Own your agent harness: why out-of-the-box frameworks cap what your agents can do
omarsar0 · x · 2026-11-09
The author argues the repeated mistake in agent building is over-relying on someone else's harness. Standard out-of-the-box harnesses constrain LLMs as usage deepens, and real gains require deep customization — which is why more full-stack AI companies build harnesses in-house.
- Proprietary harness lock-in is worst; open source is better but still borrowed
- Hot take: fully own the harness — build, understand, and maintain it yourself
- Harnesses can now stay minimal since LLMs handle tool calling, progressive disclosure, and persistence well
- Freed from others' harnesses, the author customizes memory systems, proactive patterns, and context strategies for agent teams
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