How to give AI agents a real, shared memory.

Every AI agent has the same handicap: it is brilliant for one session and blank the next. Teams solve this badly by default, and the failure modes are predictable. Here is what durable agent memory actually needs.

The default failure modes

• The vendor silo. The agent's memory lives inside one AI product. Nobody on the team can open it, audit it, or correct it, and switching vendors means amnesia.
• The context dump. Someone pastes the same background into every session. It drifts, it forks, and the agent's "knowledge" is whatever version someone last pasted.
• The embeddings black box. Everything goes into a vector store nobody can read. Retrieval is probabilistic, correction is impossible, and when the agent asserts something wrong, nobody can find where it learned it.

What durable agent memory needs

• Human-legible storage. If a person cannot open the memory and read it, nobody can audit or fix it. Plain markdown beats every opaque format here.
• Shared, under permissions. The team and the agent should read and write THE SAME memory, with the agent holding explicit grants: these folders, this access, nothing else. A leaked credential should be one revocation from dead.
• Attribution. Every write carries its author. When the agent files something wrong, you can see it was the agent, see when, and revert it without archaeology.
• An open protocol. The memory should speak a standard (MCP is the emerging one) so ANY agent, from any vendor, in any harness, can connect. Memory that only one AI can use is a silo with better marketing.
• Live merge. An agent writing into a note a human has open must merge, visibly, in real time, not clobber or queue. Watching the words arrive, attributed, is also what builds the team's trust in the agent.

Memava gives any MCP-speaking agent exactly this: twenty-three note tools, per-agent grants, attributed live writes. The developer page has the endpoint shape.