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Agent Eye's landscape continues to evolve at an incredible rate, and practitioners find it increasingly difficult to keep multiple agents on a task despite their intersecting workflows with each other.
We've put together an incredible lineup of articles exploring two tools: Google's Agent2Agent protocol and Hugging Face's Smolagents framework to minimize chaos and maintain harmony among agents. Read on to learn how to utilize them in your own cutting edge projects.
Within Google's Agent2Agent (A2A) protocol: Teach AI agents to talk to each other
If you're taking the first step with an AI agent, don't miss an accessible introduction to the A2A protocol: “Because it's promised to split AI agents out of the silo and work together like a well-tuned team, not an isolated genius.”
Multi-agent communication with A2A Python SDK
Ready to mess with your A2A? Deborah Mesquita creates examples of toys that will help you understand how the protocol works inside.
From data to stories: KPI story code agent
For another approach to multi-agent orchestration, Mariya Mansurova walks through a smolajan driven workflow.
Must-see stories of this week
We catch up with articles that have been making waves in our community lately. Here's a summary of this week's trending stories:
How to Design My First AI Agent by Fabiana Clemente
Build modern dashboards with Python and Gradio with Thomas Reid
How Himanshu Sharma automates machine learning workflows with just 10 lines of Python
Other recommended readings
Check out some of the most recent articles on other topics, from LLM Agent Benchmarks to programming best practices.
- Gaia: What everyone talks about LLM Agent Benchmarks, Shuai Guo
- Bayesian optimization for hyperparameter tuning of deep learning models, Iwako ko
- Jupyter to Programmer Journey: A Quick Start Guide by Lucy Dickinson
Meet our new authors
We are excited to welcome a fresh cohort of data science, machine learning and AI experts each week. Don't miss some of our latest contributors' work:
- Maciej Adamiak combines geospatial data with a passion for deep learning with his research at the University of Lodz.
- Sylvain Kalache is head of Rootly's AI lab and has an interdisciplinary background, including communication and education.
- Doster Esh immerses his writing in depth expertise in data science, economics and risk assessment, among other fields.
We love publishing articles from new authors, so if you recently wrote an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, why not share it?
