Building an intelligent agent goes far beyond clever prompt engineering for language models. To create real-world, autonomous AI systems that can think, reason, act, and learn, you need to engineer a full-stack solution that orchestrates multiple tightly–integrated components. The following seven-layer framework is a battle-tested mental model for anyone serious about AI agent development—whether you’re…
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 the Agents of Change podcast, host Anthony Witherspoon welcomes Archie Mayani, Chief Product Officer at GHX (Global Healthcare Exchange), to explore the vital role of artificial intelligence (AI) in healthcare. GHX is a company that may not be visible to the average patient, but it plays a foundational role in ensuring healthcare systems operate…
Deep Research (DR) agents have rapidly gained popularity in both research and industry, thanks to recent progress in LLMs. However, most popular public DR agents are not designed with human thinking and writing processes in mind. They often lack structured steps that support human researchers, such as drafting, searching, and using feedback. Current DR agents…
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…
Estimated reading time: 5 minutes Table of contents Introduction to LangGraph Key Features: Setting Up Our Environment Installation Understanding the Power of Coordinated Processing Introduction to LangGraph LangGraph is a powerful framework by LangChain designed for creating stateful, multi-actor applications with LLMs. It provides the structure and tools needed to build sophisticated AI agents through…
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…