Unverified50% confidenceOpinionExact time
The calculation in the article argues that teams with predictable, relatively heavy usage get better value from Team Seats, while teams with highly variable or relatively light usage are better off with the API.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/12/2026
First Seen
Valid until: 1/10/2027
Sources
Related Entities
Related Claims
UnverifiedOn-demand state repartitioning allows teams to go live first with a reasonable initial value, then gradually optimize the partition configuration based on real operational data, shifting capacity planning from a one-time decision to continuous adjustment.64% similarUnverifiedTeams interested in trying it are advised to first test agents in low-risk scenarios (such as standup summaries and request triage) before gradually expanding to work that requires more judgment.63% similarUnverified少样本示例的质量和代表性比数量更关键，与目标任务分布高度吻合的示例能显著提升性能63% similarUnverifiedRetraining involves a trade-off: some extremely high scores showed a slight decline on cleaner validation distributions, and the team considered it worthwhile to sacrifice perfect benchmark scores in exchange for improved real-world performance.62% similar
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