Google Cloud expands AI agent tools for healthcare

Build AI Agents in Pure Python

Author

Jahanzaib Ahmed
April 14, 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: Search Engine Journal

Overview

Google Cloud is making significant strides in applying AI agent technology to the healthcare sector. The company's recent announcements focus on providing tools and frameworks to enable the development and deployment of AI agents within healthcare organizations.

Key Features:

  1. Agent Garden: A centralized hub providing access to pre-built AI agents for healthcare, as well as tools for organizations to create their own.

  2. Agent2Agent Protocol (A2A): A framework that allows different AI agents to communicate and collaborate, regardless of the underlying technology stack.

  3. Workflow Automation: AI agents are being developed to automate complex healthcare workflows, reducing administrative burden on healthcare professionals.

  4. Data-Driven Insights: Agents can analyze healthcare data to provide insights for improved decision-making and patient care.

  5. Focus on Interoperability: Google Cloud's initiatives emphasize the importance of agent interoperability within the healthcare ecosystem.

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⚡️TRENDING AI REPORTS

Image source: Forbes

Overview: Adobe is actively developing agentic AI capabilities to enhance creative and productivity workflows within its suite of applications. The company envisions AI agents as assistants that can guide users and automate tasks within applications like Photoshop, Acrobat, and Express.

Key Features:

  • Creative Agents in Photoshop: The development of AI agents to analyze images and recommend smart, context-aware edits.

  • Productivity Agents in Acrobat: Enabling the creation of custom agents to assist with document analysis, question answering, and information exploration.

  • Express Integration: Incorporating agentic AI to guide users in content creation and achieve better creative outcomes.

Overview: A key development in the AI agent field is the push for interoperability. The Agent2Agent (A2A) protocol is an open standard designed to enable AI agents to communicate and collaborate effectively, even if they were built using different technologies.

Key Points:

  • Interoperability: A2A aims to break down silos and allow agents to work together seamlessly.

  • Collaboration: The protocol facilitates the coordination of actions and the exchange of information between agents.

  • Standardization: A2A provides a standardized framework for agent communication, promoting consistency.

Overview: OpenAI is reportedly developing an AI agent called "A-SWE" (Agentic Software Engineer) with the goal of automating various software engineering tasks. This agent has the potential to significantly impact the software development process.

Key Points:

  • Automated Development: A-SWE is designed to automate code writing and application development.

  • Quality Assurance: The agent is intended to perform tasks like code quality assurance, bug testing, and bug fixing.

  • Documentation: A-SWE may also automate the generation of software documentation.

💻 TOP TUTORIALS

Image source: Level Up Coding - gitconnected

This tutorial provides a comprehensive guide to building AI agents that can effectively utilize a wide range of tools and external systems.

Key Steps:

  • Setting up agent frameworks that support tool use.

  • Defining and integrating various types of tools (APIs, databases, etc.).

  • Implementing mechanisms for agents to select and execute tools.

This tutorial focuses on designing safe, secure, and reliable tool-using mechanisms for AI agents, emphasizing error handling and security best practices.

Key Steps:

  • Implementing safety protocols for tool execution.

  • Securing agent interactions with external systems.

  • Developing robust error handling and recovery mechanisms.

This tutorial offers step-by-step guides on building AI agents that can plan and execute complex workflows involving multiple tools and dependencies.

Key Steps:

  • Designing workflow planning and execution algorithms.

  • Managing tool dependencies and resource allocation.

  • Implementing dynamic adaptation and feedback mechanisms.

🎥 HOW TO

Overview: This tutorial guides you through the process of building AI agents using pure Python, emphasizing a foundational understanding of AI agent development. It focuses on direct interaction with LLM APIs and avoids reliance on complex frameworks, providing a clear understanding of the underlying mechanisms.

Steps:

  1. Understand AI Agent Fundamentals:

    • Learn the core concepts of AI agents, including their interaction with environments, decision-making processes, and goal-oriented behavior.

    • Focus on the basic building blocks of an AI agent, such as perception, reasoning, and action.

  2. Interact Directly with LLM APIs:

    • Learn how to make API calls to LLMs (e.g., OpenAI API) using Python's requests library or a similar tool.

    • Understand the structure of API requests and responses, including authentication, headers, and data formats.

  3. Work with Prompts Effectively:

    • Master the art of prompt engineering to elicit desired responses from LLMs.

    • Learn how to design prompts that are clear, concise, and contextually relevant.

    • Explore techniques for prompt chaining and prompt composition to create more complex agent behavior.

  4. Structure Output for Reliability:

    • Implement methods to structure the output from LLMs into a predictable format (e.g., JSON).

    • Learn how to use libraries or techniques to parse and validate structured output.

    • Understand the importance of structured output for reliable processing and integration with other systems.

  5. Utilize Tools and Functions:

    • Learn how to define and integrate external tools or functions that the AI agent can use to perform specific tasks.

    • Explore techniques for tool selection and orchestration, enabling the agent to choose the appropriate tool for a given situation.

    • Understand how to manage tool dependencies and handle tool execution errors.

  6. Implement Workflow Patterns:

    • Explore common workflow patterns for building AI agents, such as:

      • Prompt chaining to create multi-step processes.

      • Conditional logic to handle different scenarios.

      • Looping to repeat actions.

    • Learn how to combine these patterns to create more sophisticated and reliable agent behavior.

  7. Address Error Handling and Robustness:

    • Implement error handling mechanisms to gracefully manage unexpected LLM responses or API failures.

    • Design robust agent behavior that can adapt to noisy or incomplete input.

    • Explore techniques for testing and debugging AI agents.

Thanks for sticking around…

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

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