Frameworks & Platforms

Google Agent Space, a platform by Google Cloud, empowers enterprises to create, deploy, and manage AI agents leveraging Google's advanced AI technologies. This article highlights how Agent Space facilitates seamless integration with popular apps, enables multimodal search across enterprise data, drives impact through content generation and automation, and unlocks innovation with AI agents, emphasizing enterprise-grade security and compliance.

Google Cloud's Agentspace is a powerful Enterprise Search and Assistant platform that utilizes intelligent agents powered by Gemini reasoning to enhance productivity by tackling information silos. This article provides an overview and UI walkthrough of Agentspace, offering insights into how it can transform enterprise information access and boost productivity, making it valuable for AI developers interested in building AI assistants and enterprise search solutions.

This code snippet demonstrates how to use agent operations with the Sharepoint tool from the Azure Agents service using a synchronous client. It creates an agent, initializes a Sharepoint tool with a connection ID, and interacts with agents and threads for communication. The code provides a practical implementation of agent orchestration and tool integration within an AI project environment.

This code snippet demonstrates how to use agent operations with a toolset from the Azure Agents service using a synchronous client. It showcases creating an agent with a toolset, enabling auto function calls, creating threads for communication, and sending messages within an agent system.

This code snippet demonstrates how to use agent operations with the OpenAPI tool from the Azure Agents service using custom key authentication against the TripAdvisor API. It showcases setting up connections, loading OpenAPI specs, and initializing an Agent OpenAPI tool. This code provides practical implementation examples for integrating Azure AI services with external APIs.

This code snippet demonstrates how to use the Azure Agents service with OpenAPI tools for agent operations using a synchronous client. It showcases setting up authentication, initializing agent tools with OpenAPI specs, and creating an agent to retrieve weather information and a list of countries. The code provides hands-on experience in integrating Azure AI services and working with OpenAPI specifications.

This code snippet demonstrates how to use agent operations with multiple Model Context Protocol (MCP) servers from the Azure Agents service using a synchronous client. It showcases how to interact with AI models deployed on different MCP servers and manage tools dynamically within the agents.

This code snippet demonstrates how to use Azure Agents service with the Connected Agent tool in Python. It shows how to create multiple agents, such as 'stock_price_bot' and 'weather_bot', and provides instructions for setting up the environment variables and dependencies before running the sample.

This code snippet demonstrates how to use agent operations with custom functions from the Azure Agents service using a synchronous client. It showcases creating an agent, running user requests with function calls, creating threads, messages, and runs, and monitoring the status of the run.

This code snippet demonstrates how to use Agent operations with the Microsoft Fabric grounding tool from the Azure Agents service using a synchronous client. It showcases creating an agent, initializing a Fabric tool, creating threads for communication, and processing an Agent run with tools.

This code snippet demonstrates how to use agent operations with a code interpreter from the Azure Agents service using a synchronous client. It showcases creating an agent, creating a thread, and uploading a local file to Azure for vector store creation.

This code snippet demonstrates how to use agent operations with a code interpreter from the Azure Agents service using a synchronous client. It showcases uploading a file, creating an agent with a code interpreter tool, and creating a message thread within an AI project environment.

This code snippet demonstrates how to create an agent using the Bing grounding tool from the Azure Agents service. It showcases the integration of the Azure AI Project client, agent creation with specific tools, and message handling within a thread, providing insights into building AI agents with specialized functionalities.

This code snippet demonstrates how to use the Azure Agents service with the Bing Custom Search tool to create an agent for performing search operations. It showcases the integration of Azure AI tools for agent orchestration and communication.

The blog post introduces the concept of agentic AI in Azure AI Foundry, emphasizing how agents can reason, act, and collaborate to bridge knowledge and outcomes. It discusses common use cases and design patterns for leveraging agentic AI, providing valuable insights for AI developers interested in building intelligent agents.

This article explores the Thought-Action-Observation (TAO) cycle in AI agents, emphasizing how agents reason, act, and learn from their experiences. It delves into the core components of the TAO cycle, such as Thought, Action, and Observation, providing insights and real-world examples for understanding how AI agents operate effectively.

This repository provides a supervisor agent for the Open Agent Platform, enabling dynamic agent delegation and configurable agents for orchestrating specialist agents. It leverages LangGraph Supervisor and RemoteGraph for agent orchestration.

This article delves into Agentic AI architectures and design patterns, focusing on autonomous AI systems that make decisions independently. It discusses key tools like LangChain and AutoGen, design patterns for building complex autonomous systems, and challenges such as coordination in multi-agent setups and potential biases. AI developers can learn about enhancing productivity, real-time data handling, and implementing design patterns for improved AI performance.

The video provides a concise overview of Google Agentspace, explaining its functionality and purpose. It offers a quick insight into building AI agents on Google Cloud, making it a valuable resource for AI developers interested in multi-agent systems and cloud-based AI development.

The article delves into the importance of aligning AI deployments with ethical imperatives, regulatory mandates, and business value. It offers a systematic examination of responsible AI ecosystems, platforms, providers, and regulatory frameworks, providing insights for decision-makers to navigate the complexities of AI governance.

This repository provides a Python library for creating hierarchical multi-agent systems using LangGraph. It allows users to create a supervisor agent to orchestrate multiple specialized agents, with features like tool-based agent handoff mechanism and flexible message history management. The library is built on top of LangGraph, offering support for streaming, memory management, and human-in-the-loop interactions.

This article provides a framework for the governance of artificial intelligence, emphasizing the importance of values like transparency and truth in AI systems. It offers policymakers strategic methodologies for overseeing AI technologies, promoting trust, accountability, and ethical compliance.

Agentic AI is an advanced artificial intelligence system that exhibits autonomy, goal-driven behavior, and adaptability, allowing it to accomplish specific tasks independently. This article explores how agentic AI leverages generative AI techniques and large language models to function in dynamic environments, highlighting its potential for autonomous completion of complex tasks.

The article explores the distinction between Agentic AI, focusing on autonomous decision-making, and Generative AI, which creates new content like text, images, and code. It discusses the impact of large language models, machine learning, and natural language processing on autonomous tasks, highlighting examples like ChatGPT and autonomous vehicles.

Azure AI Foundry is introduced as a comprehensive solution for creating AI agents efficiently. The platform offers the ability to select appropriate models and enhance agents with knowledge, providing valuable insights for AI developers looking to build intelligent solutions.