Stateful vs. stateless: managing context in AI agent builds
An n8n guide compares stateful and stateless designs and how each affects scaling and memory in agent workflows. It explains when to use memory nodes, state stores or both.
Related stories
Flint-interactive: a language for creating interactive data visualizations
Microsoft Research's Flint-interactive gives AI agents a language for building interactive charts. Readers can click, filter and highlight related content to explore beyond the author's original view.
OpenAI donates Open Responses, its spec for a common LLM API, to the Agentic AI Foundation
OpenAI is handing the Open Responses API specification to the open-source Agentic AI Foundation. The spec is meant to give language models a shared API, and the move places it under neutral governance.
Spotify's AI bet is to be the music service agents choose
Spotify co-CEO Gustav Söderström explains why the rise of AI agents could benefit the streaming service. The idea is to become the default music provider when agents pick on users' behalf.
Self-hosted LLM vs. cloud APIs: how to route between them
An n8n guide explains that running a model yourself is not always cheaper than calling an API. It covers when on-premises wins, what local hardware can handle and how to route requests between local and cloud models.
IBM puts governed context at the center of production AI
IBM argues that enterprise AI agents need governed, up-to-date context drawn from the systems they work across. Its approach ties that data foundation to the broader task of putting AI into production.