Building a Multi-Agent System in Python

You Probably Don’t Need an Agent Framework

Most LLM applications need a clear workflow, not an autonomous agent. Here's how to build one in plain Python.

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 .

Why Most Multi-Agent Systems Fail Even When Evaluation Passes

The article explains how to detect a payload that appears correct yet is not, by employing a watchdog pattern in Python. It discusses the challenges that cause many multi‑agent systems to fail even when their evaluations succeed. The post was originally published on Towards Data Science.

Tiny Agents in Python: a MCP-powered agent in ~70 lines of code

Computer-Using Agent

I Built a Tool-Calling Agent in Python. Here’s How I Debugged It

A minimal loop with real API calls, validation, compact outputs, and trace evidence before adding an agent framework The post I Built a Tool-Calling Agent in Python. Here’s How I Debugged It appeared first on Towards Data Science .

How to Orchestrate 100+ Agents With Claude Code

Run 100+ agents in parallel The post How to Orchestrate 100+ Agents With Claude Code appeared first on Towards Data Science .

The Python Ecosystem That Changed AI Development

How one open-source ecosystem made state-of-the-art AI accessible The post The Python Ecosystem That Changed AI Development appeared first on Towards Data Science .

Where the Agent Development Lifecycle Fits

The article "Where the Agent Development Lifecycle Fits" discusses how to coordinate the development of agent capabilities with the applications they power. It highlights the importance of aligning agent creation processes with the needs of the end‑use cases they support. The piece appears on Towards Data Science and focuses on integrating agent development into broader application workflows.

LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios

The article surveys LLM-based agentic reasoning frameworks, presenting a unified formal language that categorizes them into single-agent, tool-based, and multi-agent methods. It reviews application scenarios in scientific discovery, healthcare, software engineering, society, economics, and general-purpose tasks, and compares the distinct features and evaluation strategies of each category. The survey highlights the rapid development of complex agentic systems in real-world contexts.

How I Built a Multi-Agent System for Interrupted Time Series Analysis (ITSA)

What it takes to turn counterfactual analysis into an AI product The post How I Built a Multi-Agent System for Interrupted Time Series Analysis (ITSA) appeared first on Towards Data Science.

Building Enterprise Agent Systems that People can Trust, Verify and Improve

The article outlines five principles that guide the successful deployment of enterprise agent systems, illustrated with a real-world example from a $100M+ company. It explains how these principles help ensure that such systems can be trusted, verified, and improved over time. The post serves as a practical guide for building reliable agent-based solutions in production environments.