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AWS Machine Learning Blog (02 NOV 2026; Zappia et al.) shows SeqKD of a shopping agent: Bedrock GLM-5.3 teacher → ShoppingBench rejection sampling → LoRA SFT on Qwen3.5-4B (SageMaker AI). Held-out 324 problems, single LoRA run: student ASR 0.541 vs greedy teacher 0.540 vs base 0.297; tool-only 0.415; IFEval ≈6–8 pts down. No per-task cost/latency published. AWS-reported; we haven't retested.
AWS released Infinity under Apache 2.0 as part of Hydro with UC Berkeley. The research runtime suspends an agent during a tool call, persists the conversation, and resumes when the result arrives—so an idle agent mostly costs its history, and a serverless function can end while it waits. AWS’s own blog benchmarks cite about 100 KB per idle agent and about 75,000 concurrent agents in 8 GB. Early research release seeking feedback, not production GA. Separate from OpenAI Agents API scheduled automations.
AWS opened TorchNeuron as a Public Beta native PyTorch backend for Trainium2 and Trainium3. Device placement can move to neuron with eager mode, torch.compile, and standard APIs; compile backends, unsupported ops, and NKI custom kernels still need attention. Open source is not free cloud compute, and We have not benchmarked cost or speed.
Putting an AI assistant in a messaging app makes delegation feel effortless. That convenience depends on a less visible distinction: being able to contact an assistant should not automatically mean…