Agent-Shaped Work: When to Use AI Agents (and When Not To)

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• Core argument: agents work fine technically — the real bottleneck is dispatch strategy, i.e. deciding when a task warrants an agent at all. Evidence cited: 1.6 million registered OpenClaw agents that did nothing, and an emerging market for uninstalling unused free agent tools.
• Named framework: the One-Minute Test — routes any task into one of four verdicts (chat interface, single agent, agent team, or “don’t bother”), scored against four dimensions: size, independence, separation, checkability.
• The 40-tool audit case study: an extensive tool-verification process “paid for itself”; a two-dozen-agent team rebuilt a website for about $8 in one afternoon, passing an accessibility expert review.
• Two named constraints on multi-agent systems: the “verification wedge” and the “context ceiling.” Supporting data: a Stanford paper improved a cheap model’s task success from 15.9% to 56% through optimisation; Anthropic research found token spend explained 80% of the variance between successful and unsuccessful agent runs.
• Practical takeaway: stop asking “what can we do with agents” and start asking “which specific tasks justify agent deployment cost” — the highest-value insight is often knowing when not to automate.