Oracle Introduces AI Agent Studio

Build Multi-Agent AI Systems with CrewAI

Author

Jahanzaib Ahmed
April 21, 2025

Hey,

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In Today’s Report:

🕒 Estimated Reading Time: 5 minutes 25 seconds

📌 Top News:

⚡️Trending AI Reports:

💻 Top Tutorials:

🛠️ How-to:

📰 BREAKING NEWS

Image source: CXO Insight Middle East

Overview

Oracle has announced Oracle AI Agent Studio for Fusion Applications, a platform designed to empower users to create, extend, deploy, and manage AI agents and agent teams within their enterprise applications.

Key Features:

  • AI Agent Studio: A comprehensive platform within Oracle Fusion Cloud Applications Suite for building and managing AI agents.

  • Customizable Agents: The platform provides tools for customers and partners to create customized AI agents tailored to their specific business needs.

  • Agent Team Orchestration: Oracle AI Agent Studio enables users to set up multiple agents to work together on complex tasks.

  • Agent Extensibility: Users can extend pre-packaged Oracle Fusion Applications AI agents to address specific industry and business requirements.

  • Simplified Agent Management: The platform offers easy-to-use tools for testing, validation, and security, streamlining the agent lifecycle.

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⚡️TRENDING AI REPORTS

Image source: AiThority

Overview: OneTrust has introduced an enhanced integration with Azure OpenAI to provide more robust AI agent governance, focusing on responsible AI practices and compliance.

Key Features:

  • AI Agent Governance: OneTrust's integration with Azure OpenAI enables organizations to centrally manage and govern AI agents alongside their AI models.

  • Automated Agent Registration: The integration automates the capture of agent deployments and project records, simplifying compliance.

  • AI Lifecycle Management: OneTrust enables the management of the full AI lifecycle, including associations between models, agents, and training data.

Overview: NTT DATA has launched its Smart AI Agent™, an advanced AI tool designed to accelerate Generative AI adoption by automating complex workflows and streamlining processes.

Key Points:

  • Automated Task Execution: The Smart AI Agent™ autonomously extracts, organizes, and executes tasks based on user instructions.

  • Multi-Agent Collaboration: The tool enables multiple AI agents to work together on workflows, enhancing efficiency and effectiveness.

  • Advanced RAG: It utilizes advanced Retrieval-Augmented Generation (RAG) to provide contextual searches of internal corporate data.

Overview: Synthesia, an AI video startup, is planning to release an AI agent that will allow customers to make AI videos interactive, expanding its focus beyond static video generation.

Key Points:

  • Interactive AI Videos: Synthesia's upcoming AI agent will enable users to create interactive video experiences.

  • Real-Time Video Interaction: The agent will allow for real-time engagement with AI videos, such as answering questions or providing feedback.

  • New Communication Paradigm: Synthesia envisions this as a shift towards more interactive and engaging video communication.

💻 TOP TUTORIALS

Image source: Codiste

This tutorial provides a comprehensive guide to building AI agents that can automate complex business processes and integrate with existing enterprise systems.

Key Steps:

  • Identifying suitable workflows for AI agent automation.

  • Designing agent architectures tailored to enterprise requirements.

  • Integrating AI agents with enterprise applications and databases.

  • Implementing security measures and access controls.

This tutorial offers detailed guidance on using AI agent orchestration platforms to manage and coordinate numerous agents within an enterprise.

Key Steps:

  • Setting up and configuring an AI agent orchestration platform.

  • Designing and deploying multi-agent workflows.

  • Monitoring agent performance and resource utilization.

  • Scaling agent deployments to meet enterprise demands.

This tutorial provides practical guides on tailoring AI agents to automate tasks within specific departments, such as customer service, finance, and HR.

Key Steps:

  • Analyzing specific departmental workflows and identifying automation opportunities.

  • Developing agent prompts and instructions tailored to departmental tasks.

  • Integrating agents with departmental applications and data sources.

  • Ensuring compliance with departmental policies and regulations.

🎥 HOW TO

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:

    • Set up your development environment with Python and install the CrewAI library.

      pip install crewai

    • 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…

That’s all for now—catch you next time!

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