Dataiku Launches AI Agents

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Welcome to AI Agents Report – your essential guide to mastering AI agents.

Get the highest-quality news, tutorials, papers, models, and repos, expertly distilled into quick, actionable summaries by our human editors. Always insightful, always free.

In Today’s Report:

🕒 Estimated Reading Time: 5 minutes 25 seconds

📌 Top News:

• Dataiku launches AI Agents, bringing AI agent creation and control capabilities directly into their unified AI platform for enterprises.

⚡️Trending AI Reports:

• EY announces the launch of its EY.ai Agentic Platform, built in collaboration with NVIDIA AI, to drive multi-sector transformation, initially focusing on tax and risk domains.

• OpenAI introduces the Responses API and Agents SDK, new tools aimed at streamlining the development and deployment of AI agents for various applications.

• Chinese startup Monica launches Manus, a new AI agent claiming general-purpose capabilities and achieving state-of-the-art performance on benchmarks.

💻 Top Tutorials:

• Integrating AI Agents with Robotic Operating Systems (ROS): A comprehensive guide to connecting AI agents with the Robot Operating System for enhanced robot intelligence.

• Developing Perception Capabilities for AI Agent-Controlled Robots: Detailed tutorials on enabling AI agents to process and understand sensory data from robotic platforms.

• Implementing Planning and Control for Autonomous Robots with AI Agents: Practical guides on using AI agents for high-level planning and low-level control of robotic movements.

🛠️ How-to:

• Build Multi-Agent AI Systems with CrewAI

📰 BREAKING NEWS

Dataiku Launches AI Agents with Dataiku

Overview

Dataiku, an AI platform company, has announced the launch of AI Agents with Dataiku, providing capabilities for enterprises to create, control, and scale AI agents within their existing AI infrastructure.

Key Features:

• Integrated Agent Creation: AI agent creation is now directly integrated into Dataiku’s Universal AI Platform, allowing users to build agents alongside analytics and predictive models.

• Scalable Agent Control: The platform is designed to manage and control AI agents at scale, addressing the needs of enterprise deployments.

• Governed Agent Tools: Dataiku provides managed agent tools to ensure the quality and validation of tools used by AI agents.

• Centralized Agent Access: Agent Connect within Dataiku centralizes agent access across the organization through a single interface.

• Agnostic Approach: Dataiku supports major cloud environments, model providers, and data platforms for flexible agent deployment.

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⚡️TRENDING AI REPORTS

EY Launches EY.ai Agentic Platform

Overview: EY has announced its EY.ai Agentic Platform, developed in collaboration with NVIDIA AI, to help organizations scale enterprise AI agents and transform various business functions, initially focusing on tax and risk management.

Key Features:

• Built with NVIDIA AI: The platform leverages the full NVIDIA AI stack, including NVIDIA AI Enterprise and new reasoning models.

• Multi-Cloud Deployment: The platform is designed to run across client clouds, on-premises, at the edge, and the NVIDIA Cloud Provider ecosystem.

• Focus on Productivity Gains: Initial deployments aim to integrate AI agents to enhance productivity and streamline complex compliance requirements in tax and risk.

• Responsible AI Framework: The platform incorporates EY Responsible AI (RAI) Frameworks and NVIDIA NeMo Guardrails for risk mitigation.

• Agentic Framework: It provides a framework for agent creation and orchestration, utilizing NVIDIA Blueprints.

OpenAI Launches Responses API and Agents SDK

Overview: OpenAI has introduced new tools and APIs, including the Responses API and Agents SDK, to streamline the development of AI agents that can perform tasks using multiple tools.

Key Points:

• Responses API: A new API combining the simplicity of Chat Completions with tool use capabilities for building agents.

• Built-in Tools: The API supports built-in tools like web search, file search, and computer use to enhance agent functionality.

• Agents SDK: An open-source SDK to orchestrate single-agent and multi-agent workflows.

• Integrated Observability: Tools for tracing and inspecting agent workflow execution are included.

• Streamlined Development: These tools aim to simplify the core logic, orchestration, and interactions involved in building AI agents.

Monica Launches Manus AI Agent

Overview: Chinese startup Monica has launched Manus, an AI agent described as a truly general AI agent capable of independent thinking, planning, and executing complex tasks.

Key Points:

• General AI Agent: Manus is reportedly designed to handle a wide range of tasks in both work and daily life.

• Benchmark Performance: The company claims Manus achieved state-of-the-art results on the GAIA benchmark for general AI assistants.

• Autonomous Operation: Examples showcase Manus's ability to independently think, plan, and execute tasks to deliver complete results.

• Multi-Step Task Handling: The agent is designed to manage and complete complex, multi-step tasks without constant human guidance.

• Limited Initial Access: Access to Manus is currently limited, requiring an invitation code.

💻 TOP TUTORIALS

1. Integrating AI Agents with Robotic Operating Systems (ROS)

Learn how to connect AI agents with ROS for enhanced robot intelligence.

Key Steps:

• Set up communication bridges between AI agent frameworks and ROS.

• Utilize ROS topics and services for data exchange.

• Implement AI agent nodes within the ROS architecture.

2. Developing Perception Capabilities for AI Agent-Controlled Robots

Detailed tutorials on enabling AI agents to process sensory data.

Key Steps:

• Integrate sensor drivers and data streams.

• Implement AI models for object detection, recognition, and scene understanding.

• Fuse multi-sensor data for a comprehensive environmental perception.

3. Implementing Planning and Control for Autonomous Robots with AI Agents

Practical guides on using AI agents for robot movement.

Key Steps:

• Utilize AI planning algorithms for high-level task decomposition.

• Implement control algorithms to translate plans into robot actions.

• Incorporate feedback mechanisms for real-time adjustments.

🎥 HOW TO

Build Multi-Agent AI Systems with CrewAI

Overview: This tutorial guides you through building AI agents using the CrewAI framework, which simplifies the creation of multi-agent systems where AI agents collaborate to achieve complex goals. It's designed for beginners and covers the fundamental concepts and practical implementation of CrewAI.

Steps:

1. Introduction to CrewAI:

   • Understand the core concepts of CrewAI and its advantages for building collaborative AI workflows.

   • Learn how CrewAI facilitates the definition of agent roles, tasks, and communication.

2. Project Setup:

   • Configure any necessary API keys or access credentials for the LLMs you intend to use.

3. Define Agents:

   • Learn how to create individual AI agents within CrewAI, specifying their:

     • Roles (e.g., "Researcher," "Writer," "Developer").

     • Goals and objectives.

     • Backstory and context.

     • LLM (Large Language Model) to use.

     • Tools they have access to.

4. Define Tasks:

   • Learn how to define the specific tasks that each agent will perform, including:

     • Task descriptions and instructions.

     • Expected outputs.

     • Agent assignment.

5. Orchestrate a Crew:

   • Learn how to create a "Crew" in CrewAI to manage the interaction and workflow between agents.

   • Define the sequence of tasks and how agents will collaborate to achieve the overall goal.

   • Explore CrewAI's features for:

     • Task delegation.

     • Information sharing between agents.

     • Workflow management.

6. Implement Tools:

   • Learn how to integrate tools into your AI agents, enabling them to:

     • Access external information (e.g., web search).

     • Perform specific actions (e.g., code execution).

     • Interact with APIs.

7. Test and Iterate:

   • Thoroughly test your CrewAI system to ensure it functions as expected.

   • Analyze the agent interactions and refine their roles, tasks, and workflows to optimize performance.

8. Advanced Concepts:

   • Explore more advanced CrewAI features, such as:

     • Memory management.

     • Agent communication protocols.

     • Error handling.

     • Parallel task execution.

Thanks for sticking around…

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