Learn How to Build a Shared Context Layer for AI Agents and Humans
Your coding agent remembers your projects, but other agents working on them might not have access to those memories. Each agent works in a silo and delivers work based on context that doesn’t align, resulting in collaborative drift. Humans fall out of sync in the same way.
Meko is built to support shared memory, knowledge, and decision tracing, enabling agents to learn together.
In this webinar, we’ll build a shared context layer using two agents: A research agent records decisions and the reasons behind them. A second agent, in a fresh process with no prior context, retrieves them.
Why Watch?
Join Heather Downing, Developer Advocate at Yugabyte, who will explore how to share context across agents and tools using Meko. Decisions made in your terminal are retrievable by the agent reviewing the pull request, and your team controls the resulting shared knowledge.
Sign up for a free Meko account to follow along. No prior experience is required.
What You'll Learn
- When to use agent-scoped memory vs a shared knowledge base
- How to make your code, not the model, decide what gets persisted
- How to share isolated agent learnings selectively for team-wide knowledge
- How to trace every memory read and write
- How to permit your coding agents to refer to discovery sessions you had elsewhere
Watch the session to explore shared memory for AI coding agents.
You’ll leave with a runnable repo and the option to try it on your own project!
