Introducing the Akashik Protocol: Shared Memory for AI Agents
The article discusses the need for a shared memory protocol to enable coordination and context sharing among AI agents, leading the author to develop the Akashik Protocol.
Why it matters
The Akashik Protocol addresses a critical gap in enabling effective coordination and context sharing among AI agents, which is essential for building robust and reliable multi-agent systems.
Key Points
- 1AI agents can call tools and communicate, but lack a shared memory layer to maintain context and resolve contradictions
- 2The Akashik Protocol provides a third layer in the agent stack, complementing existing protocols for tool access (MCP) and agent-to-agent messaging (A2A)
- 3Key features include mandatory intent declaration, attunement-based context retrieval, and first-class handling of conflicts between agents
Details
The article describes the author's experience running an experiment with five AI agents working on a research task, where the agents produced contradictory results due to the lack of a shared memory layer. The author then explored existing protocols like MCP and A2A, but found they did not address the gap in managing shared context and resolving conflicts. This led the author to develop the Akashik Protocol, which introduces a third layer in the agent stack focused on shared memory and coordination. The protocol has three core ideas: 1) mandatory intent declaration when writing to shared memory, 2) attunement-based context retrieval rather than querying a database, and 3) first-class handling of conflicts between agents. The Akashik Protocol aims to provide a standardized solution for the missing memory layer in multi-agent systems.
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