Latest news with #multiagent
Forbes
22-05-2025
- Business
- Forbes
Stanford's Use Of Microsoft Agentic Platform Leads To Better Analysis
stethoscope, black, blue background, isolated getty In the era of genetic AI, we're spending a lot of time looking at connective technologies. That means developing processes and pipelines that will connect the dots, and keep things moving in end-to-end deployment of multiple AI agents collaborating on a given project. When you start to research why agentic AI is such a big deal, you realize that it has to do with what they used to call 'ensemble learning' in traditional ML. It's the idea that instead of one big neural network 'brain,' there are multiple smaller AIs, and each one has a job to do in providing an overall result. One of the simplest versions is a generative adversarial system, where some AIs generate things, and other AIs analyze them. But today these systems are a lot more sophisticated, and the support technologies and tools have to be sophisticated, too. Microsoft is pioneering this sort of work with Azure AI Foundry, which is just now becoming part of the nomenclature of today's AI world. The essential idea of Azure AI Foundry is that it combines three key pieces of the pipeline for multi-agent collaboration. That's a code environment, a collaboration environment, and a cloud service for hosting. In Microsoft's case, that is provided by the connection between Microsoft Visual Studio (an IDE), GitHub as a collaboration center, and Microsoft Azure for cloud. This setup allows over 70,000 customers to process 100 trillion tokens and generate billions of daily search queries, according to Microsoft's stats. Microsoft shows how features like agentic retrieval, always-on observability and trust features help make this platform what it is. One of the biggest feathers in Microsoft's cap here is its collaboration with Stanford in developing agent orchestration for tumor management. This agent orchestration system helps clinicians to evaluate medical imaging, seek out clinical trials, and build personal timelines for patients. When you read about the project, you learn that only one percent of patients currently have these personalized plans, which are known to promote better results in oncology. Reporting on Stanford's use of the Azure AI Foundry agent system shows how it takes many hours of clinical work out of the equation, and automates the process of using electronic health record data to save lives. Developers can also fine-tune models and test them, while targeting agents to their tasks. Hyperliterate users can find out more by looking in the Azure AI Foundry Agent Catalog here. With this process, clinical tumor review boards save time identifying a patient's situation and coming up with actionable results. 'Stanford Medicine sees 4,000 tumor board patients a year, and our clinicians are already using foundation model generated summaries in tumor board meetings today,' says Stanford School of Medicine Chief Information Officer Dr. Mike Pfeffer. 'The new healthcare agent orchestrator has the power to streamline this existing workflow by reducing fragmentation … and enables surfacing new insights from data elements that were challenging to search, such as trial eligibility criteria, treatment guidelines, and real-world evidence. Stanford Health Care is excited to further research the potential of using the healthcare agent orchestrator to build the first generative AI agent solution used in a production setting for real-world care for our cancer patients.' All of this takes place in a specific context, this month of May, where both Microsoft and Google are coming out with big announcements this week. In addition to Google's new AI Mode in search, which I've covered, Google is also touting its Gemini 2.5 model advancement. As for Microsoft, there's news on DeepSeek for Copilot and more from the Microsoft Build 2025 event this month, and there's additional agentic news there as well. But the Azure AI Foundry agent system is really a signpost on the path toward better agentic deployment, which is likely to be a primary focus of 2025, 2026, etc. Look for more as we move into summer and these enormous companies continue to wow the crowds with breakthrough innovations.
Globe and Mail
20-05-2025
- Business
- Globe and Mail
Simplenight Leverages Multiagent and Multimodal Large Language Models to Elevate Global Commerce
Simplenight is an AI-powered platform that integrates multi-agent and multimodal AI to create seamless, hyper-personalized solutions across industries, including government, finance, travel, and more. Simplenight has announced the integration of cutting-edge multi-agent and multimodal large language models into its AI-powered digital platform in a bid to transform global commerce. The company's goal is to deliver unprecedented connectivity across sectors like government, finance, travel, automotive, and healthcare. A Simplenight representative noted that this technological advancement enhances the company's mission of unifying industries into a single, dynamic digital ecosystem. The team explained that by leveraging the collaborative intelligence of multi-agent systems and the rich understanding of multimodal AI, Simplenight's platform drives deeper personalization, operational optimization, and smarter decision-making for over 500 million users worldwide. Through natural language processing, visual recognition, and contextual data analysis, the platform offers an immersive and seamless experience across devices and channels. 'Simplenight was built to transform how industries interact and how people experience services globally,' said Mark Halberstein, CEO of Simplenight. 'With the addition of multiagent and multimodal LLMs, we're not just evolving — we're redefining the very fabric of global commerce and service delivery.' Simplenight 's expansive supplier network now taps into over 10 million bookable products and services across 5,000 cities in 191 countries. From accommodations and transportation to healthcare services and entertainment, users can now access a truly integrated global marketplace. Multi-agent collaboration enhances service curation, while multimodal AI ensures a highly intuitive user interface capable of understanding voice commands, images, and text. In the travel and tourism domain, the new platform enables hyper-personalized itinerary creation, which offers curated travel experiences based on preferences, environmental conditions, and even user emotions detected through multimodal analysis. Users can book flights, hotels, dining, and excursions effortlessly through a unified, AI-enhanced journey planner. Financial institutions are embedding Simplenight's multi-agent capabilities into mobile apps, allowing for smart portfolio management, lifestyle service recommendations, and contextual commerce opportunities. Meanwhile, automotive companies integrate the platform into connected vehicle systems to offer drivers and passengers proactive, AI-driven service suggestions in real-time. Government agencies are also adopting Simplenight's platform to streamline resident services, foster tourism, and enhance public engagement through intelligent, personalized citizen interactions, all while adhering to strict global privacy and ethical AI standards. Gary Fowler, advisor to Simplenight and CEO of GSD Venture Studios, emphasized the game-changing nature of the update, saying, 'Simplenight's embrace of multi-agent and multimodal LLMs is setting a new benchmark. They're creating not just a smarter commerce platform, but a more human-centered digital world.' In addition to transforming commerce and service delivery, Simplenight's AI-driven predictive analytics empower businesses and institutions to make faster, more informed decisions. This ensures adaptive responses to market changes, user needs, and emerging opportunities, maintaining Simplenight's leadership in the rapidly evolving landscape of AI-integrated commerce. The Simplenight technology is built to make its offerings customized, bookable, and personalized through cloud-based distribution and dynamic packaging and merchandising. The company customizes its API and white-label booking solution for every client so they can power their own interface via the universal API. When it comes to booking, Simplenight fully handles the process and considers it a vital component of the entire package to make booking as easy as possible for potential customers. Finally, the team stressed the importance of personalized recommendations. "We see a holistic picture of the customers' interests and historical purchasing behavior. That's why we offer partners supreme data insights to drive loyalty and customer lifetime value," the company representative stated. The team noted that many Simplenight suppliers were happy to report increased exposure and revenue. Many have praised Simplenight for its multilingual and multicurrency approach, which the company described as an obvious approach for a truly customizable global platform. Suppliers can optimize their operations through a host of performance tracking features, including intuitive reports on traveler booking trends, payment status, and transactional history. More information about Simplenight, its approach, and key features can all be found on the company's official website. Media Contact Company Name: Contact Person: Gary A. Fowler Email: Send Email Country: United States Website:
Geeky Gadgets
09-05-2025
- Geeky Gadgets
How OpenAI's Agents SDK is Changing Task Management : Multi-Agent Systems Guide
What if you could design a system where multiple specialized agents work together seamlessly, each tackling a specific task with precision and efficiency? This isn't just a futuristic vision—it's the core promise of multi-agent systems powered by OpenAI's Agents SDK. Imagine an orchestrator delegating tasks like web searches, document retrieval, or even secure code execution to a network of sub-agents, each optimized for its role. This modular approach doesn't just streamline workflows; it transforms how we think about automation, allowing scalable, adaptable systems that can evolve alongside your needs. Whether you're a developer exploring innovative AI tools or a team leader seeking smarter task management, the possibilities are both exciting and practical. In this comprehensive tutorial, James Briggs explains how to set up and optimize a multi-agent system using OpenAI's Agents SDK. From understanding the orchestrator-sub-agent architecture to crafting precise prompts and integrating specialized tools, this guide walks you through every step. You'll learn how to design workflows that balance complexity with performance, debug systems effectively, and harness the SDK's advanced features to build reliable, scalable solutions. By the end, you'll not only grasp the technical mechanics but also gain insights into how these systems can transform your approach to automation. So, how do you create a system where collaboration between agents feels almost effortless? Let's explore. Building Multi-Agent Workflows Understanding Multi-Agent Systems in OpenAI's Agents SDK OpenAI's Agents SDK is a versatile tool for creating multi-agent systems, building on earlier frameworks like the Swarm package. At its core, the SDK enables you to design systems where an orchestrator coordinates multiple sub-agents, each specializing in a specific task. This orchestrator-sub-agent architecture ensures efficient task management and is particularly suited for workflows requiring diverse functionalities. The orchestrator acts as the central controller, delegating tasks to sub-agents based on their specific capabilities. This modular approach not only enhances scalability but also allows for seamless integration of new functionalities as your workflow evolves. By using this architecture, you can create systems that are both flexible and efficient. The Role and Functionality of Sub-Agents Sub-agents are the building blocks of multi-agent systems, each designed to handle a specific task. Their modularity ensures that the system remains efficient and adaptable to changing requirements. Below are three common types of sub-agents and their roles: Web Search Sub-Agent: This sub-agent integrates with web search APIs, such as LinkUp, to retrieve and summarize information. By using asynchronous programming, it can handle multiple API calls simultaneously, reducing latency and improving response times. This sub-agent integrates with web search APIs, such as LinkUp, to retrieve and summarize information. By using asynchronous programming, it can handle multiple API calls simultaneously, reducing latency and improving response times. Internal Docs Sub-Agent: Acting as a retrieval-augmented generation (RAG) tool, this sub-agent processes internal documents to answer queries. It ensures secure and efficient access to private data, making it ideal for sensitive information retrieval. Acting as a retrieval-augmented generation (RAG) tool, this sub-agent processes internal documents to answer queries. It ensures secure and efficient access to private data, making it ideal for sensitive information retrieval. Code Execution Sub-Agent: Designed for tasks requiring mathematical or logical operations, this sub-agent uses secure code execution tools. It emphasizes accuracy and security, particularly for operations involving sensitive data. Each sub-agent operates independently but communicates with the orchestrator to ensure smooth task execution. This separation of responsibilities allows for better error handling and easier debugging, as issues can be isolated to specific sub-agents. Multi-Agent Systems in OpenAI's Agents SDK Watch this video on YouTube. Uncover more insights about multi-agent systems in previous articles we have written. Setting Up and Optimizing the Orchestrator The orchestrator is the central component of a multi-agent system, responsible for managing communication between the user and sub-agents. Its primary role is to route queries to the appropriate sub-agent, making sure tasks are executed efficiently. To set up an effective orchestrator: Convert sub-agents into callable tools: Ensure that each sub-agent is accessible to the orchestrator as a distinct tool, simplifying task delegation. Ensure that each sub-agent is accessible to the orchestrator as a distinct tool, simplifying task delegation. Craft precise prompts: Develop clear and specific prompts to guide the orchestrator's behavior. This ensures it understands user intent and delegates tasks effectively. Develop clear and specific prompts to guide the orchestrator's behavior. This ensures it understands user intent and delegates tasks effectively. Integrate sub-agents into a unified workflow: Establish seamless communication between the orchestrator and sub-agents to enable efficient collaboration. Optimization is key to making sure the orchestrator performs reliably. OpenAI provides tracing tools to monitor workflows, identify bottlenecks, and resolve issues. By refining prompts and optimizing sub-agent behaviors, you can enhance the overall performance of your system. Debugging and Performance Enhancement Building a reliable multi-agent system requires continuous debugging and performance optimization. OpenAI's tracing tools are invaluable for monitoring workflows and identifying areas for improvement. Here are some strategies to enhance system performance: Refine orchestrator prompts: Clear and concise prompts improve the orchestrator's ability to understand and delegate tasks. Clear and concise prompts improve the orchestrator's ability to understand and delegate tasks. Optimize sub-agent operations: For instance, reduce latency in asynchronous calls for the web search sub-agent to improve response times. For instance, reduce latency in asynchronous calls for the web search sub-agent to improve response times. Test workflows regularly: Simulate various scenarios to identify potential issues and refine system behavior. By adopting these strategies, you can ensure your multi-agent system operates efficiently and delivers accurate results. Balancing Complexity and Performance Designing multi-agent systems involves balancing functionality with performance. While the orchestrator-sub-agent pattern is ideal for managing complex workflows, it can introduce latency due to the coordination of multiple sub-agents. For simpler tasks, a single-agent approach may be more efficient. Understanding these trade-offs is crucial for selecting the right architecture for your specific use case. Practical demonstrations can help validate your system's design. For example, simulate a multi-step workflow where the orchestrator delegates a web search task to one sub-agent and a document retrieval task to another. Analyze the system's responses to identify areas for improvement and ensure accurate, efficient outputs. Key Insights for Effective Multi-Agent Systems The orchestrator-sub-agent pattern is highly effective for managing workflows involving multiple specialized tasks. Clear prompting and seamless tool integration are essential for optimal system performance. Regular debugging and performance optimization are critical for building reliable, efficient systems. By following these best practices, you can use OpenAI's Agents SDK to create flexible, scalable workflows that handle diverse tasks with precision and efficiency. Whether managing web searches, processing internal documents, or executing secure code, these strategies will help you design systems that meet your needs effectively. Media Credit: James Briggs Filed Under: AI, Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
