Your AI Agents Are Stuck in Pilot. It’s a Data Problem, Not a Model Problem.

The hard part of building production AI agents was never the model. It’s the data layer underneath. Today Couchbase is changing that. 

We’re excited to announce that the AI Data Plane is now generally available for self-managed Couchbase Enterprise deployments, extending beyond Capella for the first time. The Couchbase AI Data Plane is a high velocity unified data infrastructure layer for enterprise AI agents, giving them 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 resolve a customer’s issue without making them repeat themselves, reach live business data through one governed interface instead of a new integration per agent, and stop paying to resend the same context on every turn. Here’s why agents stall, and how a unified data layer gets them to production.

Why you need a unified data infrastructure layer

Every enterprise has built the same demo by now: a chatbot that answers questions, an assistant that drafts emails, an agent that looks genuinely capable in a controlled setting. The demo works. But then it goes to production, and it falls apart.

The agent forgets what a customer said five minutes ago. It can’t reach the operational data it needs without a custom integration written from scratch. Nobody can explain why it made a particular decision or which prompt version produced a bad answer. Costs climb as the same context gets resent on every turn. The impressive pilot becomes a project that never ships.

Models are rentable, interchangeable, and improving on their own. The real challenge is the data layer underneath: persistent memory, governed access to live enterprise data, traceability, prompt and tool management, and low-latency interaction with the systems that actually run the business. That foundation is what the Couchbase AI Data Plane provides.

Why the usual approaches fall short

Teams trying to get agents to production usually reach for a stack of specialized point products: a vector database for semantic search, key-value store for fast data access, relational database for complex SQL, a separate store for memory, an in-memory cache for low-latency reads, a feature store, a graph database for relationships between entities, and a search engine, all stitched together with custom integration code. Every common approach has a structural weakness:

• Bolted-on memory stores sit apart from your operational data, so agents reason over stale or disconnected context and you have to maintain yet another system.
• Ungoverned data access means every new agent needs custom development to reach enterprise data, with no standard interface, no access control, and no audit trail.
• A fragmented point-product stack spreads memory, caching, vector search, tools, prompts, and traces across multiple vendors, multiplying cost, latency, and the integration tax on your best engineers.
• A stitched-together stack caps your scale. Each point product scales on its own model and its own limits, so the system is only as scalable as its weakest link. As agent traffic grows, the seams between them become bottlenecks, and scaling up means scaling, tuning, and paying for every piece separately.

Each seam is a place where context breaks, latency compounds, and trust between operational data and AI insight is lost.

Couchbase AI Data Plane: one governed layer for production agents

The AI Data Plane unifies fragmented services into a single governed layer where operational data, context, and memory work together as one fabric across cloud, edge, and lakehouse architectures. It brings three essential components together on Couchbase’s JSON-native, scale-out, memory-first architecture:

• Agent Memory. Persistent, reusable memory across sessions, restarts, users, and frameworks, in close proximity to operational data. Agents retain business context instead of starting from scratch every time.
• MCP Server. Enterprise-supported, standardized agent access to Couchbase operational data, vectors, documents, and cache, so agents connect to your systems through a governed interface rather than bespoke integration code.
• Agent Catalog. Manages tools, prompts, metadata, and end-to-end traces so teams can inspect, reuse, and govern agent behavior, and see exactly which tool an agent used and which prompt version shaped a response.

The principle is simple: the model is rentable, your data is strategic, and the AI Data Plane puts it to work. Your data and vectors run natively on one operational foundation.

What’s in this release

This release widens what agents can reach across your data, gives developers faster ways to build and operate on Couchbase, and extends the platform to the edge and mobile.

Reaching more of your data: An agent is only as good as the data it can reach. These features let agents reason over more of your data, in place, without waiting on ETL or copying it first. The more of your operational and analytical data an agent can see in real time, the more accurate and grounded its decisions, instead of reasoning over a stale or partial copy.

• Apache Iceberg federation. Query Iceberg lakehouse tables in place from Enterprise Analytics 2.2, without the operational complexity of ETL or data duplication.
• Trino adapter (coming in Q3). Run federated SQL queries from Trino-based engines, including AWS Athena, Amazon EMR, Google Dataproc, and Starburst, directly against live Couchbase data, with no extract or replication step.
• iQ multi-model natural language query. Choose AWS Bedrock or OpenAI for Capella iQ, governed by org-level provider policies.

Building and operating: More agents mean more load and more upkeep. These features give developers a faster runtime and platform automation instead of manual setup. As agent fleets grow, the platform underneath has to scale and stay manageable without a linear increase in engineering toil, or the operational cost of agents outruns their value.

• Rust SDK. A new server SDK for building high-performance applications on Couchbase, with async-native access to key-value, query, Full-Text Search, and vector indexing without the overhead of a garbage collector.
• Expanded infrastructure-as-code support. Generate custom usage reports through the billing management API, with programmatic configuration of App Services and Eventing functions coming in Q3, so teams can manage Couchbase through automation rather than manual setup.
• Azure XDCR over Private Link. Cross-data center replication over private connectivity, off the public internet.

Edge and mobile: Agents are only as useful as the data they can reach, and that data increasingly lives on devices and in disconnected environments. These updates keep that data synced and accessible at the edge, so it’s ready when agentic workloads extend there.

• Bluetooth peer-to-peer sync. Couchbase Lite 4.1 syncs data over Bluetooth in fully disconnected, zero-network edge environments.
• React Native 1.1. Enterprise Edition React Native support for high-performance cross-platform apps, on Couchbase Lite 4.1.
• Sync Gateway. Rolling upgrades, faster large-dataset resync, and system metadata isolation.
• Edge Server 1.1. Client-level access control, CORS, credential rotation, and Windows/ARM support.

One platform, deployed your way

The AI Data Plane is how AI runs on Couchbase data, spanning both deployment models. Couchbase Capella delivers it as a managed service, while Couchbase Enterprise delivers it as a self-managed solution. In either model, every agent gets the same memory, governed data access, context, and tooling on one foundation.

Agent Memory, MCP Server, and Agent Catalog are now available as self-managed services on Couchbase Enterprise, extending the AI Data Plane beyond Capella and bringing these capabilities to self-managed deployments for the first time. The AI Data Plane builds on Couchbase’s multi-model architecture: JSON documents, key-value, SQL++, full-text search, eventing, and vector search in a single distributed system.

Why Couchbase

Couchbase gives enterprises a single operational data foundation for production AI agents, enabling agents to remember, access data, use governed tools, explain decisions, and scale, without having to stitch together a fragile stack of point products. Several long-standing architectural choices are uniquely suited for agent workloads:

• One data layer, not a stack. Agent Memory, MCP Server, Agent Catalog, and core Couchbase capabilities come together as a unified plane, with no additional data layers to manage.
• Handles AI data variety. A native JSON document model fits varied, fast-changing data: profiles, sessions, memory records, cache entries, metadata, tools, traces, and agent state.
• Fast enough for agentic workloads. A memory-first architecture delivers low-latency reads, writes, searches, and lookups, extending performance to repeated prompts.
• Scales with agent growth. Scale-out architecture absorbs unpredictable demand as teams build more agents, without brittle scale-up patterns.
• Always-on by design. Global HA, powered by a K-safety model and active-active cross data center replication (XDCR) to keep the data layer available and writable across regions, enables agents to support your customers, employees, operations, and revenue through node, datacenter, or region failure.
• Consolidation that improves operations. One foundation for operational data, memory, caching, vector search, tools, prompts, and traces translates to lower TCO and fewer systems to run, while removing the cross-product seams that slow agents down with latency and stale context.

Why it matters now

The AI conversation has been dominated by models. But as models converge, the real bottleneck is shifting underneath them to the data layer: memory, governed retrieval, and the ability to act on live enterprise data with agentic speed and scale. The enterprises that win the agent era will be those whose data infrastructure can keep up, not those with marginally better models.

Couchbase has spent more than a decade building a fast, scalable, memory-first data platform as a core strategic strength, not a bolt-on afterthought. The AI Data Plane brings that foundation to agent workloads, powering demanding production environments today. The engineering time typically spent stitching together and maintaining a fragile AI stack can now go toward improving agents and creating competitive advantage.

Learn more

Visit the AI Data Plane page. 

For more details on today’s release, visit the Couchbase Product Release Announcement page