AI agents are getting smarter. The infrastructure that lets them remember, share, and learn together is only just arriving.
Yugabyte, the distributed database company behind YugabyteDB, has launched Meko, a data infrastructure platform built specifically for multi-agent AI systems — addressing what the company identifies as one of the most consequential unsolved problems in enterprise AI deployment: how to give agents the persistent, shared memory they need to compound their learning over time.
The launch targets a structural gap that has grown more visible as enterprises move from single-agent deployments to complex, coordinated AI workflows. Individual agents today typically operate within isolated context windows, reconstructing knowledge from scratch at the start of each interaction and losing the reasoning behind previous decisions when a task is handed off. Meko is designed to replace that pattern with a unified, shared layer that persists across agents, sessions, and time.
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The Infrastructure Problem
Enterprise AI development currently requires teams to stitch together a fragmented stack of relational databases, vector stores, document stores, caches, and object storage — each optimized for a different data type, none designed to work together as a coherent system for agentic applications.
Meko replaces that stack with a single, purpose-built infrastructure layer that manages conversation histories, contextual knowledge, operational traces, and long-term memory in one place. It is built on YugabyteDB, which natively supports SQL, NoSQL, vector, time-series, and graph queries — meaning a single query can span multiple data models without requiring additional integration work.
The platform exposes what Yugabyte calls agent-native actions — functions such as “add knowledge” that directly represent the data constructs AI agents actually use, accessible through standard Model Context Protocol interfaces. The underlying complexity of how data is stored, indexed, and optimized is handled automatically.
Collective Memory and the Datapack
The central architectural concept in Meko is what Yugabyte calls collective memory — a shared foundation in which every agent’s learning compounds across the entire system rather than remaining confined to a single context window.
This is implemented through a structure called a Datapack: a portable, multi-tenant data store that persists per-agent memory while making knowledge shareable across an entire network of agents. When one agent learns something, that knowledge becomes available to others — along with the reasoning context and decision traces that produced it. When a second agent picks up where a first left off, it inherits not just the output but the understanding that shaped it.
“There is no data infrastructure today that seamlessly allows combined learning and sharing across agents and humans,” said Karthik Ranganathan, co-founder and chief executive of Yugabyte. “Meko solves this through collective memory — a shared foundation where every agent’s learning compounds across the entire system, not just within a single context window.”
Built for Agentic Economics
The platform is architected around the bursty, variable workload patterns characteristic of agentic applications — periods of high activity interspersed with idle time that make traditional enterprise database pricing models poorly suited to the use case.
Meko’s serverless, multi-tenant design keeps costs low when agents are inactive and scales automatically when they are not. It also handles context tiering automatically, moving older data from high-performance solid-state storage to object stores such as Amazon S3 and warming it back on demand — providing enterprise-grade storage capability without the corresponding infrastructure overhead.
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Auditability as Architecture
As regulators move toward mandatory documentation requirements for high-risk AI systems — including under the EU AI Act — Meko’s approach to traceability is designed to make compliance a structural feature rather than a retrospective exercise.
All memory reads and writes route through a single endpoint backed by a unified database, generating a complete and traceable audit trail of what agents learn, how they share knowledge, and what data operations underpin every interaction. The audit trail is not a bolt-on compliance layer. It is a byproduct of the system’s design.
