Yugabyte targets the missing memory and knowledge layer for enterprise AI agents
Enterprise investment in agentic artificial intelligence is accelerating, but the infrastructure supporting those systems is still catching up. Organizations are moving agents into customer support, software development, sales operations and other production workflows. Yet many of those agents remain stateless, unable to retain durable context, share knowledge with other agents or explain how previous decisions [...
Key Takeaways
- Agentic artificial intelligence needs shared memory and traceability.
Yugabyte's Meko provides a data layer for multi-agent systems.
- "The model problems are getting solved really well, and the orchestration problems are getting really good.
It's now the iteration cycles between your data and data infrastructure.
- Agents typically exchange outputs, but not the reasoning, assumptions, context or prior knowledge that produced those outputs.
In a human team, that would be equivalent to sharing only final conclusions without explaining how anyone arrived at them.
- Instead of asking how to make a single agent smarter, enterprises must determine how agents can share truth across systems.
Moving from AI-enabled databases to agent-native infrastructure Most database vendors are extending existing platforms to support vector search, embeddings and other AI-related access patterns.
- Without a structured layer, agents may map conversations, memories, queries and knowledge into underlying systems differently each time.
Stats & Key Facts
- #UPDATED 16:10 EDT / JULY 27 2026 AI Yugabyte targets the missing memory and knowledge layer for enterprise AI agents by Paul Nashawaty Enterprise investment in agentic artificial intelligence is accelerating, but the infrastructure supporting those systems is still catching up.
Agentic artificial intelligence needs shared memory and traceability. Yugabyte's Meko provides a data layer for multi-agent systems. UPDATED 16:10 EDT / JULY 27 2026 AI Yugabyte targets the missing memory and knowledge layer for enterprise AI agents by Paul Nashawaty Enterprise investment in agentic artificial intelligence is accelerating, but the infrastructure supporting those systems is still catching up.
Organizations are moving agents into customer support, software development, sales operations and other production workflows. Yet many of those agents remain stateless, unable to retain durable context, share knowledge with other agents or explain how previous decisions shaped current outputs. That gap is becoming less of a model problem and more of a data architecture problem.
In the latest episode of theCUBE Research's AppDevANGLE podcast, I spoke with Karthik Ranganathan, co-founder and chief executive officer of Yugabyte, about the company's launch of Meko, a new data infrastructure platform designed to provide persistent memory, shared knowledge and traceability for multi-agent systems. "Your agentic systems are only as good as the state and the data that you feed them," Ranganathan said. "The model problems are getting solved really well, and the orchestration problems are getting really good.
For more details please read the original article at SiliconANGLE AI.
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