Agentic AI

MLE-STAR (Machine Learning Engineering via Search and Targeted Refinement) is a state-of-the-art agent system developed by Google Cloud researchers to automate complex machine learning ML pipeline design and optimization. By leveraging web-scale search, targeted code refinement, and robust checking modules, MLE-STAR achieves unparalleled performance on a range of machine learning engineering tasks—significantly outperforming previous autonomous…

In this tutorial, we explore a powerful multi-agent system built around the PEER pattern: Plan, Execute, Express, and Review. We run the entire workflow in Google Colab/Notebook, integrating agents with specialized roles and leveraging Google’s Gemini 1.5 Flash model via a free API key. As we walk through the system, we observe how each agent…

In this tutorial, we delve into building an advanced AI agent with agent memory using Cognee and Hugging Face models, utilizing entirely free, open-source tools that work seamlessly in Google Colab and other notebook. We configure Cognee for memory storage and retrieval, integrate a lightweight conversational model for generating responses, and bring it all together…

AgentSociety is a cutting-edge, open-source framework designed to simulate large populations of agents, each powered by Large Language Models (LLMs), to realistically model the complex interactions found in human societies. Leveraging powerful distributed processing technologies—especially Ray—this project achieves simulations involving tens of thousands of simultaneously active agents, each embedded in detailed, realistic environments that capture…

In this tutorial, we explore the advanced capabilities of Google’s Agent Development Kit (ADK) by building a multi-agent system equipped with specialized roles and tools. We guide you through creating agents tailored for tasks such as web research, mathematical computation, data analysis, and content creation. By integrating Google Search, asynchronous execution, and modular architecture, we…

In this tutorial, we walk through the creation of an advanced AI evaluation framework designed to assess the performance, safety, and reliability of AI agents. We begin by implementing a comprehensive AdvancedAIEvaluator class that leverages multiple evaluation metrics, such as semantic similarity, hallucination detection, factual accuracy, toxicity, and bias analysis. Using Python’s object-oriented programming, multithreading…