'Observational memory' cuts AI agent costs 10x and outscores
RAG on long-context benchmarks

RAG isn't always fast enough or intelligent enough for modern agentic AI workflows. As teams move from short-lived chatbots to long-running, tool-heavy agents embedded in production systems, those limitations are becoming harder to work around.

In response, teams are experimenting with alternative memory architectures — sometimes called contextual memory or agentic memory — that prioritize persistence and stability over dynamic retrieval.

One of the more recent implementations of this approach is "observational memory," an open-source technology developed by Mastra, which was founded by the engineers who previously built and sold the Gatsby framework to Netlify.