Agentic AI

The Agent-to-Agent (A2A) protocol is a new standard by Google that enables AI agents—regardless of their underlying framework or developer—to communicate and collaborate seamlessly. It works by using standardized messages, agent cards (which describe what an agent can do), and task-based execution, allowing agents to interact via HTTP without custom integration logic. A2A makes it…

The Importance of Symbolic Reasoning in World Modeling Understanding how the world works is key to creating AI agents that can adapt to complex situations. While neural network-based models, such as Dreamer, offer flexibility, they require massive amounts of data to learn effectively, far more than humans typically do. On the other hand, newer methods…

Introduction AI agents are increasingly moving from pure backend automators to visible, collaborative elements within modern applications. However, making agents genuinely interactive—capable of both responding to users and proactively guiding workflows—has long been an engineering headache. Each team ends up building custom communication channels, event handling, and state management, all for similar interaction needs. The…

OpenAI has open-sourced a new multi-agent customer service demo on GitHub, showcasing how to build domain-specialized AI agents using its Agents SDK. This project—titled openai-cs-agents-demo—models an airline customer service chatbot capable of handling a range of travel-related queries by dynamically routing requests to specialized agents. Built with a Python backend and a Next.js frontend, the…

Python A2A is an implementation of Google’s Agent-to-Agent (A2A) protocol, which enables AI agents to communicate with each other using a shared, standardized format—eliminating the need for custom integration between services. In this tutorial, we’ll use the decorator-based approach provided by the python-a2a library. With simple @agent and @skill decorators, you can define your agent’s…