Guide
Shared team memory for AI agents: keeping it trustworthy
Shared memory for AI agents is a store of what a team knows that every agent and teammate reads from, so the next session starts where the last one stopped. It is only useful if it can be trusted, which means knowing where each fact came from, catching facts that contradict each other, and letting facts that go out of date expire.
Why per-person memory does not work for teams
Chat assistants increasingly remember things about the person using them. On a team that means each person's assistant knows a different, partial version of the same project, and a decision one person made is invisible to everyone else's agent.
Team memory puts that knowledge in one place the whole team's agents read, including the coding assistants people run in their own editors.
What makes shared memory trustworthy
- Provenance: whether a fact was confirmed by a person or inferred by a model, and which session it came from.
- Approval before storage: an agent's draft is an opinion until a person approves it.
- Contradiction detection: when two stored facts cannot both be true, the team is asked to resolve it rather than both being served as settled.
- Expiry: facts about a moving world, such as a competitor's pricing, stop being served as current after a while and wait for someone to re-check them.
Remembering directions, not only facts
Some of the most valuable things a team tells an agent are rules rather than facts: always cite the primary source, never quote prices in euros. Keeping those as team rules that every later session reads is how one person's correction benefits everyone.
How Vrittara does it
An approved session result can be kept in the workspace's memory together with the directions that shaped it. It is marked as model-inferred until a person verifies it, because approving a deliverable is not the same as vouching that every sentence will still be true in six months. A deterministic check flags pairs of beliefs that contradict each other, and beliefs about fast-changing things expire into a queue for re-verification rather than being deleted.
Directions can be kept as team rules that every later session follows, and coding assistants such as Claude Code and Cursor can search the same memory over MCP.
Related guides
Common questions
- What is shared memory for AI agents?
- A knowledge store a whole team's agents and people read from and write to, so every new session starts with what the team already knows instead of a blank prompt.
- How do you stop AI agents from remembering wrong things?
- Store only what a person approved, record whether each fact was confirmed or inferred, flag facts that contradict each other, and let facts about changing things expire until someone re-checks them.
- Can coding assistants like Claude Code or Cursor use a team's shared memory?
- Yes, when the memory is exposed over MCP. In Vrittara they can search the same workspace memory the team's agent sessions use.