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The harder prediction is whether the context-window problem gets a real answer. Today, the moment a tool returns sensitive data into the context, that data has already left the user’s environment and reached the model provider. Sandboxing, approval gates, and output filters all operate after that boundary has been crossed. Some version of confidential computing or client-side inference may eventually close that gap for high-sensitivity workloads, but the timeline is unclear and the performance trade-offs are steep. For most teams, this means accepting a residual exposure that no permission architecture can eliminate, and scoping agent access accordingly.
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GCP prices vary per region and feature some strange patterns. For example when you reserve, t2d instances which give you a full AMD EPYC core per vCPU and n2d instances which give you a Simultaneous Multi-Thread (i.e. HALF a core) per vCPU have the same price per vCPU, but n2d is cheaper on demand and gets a 20% discount for sustained monthly use.