Agentic AI

A Team of researchers from USC, Salesforce AI and University of Washington have introduced CoAct-1, a pioneering multi-agent computer-using agent (CUA) that marks a significant leap in autonomous computer operation. By elevating coding to a first-class action—on par with traditional GUI manipulation—CoAct-1 overcomes longstanding challenges of efficiency and reliability in complex, long-horizon computer tasks. On…

Google DeepMind has announced Genie 3, a revolutionary AI system capable of generating interactive, physically consistent virtual worlds from simple text prompts. This marks a substantial leap in the field of world models—a class of AI designed to understand and simulate environments, not merely render them, but produce dynamic spaces you can move through and…

In this tutorial, we dive into building an advanced AI agent system based on the SAGE framework, Self-Adaptive Goal-oriented Execution, using Google’s Gemini API. We walk through each core component of the framework: Self-Assessment, Adaptive Planning, Goal-oriented Execution, and Experience Integration. By combining these, we aim to create an intelligent, self-improving agent that can deconstruct…

OpenAI has just sent seismic waves through the AI world: for the first time since GPT-2 hit the scene in 2019, the company is releasing not one, but TWO open-weight language models. Meet gpt-oss-120b and gpt-oss-20b—models that anyone can download, inspect, fine-tune, and run on their own hardware. This launch doesn’t just shift the AI…

In this tutorial, we explore how to integrate Microsoft AutoGen with Google’s free Gemini API using LiteLLM, enabling us to build a powerful, multi-agent conversational AI framework that runs seamlessly on Google Colab. We walk through the process of setting up the environment, configuring Gemini for compatibility with AutoGen, and building specialized teams of agents…

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…