A running explainer series on AI agent memory. Each post takes one concept, leads with a plain definition, then shows the mechanism with real config and code.
AI agents say 'I'll handle it' dozens of times a day, and most memory systems store that promise the same way they store a weather observation. Here is how to give commitments a due date, a lifecycle, and progress that only advances when the work actually gets done, so a promise survives the context reset that would otherwise erase it.
Most agent memory systems let vector similarity decide what surfaces, which cannot tell a contract deadline from a coffee preference. This is how STARE 5D scores a memory across five dimensions, walked through two real scenarios.
Most agent memory stores facts. Open Threads stores stories. A plain-language explainer of episodic arc weaving: how Meaning Memory connects related events across many sessions into coherent narrative threads, and why that models human autobiographical memory better than a pile of retrieved facts.
The context window is where every agent project starts and where most of them quietly hit a ceiling. This is a CTO-level guide to why that ceiling appears, what a memory layer actually is, and how to tell a real one from a fast cache.
Deploying AI agents is an organizational decision before it is a technical one. The decisions that decide whether a fleet works, who owns what, who can see what, who is accountable for what, and how a memory layer implements each.
AI agent memory is the layer that lets an autonomous agent keep what it learns, decide what matters, and recall the right thing later. Here is what that means, why a bigger context window is not the same thing, and how the current approaches differ.
Run more than one agent and memory becomes a boundary question. Here is how scope groups in Meaning Memory keep some memories private, share others with a team, and enforce the line in the data model, not the prompt.