AI BriefingYour Harness, Your Memory
Key point
The agent harness is the core of memory management, so securing memory sovereignty requires using an open harness.
Details
The way agents are built is rapidly evolving from simple RAG chains through LangGraph to Agent Harness. Claude Code, Deep Agents, and Letta Code are representative examples.
Even as models advance, the model will not absorb all the scaffolding. Since an agent is fundamentally a system in which an LLM interacts with tools and data, a harness to orchestrate this must necessarily exist.
The harness is a core element inseparable from memory. This is because memory is not a simple plugin, but a core function of the harness that manages context. Both short-term memory (conversation content, tool call results) and long-term memory (memory across sessions) are managed through the harness.
Therefore, using a closed API-based harness results in handing over control of the agent's memory to a third party, which creates strong lock-in. To sustain the agent's experience and retain initiative, both the harness and the memory must be open.
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