major expansion of its 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. This lets enterprises adopting Iceberg for its open governance, performance, and ecosystem benefits derive more value from those investments by integrating Iceberg tables into the same platform that serves their agentic workloads.

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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 support, and corresponding SDK updates across Java, .NET, Python, JavaScript, and Go. These enhancements give platform teams faster, more governed analytics across their existing tools and languages, so they can build and optimize AI and data workloads without adding infrastructure complexity.

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A new Trino adapter, expected in Q3, will provide 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 to extract and replicate live data into separate analytical stores before querying when building AI and analytics workflows that span operational and lakehouse environments.

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Capella iQ Enhancements

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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. This gives teams the flexibility to choose the right model for each workload while administrators keep inference costs, compliance, and data residency tightly controlled through central policies.

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Edge, Mobile, and Distributed Application Updates

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As AI agents become part of the operational workforce, their data needs to follow the work, which increasingly happens on devices, in the field, and at the network edge rather than behind a desk or in a data center. Couchbase extends the AI Data Plane to the edge, so agents running in mobile and edge environments can access replicated data and perform local vector search, even with intermittent or no connectivity.

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New innovations include:

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Couchbase Lite (CBL) 4.1: Native, out-of-the-box peer-to-peer sync over Bluetooth with automatic switching to Wi-Fi for more reliable collaboration in disconnected edge environments. A modernized Android API with native Kotlin @Serializable support removes boilerplate data mapping and enables efficient, reactive UI updates through direct serialization and delta-based change detection, while new C++ API bindings simplify building high-performance embedded applications.
Edge Server 1.1: Client-level access control for fine-grained local permissions, CORS support for browser-based edge apps, 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: Cloud-to-edge data synchronization gateway for non-disruptive rolling upgrades and concurrent distributed resync for high-volume workloads, reducing operational overhead for enterprise deployments. Available as a managed service through App Services.

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“The database layer is where agentic AI either scales or stalls, and 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.”

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