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The system surrounding an AI model — the harness that manages workflows, context, permissions, evaluation, persistent state and continuous improvement — has become just as important as the agent itself, marking a shift from prompting models to engineering reliable systems around them.
Lilian Weng's 2026 essay argues that AI engineering has moved past building autonomous agents toward engineering the harnesses (workflow, context, evaluation, state) that surround them. ✦ AI generated
Lilian Weng · Latent Space · 2026-07-14 · original ↗
Rather than focusing on the agent itself, Weng argues that the system surrounding the model has become just as important: the harness that manages workflows, context, permissions, evaluation, persistent state and continuous improvement. In other words, AI engineering has moved beyond prompting models toward engineering reliable systems around them.
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- ·Model itself is no longer the main focus
- ·Surrounding system now matters just as much
- ·Harness manages workflow, context, permissions, evaluation, state
- ·AI engineering has moved beyond prompting models
- ·Now focused on engineering reliable systems around them
- ·Continuous improvement built into the system, not the prompt
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extends → A harness is simply code you write to make an AI agent more effective at a specific job.Claire · Lenny's Newslettersupports → You should build a custom harness when a workflow needs the same setup and produces the same outcomes every time, combining deterministic steps with non-deterministic AI judgment.Claire · Lenny's Newsletter