Scaling Human Judgment: How Dropbox Uses LLMs to Improve Labeling for RAG Systems
To improve the relevance of responses produced by Dropbox Dash, Dropbox engineers began using LLMs to augment human labelling, which plays a crucial role in identifying the documents that should be used to generate the responses. Their approach offers useful insights for any system built on retrieval-augmented generation (RAG).
OpenAI Extends the Responses API to Serve as a Foundation for Autonomous Agents
OpenAI announced they are extending the Responses API to make it easier for developer to build agentic workflows, adding support for a shell tool, a built-in agent execution loop, a hosted container workspace, context compaction, and reusable agent skills.
Google Open Sources Experimental Multi-Agent Orchestration Testbed Scion
Designed to manage concurrent agents running in containers across local and remote compute, Scion is an experimental orchestration testbed that enables developers to run groups of specialized agents with isolated identities, credentials, and shared workspaces.
How Slack Manages Context in Long-running Multi-agent Systems
To sustain productivity in long-running agent systems, Slack engineers moved away from accumulating chat logs and started using structured memory, validation, and distilled truth to maintain coherence and accuracy of long-running agent systems.
Google New TPU Generation is Specifically Designed for Agents and SOTA Model Training
Google has unvelied a new generation of Tensor Processing Units (TPUs), featuring two specialized chips designed to accelerate model training and agent workflows, which require continuous, multi-step reasoning, and action loops distributed across multiple models. The new TPUs deliver better performance, memory, and energy efficiency, the company says.