Codex vs Fable: Which AI Agent Picked the Better Problem?

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• Nate gave Fable and Codex the identical open-ended brief — find something in the business worth automating — and let each model choose the problem rather than execute a pre-assigned task; the two models disagreed on what mattered.
• Codex built a cleaner research-to-scripting handoff (good UX, but a secondary bottleneck); Fable targeted story selection itself — “one of the hardest jobs,” requiring evidence, audience feel, and instinct — a higher-leverage but harder target.
• Named shift: “picking the problem is becoming part of the agent’s job” — moves model routing earlier in the workflow, before the deliverable is even specified.
• Nate deliberately redesigned a “magic button” prototype (letting AI both pick and build the automation) to insert a human decision gate rather than let the agent self-select and execute unsupervised.
• Practical takeaway: pilot “problem discovery” agent runs that generate multiple evidenced automation candidates for a human to choose from, rather than assuming the org has already identified the right problem to automate.