GPT5.6 more effective than Fable
A small observation from ongoing daily work on Agentery: GPT-5.6 is noticeably better than Fable at practical, real-world system design.
Not because it writes more elegantly, or produces more ideas. It is better at noticing when a proposed solution creates a new problem.
Today's example - fixing a bug with the agent price index:
Agentery has one overall price index for each niche, plus four sub-indices for Individual, Pro, Team/SME and Enterprise pricing.
The problem was that the tier charts and the main niche index were calculated differently, so they could contradict each other.
Fable’s suggestion was to rebuild the overall niche index as the equal-weight average of the four tier indices. The tier charts would then add up neatly to the headline number, and every surface could use the same result.
It sounded tidy.
GPT-5.6 pointed out the problem immediately.
If a niche has 20 Pro agents and only one Enterprise agent, equal-weighting the four tiers gives that single Enterprise agent the same influence as the entire Pro market.
Worse, the overall index would now depend on the model correctly assigning every plan to a buyer tier. A single misclassified plan could distort the headline number.
GPT-5.6 proposed a much better solution:
Leave the existing equal-agent overall index alone. It is already the more accurate measure. Then decompose its movement into tier contributions that sum back to the overall.
So the tiers explain the headline; they do not rebuild it.
That distinction matters. A mis-tiered plan may affect the wrong sub-chart, but it cannot corrupt the overall niche index.
This is the kind of difference I keep noticing.
Fable often produces a clean answer to the visible problem. GPT-5.6 is more likely to ask what the proposed fix breaks elsewhere, preserve the parts that already work, and make the smallest structural change needed.
For production systems, that is usually the better answer.
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