Competitive advantage in enterprise AI comes down to context: which platform can provide the agent with the right memory, the right search, and the right information at the moment of decision.
On Tuesday, Couchbase announced AI Data Plane, which combines persistent agent storage, real-time contextual search, and an enterprise-managed MCP server into a single operating platform.
Couchbase has roots caching and high transaction databases — an architecture the company claims makes it a better fit for agent storage than vendors struggling with search or analytics. AI Data Plane works in cloud, on-premise and disconnected edge environments alike, extending agent memory and local vector search to devices without network connectivity.
"How can you be sure that the intelligence you get from these models is specialized databases?" Gopi Duddy, CTO of Couchbase, told VentureBeat. "How can you get this value from the storage systems which will still be the database?"
What AI Data Plane delivers
AI Data Plane combines three components designed to replace the fragmented stacks that most enterprises currently operate.
Agent Memory: A single persistence layer for conversational context, structured transaction data, and vector inputs. Couchbase says guard blocks are what differentiate it from standalone storage services: token limits per session, time-to-live limits on stored storage, and metering controls that limit compute consumption per agent session.
Enterprise MCP server: An enterprise-supported self-managed server for protocol integration within the context of a standardized model, shipping as part of the platform instead of requiring a separate service.
Agent directory: A functional-level directory of discoverable agent tools built by Couchbase. Duddy distinguished it from metadata catalogs such as Databricks Unity or AWS Glue—in his own words, describing it as closer to the glorified MCP, which shows that agents act as callable tools on the platform.
A memory first architecture moves the agent context to a disconnected edge
Couchbase’s pedigree and its underlying architectural foundation give it an edge in context, as Duddy says.
"Before we became a database, we were a cache," Duddy said.
Writing to memory is 10 times faster than writing to disk, Duddy said — a speed advantage that separates Couchbase from NoSQL databases that stack in-memory workloads on top of disk-based storage.
Couchbase isn’t the only data technology that has its roots in the caching layer. Redis is similarly cached and also rooted announced recently agent AI context layer. Duddi claimed that Couchbase stands out because of its ACID (Atolicity, Consistency, Isolation, and Persistence) compliant database storage, which is important for operational workloads. Couchbase also has a long history among many deployment methods.
This architecture extends to the edge through Couchbase Lite, the platform’s on-device runtime. It performs SQL, full-text search, and vector search locally without a network connection, using a proprietary synchronization mechanism to replicate bidirectionally to the cloud or between edge nodes when connectivity is restored. Target environments are retail operations, field service, industrial deployments, and regulated settings where agent data cannot leave the device.
Duddy pointed to hotel reservations as an early example: multiple agents serving customers simultaneously, each using a local context and performing a vector lookup on the device, with a shared session memory centrally synchronized. The practical benefit is token efficiency. Instead of each agent fetching and processing the same data independently, the platform stores memory in a shared context so that parallel sessions can use it without repeatedly burning tokens.
Production view of Agora
Agora, a platform that helps developers embed real-time voice, video and conversational AI into enterprise applications, is launching Couchbase in production from February 2024.
The initial use case was to manage its Signal product, channel setup and state synchronization for live calls. The expansion to conversational AI agents brought more stringent requirements: storage-first architecture, full JSON support for storage and query, cross-datacenter replication for high availability, and enterprise-grade vendor support.
"Couchbase was the best fit based on these criteria," Patrick Ferriter, vice president of product at Agora, told VentureBeat.
Agora now extends this connectivity to support contextual search for conversational AI agents.
"This will simplify the architecture and deliver the predictable low-latency enterprise-grade RAG required for conversational AI use cases," Ferriter said.
For data professionals trying to figure out the best approach in context, there are no answers. In choosing a platform, Ferriter was direct.
"It depends on the organization’s priorities and goals, including timing." Ferriter said. "If they want something enterprise-grade and optimized for immediate production and scale, they have to optimize and maintain an open source solution with community support. We wanted the former, and so we looked at an expanded partnership with Couchbase."
Competitive context: following the right trend
In 2025, the context layer has become a crowded space.
Oracle put memory core In March, it introduces a context layer in its database. Redis added a context layer as a vector-native database vendor in May Pine cone.
"Couchbase follows this trend, doesn’t set it, but it’s the right one to watch." Devin Pratt, research director of artificial intelligence, automation, data and analytics at IDC, told VentureBeat. "Its real advantage is running the same platform from the cloud to the mobile platform, which is how businesses actually operate. The test now is to compare with the bigger names."
For teams looking at the vendor landscape, Pratt’s framework is straightforward. "Adapt the tool to the workload. Aggregate where it makes sense, use a dedicated engine like graph databases where heavy relational reasoning wins, and let management handle the call rather than treat memory as plumbing." Pratt said.
