Level 300: Builder

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.

This article explores the evolving use cases of AI, particularly in personal and professional lives, focusing on applications like therapy/companionship, organizing life, and finding purpose. It delves into the surprising shift from productivity-driven uses to more growth-oriented and personal applications, shedding light on the human-AI interaction landscape.

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 article delves into the systematic process of identifying and prioritizing high-impact AI use cases for enterprise implementation, covering strategic imperatives, alignment with core strategies, measuring business value, feasibility, data readiness, risk management, ROI, scalability, ethical considerations, talent and skills, sustainability, adoption, and customer impact. It provides a comprehensive blueprint for leaders navigating AI adoption in organizations.

This article delves into the use cases of AI, focusing on image/media generation and AI customer support chat systems. The author shares insights from their AI deep dive, including building AI apps like chatbots on Telegram to showcase AI applications. AI developers can learn about practical AI implementations and the potential of AI in enhancing existing products.

This article provides a step-by-step guide for .NET developers to create a modular multi-agent system using Google's A2A, Microsoft's Semantic Kernel, and Semantic Kernel C# SDK. Readers will learn about the architecture of multi-agent systems, setting up A2A servers and clients, and real-time orchestration, offering practical insights into building AI agents that collaborate seamlessly.

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 introduces the Model Context Protocol (MCP) and its significance for Azure AI Foundry, enabling seamless integration between AI agents and tools without manual coding. By leveraging MCP, developers can enhance agent development speed, reduce maintenance overhead, and achieve interoperability across Azure and OpenAI platforms.

The article discusses the author's critical views on AI, particularly focusing on the hype surrounding Generative AI and the author's decision to not fully embrace AI technology. It provides insights into the author's perspective on the current state and future potential of AI, offering a critical viewpoint for AI developers to consider.

Azure AI Foundry is a comprehensive web portal that consolidates various Azure AI services, facilitating collaborative AI project development, model deployment, testing, and integration of custom data sources. AI developers can leverage Azure AI Foundry to create generative AI applications, deploy models to real-time endpoints, define workflows, and incorporate multiple AI capabilities through Azure AI Services.

This article from Tribe AI provides a comprehensive guide to advanced AI governance, focusing on essential strategies for ensuring organizational compliance with advanced AI systems. It covers core principles, challenges, and best practices in AI governance, emphasizing ethics, transparency, and regulatory standards, offering valuable insights for AI developers and leaders in shaping responsible AI adoption.

The article introduces AWS SageMaker Unified Studio, an all-in-one interface that streamlines the machine learning and AI development lifecycle. It highlights the accessibility of data, collaboration features, model training capabilities, and integration with Amazon Q Developer for code assistance, making it suitable for various users from research projects to enterprise teams.

Mosaic AI Agent Bricks simplifies the extraction of structured data from documents like contracts and invoices without manual labeling or schema training. AI developers can learn how to enhance automation and reduce effort by leveraging schema feedback and AI-assisted evaluation for continuous improvement.

This article delves into the common reasons why AI projects fail, highlighting challenges such as unclear success metrics, scope creep, and the added layer of probabilistic uncertainty in AI projects. It provides insights on how organizations can avoid these pitfalls and improve the success rate of their AI initiatives, making it a valuable read for AI developers and builders.

This article reflects on the journey of creative.ai, an early exploration into creative AI, emphasizing the focus on augmenting human creativity rather than replacing it. It discusses the challenges faced, inspirations drawn from past creative computer science works, and the ethos of empowering artists and designers. The open-sourcing of code repositories adds value for AI developers interested in creative AI and historical perspectives on computational creativity.

The article introduces 13 foundational AI courses and resources from MIT Open Learning, covering topics like artificial intelligence, machine learning, machine vision, and algorithms. These resources are valuable for AI developers looking to grasp the basics and advance their knowledge in AI technologies.

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.

Azure AI Foundry is a platform for designing and managing AI applications, offering a secure and flexible environment. This article delves into the significance of Azure AI Foundry as an AI app factory, providing insights for AI developers on leveraging this tool for AI application development.

Open Agent Platform is an educational platform for building and managing no-code agents with advanced features like RAG integration and agent supervision. It provides a user-friendly interface for creating and interacting with LangGraph agents, making it suitable for users without technical expertise as well as developers.

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 comprehensive guide to Amazon Bedrock, a managed AWS service for accessing and managing foundation models (FMs) essential for generative AI applications. AI developers can learn how to leverage AWS Bedrock to simplify infrastructure management, access cutting-edge AI models, and develop scalable generative AI applications aligned with their goals.

GraphRAG, a retrieval mechanism enhancing GenAI applications by leveraging graph data structures, is discussed in this article. AI developers can learn how GraphRAG optimizes information retrieval in graph databases, offering insights into improving AI systems' performance.

Open Deep Research is an open-source deep research agent that works across various model providers, search tools, and servers. It has achieved notable performance and rankings on the Deep Research Bench Leaderboard. The repository offers a free course and updates on recent advancements in deep research.

This article provides a walkthrough on building an AI agent observability system using LangChain, Rudderstack, and Clickhouse. It explores the importance of monitoring AI agents in chat systems and delves into the concept of AI observability, offering practical insights for developers interested in enhancing AI system visibility and performance.

This article delves into the transformative impact of effective prompt engineering in AI for solving business challenges. It emphasizes the importance of using specific frameworks to optimize AI models like Claude, Copilot, Perplexity, and others, offering practical insights for AI developers to enhance their prompt creation skills.

Caleb Baechtold from Snowflake demonstrates building an application using Cortex Analyst to allow business users to ask data questions in natural language. The walkthrough covers setting up Snowflake, creating a Streamlit app, testing conversational analytics, exploring the semantic model, and more, offering practical insights into leveraging AI for business intelligence.

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.

langfuse is an open-source LLM engineering platform that focuses on LLM observability, metrics, evaluations, prompt management, playgrounds, and datasets. It integrates with various technologies like OpenTelemetry, Langchain, OpenAI SDK, LiteLLM, and more. The repository is suitable for individuals interested in exploring large language models and observability in AI systems.

ChainForge is an open-source visual programming environment designed for battle-testing prompts to Large Language Models (LLMs). It enables users to query multiple LLMs simultaneously, compare response quality across different prompts and models, set up evaluation metrics, and streamline the prompt engineering process using AI features.

LangChain is a framework for building context-aware reasoning applications powered by LLM technology. It enables developers to create AI applications by chaining interoperable components and third-party integrations, simplifying AI development and future-proofing decisions as technology evolves.

Azure AI Foundry offers a comprehensive platform for designing, customizing, and managing AI applications and agents using various tools like GitHub, Visual Studio, and Copilot Studio. Developers can access a wide range of industry-leading AI models and services to build and deploy AI solutions efficiently.

This tutorial guides AI developers in building a retrieval agent that can make decisions on retrieving context from a vector store or responding directly to users. Key takeaways include fetching and preprocessing documents, indexing them for semantic search, and creating an agentic RAG system for decision-making.

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.

Graph RAG is a powerful approach that enhances large language models (LLMs) with external knowledge, enabling more relevant and accurate answers to natural language questions. This article delves into the importance of integrating domain-specific proprietary knowledge into conversational interfaces through the Graph RAG approach, offering valuable insights for AI developers looking to optimize question-answering systems.