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AWS open-sources Strands Harness

AWS has released Strands Harness, a fully assembled agent, under the Apache 2.0 license. A harness is the runtime that turns a language model into an agent: it manages tools, context and intermediate steps. Strands runs on models via Amazon Bedrock, Anthropic, OpenAI, Google, Ollama and LiteLLM. By AWS’s own measurements it gets by with 28 percent lower token costs than other harnesses on the same models, at equal or better accuracy. The savings are meant to come mainly from its context defaults: long tool results are truncated, and once the context window is 85 percent full the agent summarizes it. In the same comparison AWS names DeepSeek Harness the most token-efficient, but also the least accurate.

Our take: Strands is a solid foundation for agents on AWS – but it is only the runtime. Approvals, governance and the question of which actions a person has to confirm are not part of the harness. That remains work for each project.

Read the announcement on strandsagents.com
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David Gorges, Managing Director

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Mistral Large 4: Europe's answer to the frontier models

Mistral released Large 4 as a public preview today. It is a 1.05-trillion-parameter Mixture-of-Experts model with 49B active parameters, text and vision input and a one-million-token context window. Mistral trained it from scratch on 3,800 Nvidia Grace Blackwell GPUs in its own European datacenters and runs it there, end to end, under European law. The weights are due as open weights by the end of October.

It doesn't quite match the leading US models, but if you need your data and your provider to stay in Europe, it is the first serious option at this size.

Read Mistral's announcement

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