Why does my agent lose context between sessions?
Part of Token efficiency is a context problem
Your agent loses context between sessions because the conversation is its only memory, and that memory ends when the session does. The next session starts blank and re-reads the whole project to catch up, which is both slow and expensive. The fix is to keep context in a shared layer that outlives any single session.
What actually gets lost?
Two things disappear at the end of a session: the work product (decisions made, files touched, what was tried) and the orientation (which file holds what, how the project is shaped). The next session has to rebuild both from scratch, usually by re-reading everything.
Why do memory frameworks fall short?
Most "agent memory" tools bolt a vector store onto the conversation and call recalled snippets "memory". This helps a little but stays private to one agent and one runtime, and it stores recall, not structured state. When a teammate or a second agent picks up the work, none of it is shared.
How does a shared data layer keep context?
A shared data layer holds the files, datasets, and shared state in one place that any session, any agent, and any teammate reads from. A new session resumes from that shared state instead of re-reading the project, and work done by one agent is immediately visible to the next.
With adlass, the Space is the persistent context: files, documents, and state live there over MCP, so context survives across sessions, agents, and people rather than dying with the chat window.
Frequently asked questions
- Isn't a bigger context window enough to keep context?
- No. A bigger window only helps within one session and degrades in the middle. Context that must survive across sessions and agents has to live outside the conversation.
- How is this different from agent memory frameworks?
- Memory frameworks store recall private to one agent. A shared data layer stores the actual files and state, shared across every agent and teammate, so handoffs do not lose anything.
Work with your agents on the same data
adlass is the shared data layer where you, your team, and their agents work over the same documents and datasets.