88% of Agent Projects Die Before Production — and the Model Is Rarely the Reason

A cluster of practitioners converged this week on the same diagnosis: agents fail in production because of architecture, error handling, and observability — not weak models.

A striking consensus formed across the builder community this week, and it cuts against the dominant narrative that better agents require better models. @AtlanHQ put a number on it: 88% of AI agent projects never reach production, attributing the failures to 13 recurring anti-patterns concentrated in 'the harness layer and data failures.' The model, in this framing, is the part that already works.

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