When a Jev-powered check gets something wrong, and a person corrects it, where does that correction go, and how can you ensure the correction is implemented in future results? This blog explains why a decision model correction should live outside the model, what a system needs to ensure corrections carry into the next request, and the code review check that prevents the system from raising a finding after a reviewer has overruled it.
If your agents hand work to each other, or to a teammate’s agents, this is the difference between the next agent building on a decision and rebuilding a dead end. Discover how to solve the agent handoff problem with Meko, the agent-native context engine for multi-agent AI systems.
After a decade of building a reliable distributed data architecture, the natural progression for Yugabyte was to begin supporting AI agents. Yugabyte is now a Silver Member of the Agentic AI Foundation (AAIF), the Linux Foundation’s home for the open standards that agentic AI runs on. This blog provides background on the AAIF’s work and shares why we joined the Foundation.
Interest in Meko has been tremendous, with user questions coming in thick and fast via Discord, LinkedIn, and at in-person events. In his recent AMA session, Yugabyte co-founder Karthik Ranganathan answered as many as he could in 30 minutes! Discover answers to popular user questions, including why Meko was built, how it works, what it can do, and what’s next.
Discover why standard MCP wiring makes context persistence optional (the model calls the tool only when it decides to) and what to do instead. We walk through how to treat persistence as control flow rather than reasoning, using your own code as the MCP client, so the write always happens. Finally, we prove this with a working Strands agent that logs every step to Meko, and audit the trace from a separate process to confirm the context survived.
Developers and engineering teams building production multi-agent AI applications need their agent systems to learn and improve collectively, not just store and retrieve data. This blog explores Meko’s agent memory features and showcases a short video by Yugabyte Developer Advocate Heather Downing that demonstrates exactly how Meko gives AI agents persistent, shareable memory.
Meko is an agent-native data layer that lives independently of any single AI vendor. It provides agents with a memory that persists across sessions, tools, and time, plus a searchable knowledge base and a full record of how they reached their answers. Discover how to create a free account today and start giving your agents persistent, shareable memory.
YugabyteDB 2026.1 introduces a PostgreSQL database for every agent. This blog covers how to make that vision a reality. Discover how to get started on YugabyteDB AMP (Agentic Multitenant PostgreSQL) and when workloads outgrow serverless, transition to YugabyteDB Dedicated with no migration, no rewrite, and no change in how agents connect.
Newly released YugabyteDB 2026.1 and YugabyteDB AMP (Agentic Multitenant Postgres) provide true serverless, scale-to-zero PostgreSQL, where every agent gets its own real, isolated database starting at a fraction of the cost of a core. Discover how YugabyteDB AMP packs hundreds of bursty, idle-prone agent workloads onto shared distributed infrastructure, how Resource Governance keeps costs predictable across a fleet, and how the same platform scales from prototype to global active-active deployment with no rewrite. We also share what else is new in 2026.1!
The Distributed SQL Summit showcases how you can use YugabyteDB to power GenAI applications, explores real-world use cases, and provides practical demonstrations of best practices to help you build ultra-resilient, globally distributed systems at scale. Discover the experience of a first-time Distributed SQL Summit attendee and why you should sign up for future events!
