Your harness, your memory
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Agent harnesses — the scaffolding that orchestrates LLMs with tools and data — are inseparable from agent memory. Using a closed or proprietary harness (like Claude Managed Agents or OpenAI's stateful APIs) means surrendering ownership of your agent's memory to a third party, creating deep platform lock-in. Memory is what makes agents sticky and personalized; losing it means losing a proprietary dataset of user interactions. The post argues that harnesses and memory must be open and model-agnostic, and introduces LangChain's Deep Agents as an open-source, model-agnostic harness with pluggable memory backends (Mongo, Postgres, Redis) and self-hostable deployment.
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Agent Harnesses are how you build agents, and they’re not going anywhereHarnesses are tied to memoryif you don't own your harness, you don't own your memoryMemory is important, and it creates lock inOpen Memory, Open Harnesses1 Comment
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