newsletterAugust 4, 2026·Towards Data Science

Using Agents as Tools

Building manager–specialist workflows with the OpenAI Agents SDK The post Using Agents as Tools appeared first on Towards Data Science.

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•Building manager–specialist workflows with the OpenAI Agents SDK The post Using Agents as Tools appeared first on Towards Data Science.
•This story was reported by Towards Data Science, covering developments in the newsletter space.
•AI advancements continue to reshape industries — read the full article on Towards Data Science for complete coverage.

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The Medallion Data Architecture: An Introduction

A practical guide to Bronze, Silver and Gold, with a working Python and DuckDB example The post The Medallion Data Architecture: An Introduction appeared first on Towards Data Science.

Towards Data Science

How to Get More Statistical Power from Fewer Research Participants

An online simulation and a novel method for increasing power The post How to Get More Statistical Power from Fewer Research Participants appeared first on Towards Data Science.

Towards Data Science

Prompt, Context, Loop: The Three Engineering Layers Every RAG System Is Built On

Enterprise Document Intelligence [Vol.1 #M2] - Every RAG system is built in three engineering layers stacked on one LLM call: prompt (the call itself), context (what fills the model’s window), loop (when the next call fires and when it stops). Knowing which layer you are standing on is half of build

Towards Data Science

How to Build CLI Agents with Python & Ollama

Create a local CLI Agent from scratch completely for free The post How to Build CLI Agents with Python & Ollama appeared first on Towards Data Science.

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Using Agents as Tools

Key Takeaways

Related Articles

The Medallion Data Architecture: An Introduction

How to Get More Statistical Power from Fewer Research Participants

Prompt, Context, Loop: The Three Engineering Layers Every RAG System Is Built On

How to Build CLI Agents with Python & Ollama

Discussion

Using Agents as Tools

Key Takeaways

Related Articles

The Medallion Data Architecture: An Introduction

How to Get More Statistical Power from Fewer Research Participants

Prompt, Context, Loop: The Three Engineering Layers Every RAG System Is Built On

How to Build CLI Agents with Python & Ollama

Discussion

Related Articles

Towards Data Science
The Medallion Data Architecture: An Introduction
A practical guide to Bronze, Silver and Gold, with a working Python and DuckDB example The post The Medallion Data Architecture: An Introduction appeared first on Towards Data Science.

Towards Data Science
How to Get More Statistical Power from Fewer Research Participants
An online simulation and a novel method for increasing power The post How to Get More Statistical Power from Fewer Research Participants appeared first on Towards Data Science.

Towards Data Science
Prompt, Context, Loop: The Three Engineering Layers Every RAG System Is Built On
Enterprise Document Intelligence [Vol.1 #M2] - Every RAG system is built in three engineering layers stacked on one LLM call: prompt (the call itself), context (what fills the model’s window), loop (when the next call fires and when it stops). Knowing which layer you are standing on is half of build

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How to Build CLI Agents with Python & Ollama
Create a local CLI Agent from scratch completely for free The post How to Build CLI Agents with Python & Ollama appeared first on Towards Data Science.