The competitive edge in business AI is shifting to context: which platform can provide the agent with the right memory, the right retrieval and the right data at decision time.
Couchbase on Tuesday announced its AI Data Plane, which combines persistent agent memory, real-time content retrieval and an enterprise-managed MCP server in a single operating environment.
Couchbase’s roots are in caching and high-performance databases – a structure that the opposing company makes better suited to the agent’s memory than vendors that have come to a problem from search or analysis. AI Data Plane works equally across clouds, on premises and at the disconnected edge, extending agent memory and location vector search to devices without a network connection.
"How do you make sure that the intelligence you get from these models is what the databases focus on?" Gopi Duddi, CTO at Couchbase, told VentureBeat. "How can you get that value in the final systems, which will still be the database?"
Delivered by the AI Data Plane
The AI Data Plane consists of three components designed to accommodate the different stacks that most businesses currently operate.
Agent memory: An integrated persistence layer for conversation context, structured performance data and vector embedding. Couchbase says the guardrails are what separate them from private memory services: token limits per session, lifetime limits on cached memories and compute controls that include per-agent usage.
Enterprise MCP Server: A self-managed server supported by the integration protocol of the standard content model, deployment as part of the platform rather than requiring a separate service.
Agent catalog: A functional level catalog of available agent tools created by Couchbase. Duddi differentiated it from metadata catalogs such as Databricks Unity or AWS Glue – describing it, in his words, as close to a glorified MCP that works with agents as pluggable tools within the platform.
Memory-first architecture takes the agent core to the disconnected edge
Couchbase’s pedigree and its core architecture is what Duddi says gives it an edge when it comes to context.
"We were a repository before we were a database," Duddi said.
Writing to memory is 10 times faster than writing to disk, Duddi said — a speed advantage that he says separates Couchbase from NoSQL databases that put in-memory operations over disk-based storage.
Couchbase isn’t the only database technology that has its roots in the caching layer. Redis is similarly focused on cache and recently announced an AI core layer for agents. Duddi argued that Couchbase is unique because it maintains an ACID (Atomicity, Consistency, Isolation, and Durability) compliant database that is critical for transactional workloads. Couchbase also has a long history in multiple distribution methods.
That creativity goes to the edge with Couchbase Lite, the platform’s on-device runtime. It uses SQL, full-text search and vector search locally without a network connection, using a proprietary synchronization mechanism to replicate back to the cloud or between edge nodes when connectivity returns. Target scenarios are sales floor operations, field service, industrial deployment and managed settings where agent data cannot leave the device.
Duddi cited hotel reservations as an example of the former: multiple agents serving customers simultaneously, each pulling local context and using vector search on the device, with shared session memory synchronized centrally. The practical benefit is the efficiency of the tokens. Rather than every agent receiving and processing the same data independently, the platform maintains a cache of shared content so that parallel sessions can draw on it without repeatedly burning tokens.
A view of the Agora from production
Agora, a platform that helps developers embed real-time voice, video and conversational AI into business applications, has used Couchbase in production since February 2024.
The first use case was its Signaling product, managing channel setup and status synchronization on live calls. Extending to conversational AI agents brings strong requirements: memory-first architecture, full JSON support for storage and query, cross-datacenter replication with high availability and enterprise-level vendor support.
"Couchbase was the perfect fit based on these criteria," Patrick Ferriter, SVP of Product at Agora, told VentureBeat.
Agora now extends that relationship to support contextual retrieval of AI chat agents.
"This will simplify design and deliver enterprise-grade RAG with predictable low latency required for conversational AI use cases," Ferriter said.
For data professionals trying to find the best approach to content, there is no one answer. In the choice of platform, Ferriter was specific.
"Depending on the preferences and goals of the organization, including time," Ferriter said. "If they want something enterprise-class and suitable for rapid production and scale compared to developing and maintaining an open source solution with community support. We wanted the first and that is why we are looking for an extended relationship with Couchbase."
The essence of competition: following the right trend
The core layer has become a dense space by 2025.
Oracle put memory into its database back in March providing a core layer. Redis added a context layer in May as did vector-native database vendor Pinecone.
"Couchbase follows this trend, not to stop it, but to be the one to follow it," Devin Pratt, Research Director of AI, Automation, Data and Analytics at IDC, told VentureBeat. "Its real edge is reach, using the same platform from cloud to edge to mobile, which is how businesses actually work. The test now is to measure up against the big names."
For teams navigating the vendor landscape, Pratt’s framework is straightforward. "Match the tool to the task. Compile where it makes sense, use a specialized engine such as a graph database where the heavy logic finds it, and let governance make calls instead of managing memory as pipes," Pratt said.
