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Harnesses, not agents
What "agentic AI" actually ships in the wild.
Most of the production agent work I do isn't really about the agent. It's about the harness — the boring stuff around the model that makes a thousand customer-shaped failures recoverable, observable, and cheap.
A model that's 95% correct in a notebook becomes 0% deployable without a harness that can: time out a runaway tool call, retry an idempotent step, fan out work, surface intermediate state to a human, and roll back side effects when the plan changes mid-flight.
The interesting research question of 2026 isn't "can the model do it." It's "what's the smallest, most legible scaffolding under which the model can do it the same way twice."
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