Beyond Prompts: How Agentic AI is Redefining Human-AI Collaboration
The Shift from Reactive to Proactive AI As a passionate innovator in AI education, I’m on a mission to reimagine how we learn and build with AI—looking to craft intelligent agents that move beyond simple prompts to think, plan, and collaborate dynamically. Traditional AI systems rely heavily on prompt-based interactions—you ask a question, and the model responds. These systems are reactive, limited to single-turn tasks, and lack the ability to plan or adapt. This becomes a bottleneck in dynamic environments where tasks require multi-step reasoning, memory, and autonomy. Agentic AI changes the game. An agent is a structured system that uses a looped process to: Think – analyze inputs, reason about tasks, and plan actions. Act – choose and execute tools to complete tasks. Learn – optionally adapt based on feedback or outcomes. Unlike static workflows, agentic systems can: Make autonomous decisions Adapt to changing environments Collaborate with humans or other agents This shift enables AI to move from being a passive assistant to an active collaborator—capable of solving complex problems with minimal human intervention. What Is Agentic AI? Agentic AI refers to AI systems that go beyond static responses—they can reason, plan, act, and adapt autonomously. These agents operate in dynamic environments, making decisions and invoking tools to achieve goals with minimal human intervention. Some of the frameworks that can be used for Agentic AI include LangChain, Semantic Kernel, AutoGen, Crew AI, MetaGPT, etc. The frameworks can use Azure OpenAI, Anthropic Claude, Google Gemini, Mistral AI, Hugging Face Transformers, etc. Key Traits of Agentic AI Autonomy Agents can independently decide what actions to take based on context and goals. Unlike assistants, which support users, agents' complete tasks and drive outcomes. Memory Agents can retain both long-term and short-term context. This enables personalized and context-aware interactions across sessions. Planning Semantic Kernel agents use function calling to plan multi-step tasks. The AI can iteratively invoke functions, analyze results, and adjust its strategy—automating complex workflows. Adaptability Agents dynamically adjust their behavior based on user input, environmental changes, or feedback. This makes them suitable for real-world applications like task management, learning assistants, or research copilots. Frameworks That Enable Agentic AI Semantic Kernel: A flexible framework for building agents with skills, memory, and orchestration. Supports plugins, planning, and multi-agent collaboration. More information here: Semantic Kernel Agent Architecture. Azure AI Foundry: A managed platform for deploying secure, scalable agents with built-in governance and tool integration. More information here: Exploring the Semantic Kernel Azure AI Agent. LangGraph: A JavaScript-compatible SDK for building agentic apps with memory and tool-calling capabilities, ideal for web-based applications. More information here: Agentic app with LangGraph or Azure AI Foundry (Node.js) - Azure App Service. Copilot Studio: A low-code platform to build custom copilots and agentic workflows using generative AI, plugins, and orchestration. Ideal for enterprise-grade conversational agents. More information here: Building your own copilot with Copilot Studio. Microsoft 365 Copilot: Embeds agentic capabilities directly into productivity apps like Word, Excel, and Teams—enabling contextual, multi-step assistance across workflows. More information here: What is Microsoft 365 Copilot? Why It Matters: Real-World Impact Traditional Generative AI is like a calculator—you input a question, and it gives you an answer. It’s reactive, single-turn, and lacks context. While useful for quick tasks, it struggles with complexity, personalization, and continuity. Agentic AI, on the other hand, is like a smart teammate. It can: Understand goals Plan multi-step actions Remember past interactions Adapt to changing needs Generative AI vs. Agentic Systems Feature Generative AI Agentic AI Interaction Style One-shot responses Multi-turn, goal-driven Context Awareness Limited Persistent memory Task Execution Static Dynamic and autonomous Adaptability Low High (based on feedback/input) How Agentic AI Works — Agentic AI for Students Example Imagine a student named Alice preparing for her final exams. She uses a Smart Study Assistant powered by Agentic AI. Here's how the agent works behind the scenes: Skills / Functions These are the actions or the callable units of logic the agent can invoke to perform. The assistant has functions like: Summarize lecture notes Generate quiz questions Search academic papers Schedule study sessions Think of these as plug-and-play capabilities the agent can call when needed. Memory The agent remembers Alice’s: Past quiz scores Topics she struggled with Preferred study times This helps the assistant personalize recommendations and avoid repeating content she already knows. Planner Instead of doing everything at once, the agent: Breaks down Alice’s goal (“prepare for exams”) into steps Plans a week-by-week study schedule Decides which skills/functions to use at each stage It’s like having a tutor who builds a custom roadmap. Orchestrator This is the brain that coordinates everything. It decides when to use memory, which function to call, and how to adjust the plan if Alice misses a study session or scores low on a quiz. It ensures the agent behaves intelligently and adapts in real time. Conclusion Agentic AI marks a pivotal shift in how we interact with intelligent systems—from passive assistants to proactive collaborators. As we move beyond prompts, we unlock new possibilities for autonomy, adaptability, and human-AI synergy. Whether you're a developer, educator, or strategist, understanding agentic frameworks is no longer optional - it’s foundational. Here are the high-level steps to get started with Agentic AI using only official Microsoft resources, each with a direct link to the relevant documentation: Get Started with Agentic AI Understand Agentic AI Concepts - Begin by learning the fundamentals of AI agents, their architecture, and use cases. See: Explore the basics in this Microsoft Learn module Set Up Your Azure Environment - Create an Azure account and ensure you have the necessary roles (e.g., Azure AI Account Owner or Contributor). See: Quickstart guide for Azure AI Foundry Agent Service Create Your First Agent in Azure AI Foundry - Use the Foundry portal to create a project and deploy a default agent. Customize it with instructions and test it in the playground. See: Step-by-step agent creation in Azure AI Foundry Build an Agentic Web App with Semantic Kernel or Foundry - Follow a hands-on tutorial to integrate agentic capabilities into a .NET web app using Semantic Kernel or Azure AI Foundry. See: Tutorial: Build an agentic app with Semantic Kernel or Foundry Deploy and Test Your Agent - Use GitHub Codespaces or Azure Developer CLI to deploy your app and connect it to your agent. Validate functionality using OpenAPI tools and the agent playground. See: Deploy and test your agentic app For Further Learning: Develop generative AI apps with Azure OpenAI and Semantic Kernel Agentic app with Semantic Kernel or Azure AI Foundry (.NET) - Azure App Service AI Agent Orchestration Patterns - Azure Architecture Center Configuring Agents with Semantic Kernel Plugins Workflows with AI Agents and Models - Azure Logic Apps About the author: I'm Juliet Rajan, a Lead Technical Trainer and passionate innovator in AI education. I specialize in crafting gamified, visionary learning experiences and building intelligent agents that go beyond traditional prompt-based systems. My recent work explores agentic AI, autonomous copilots, and dynamic human-AI collaboration using platforms like Azure AI Foundry and Semantic Kernel.The Future of AI: Harnessing AI for E-commerce - personalized shopping agents
Explore the development of personalized shopping agents that enhance user experience by providing tailored product recommendations based on uploaded images. Leveraging Azure AI Foundry, these agents analyze images for apparel recognition and generate intelligent product recommendations, creating a seamless and intuitive shopping experience for retail customers.The Future of AI: Autonomous Agents for Identifying the Root Cause of Cloud Service Incidents
Discover how Microsoft is transforming cloud service incident management with autonomous AI agents. Learn how AI-enhanced troubleshooting guides and agentic workflows are reducing downtime and empowering on-call engineers.The Future of AI: Customizing AI agents with the Semantic Kernel agent framework
The blog post Customizing AI agents with the Semantic Kernel agent framework discusses the capabilities of the Semantic Kernel SDK, an open-source tool developed by Microsoft for creating AI agents and multi-agent systems. It highlights the benefits of using single-purpose agents within a multi-agent system to achieve more complex workflows with improved efficiency. The Semantic Kernel SDK offers features like telemetry, hooks, and filters to ensure secure and responsible AI solutions, making it a versatile tool for both simple and complex AI projects.POSTPONED: AMA: Azure AI Agent Service
POSTPONED: Please note this event has been postponed, but we do not have a new date or time in place yet. We will update this event when we do. Sorry about that! We will also be turning off the ability to comment, but will change that back once we have a new date and time. --- Join us for an AMA session on Azure AI Agent Service! This AMA is ideal for developers, technical and business decision makers looking to explore how Azure AI Agent Service can transform their operations. Azure AI Agent Service enables developers to create, deploy, and monitor autonomous AI agents that automate complex business processes while keeping humans at the center. With a rich ecosystem of the latest models, knowledge sources like Microsoft Bing and SharePoint, and over 1,400 action connectors with Azure Logic Apps, this flexible and use-case agnostic service can unlock new possibilities. Learn more: https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Faka.ms%2FAzureAI_agents_blog&data=05%7C02%7Cv-ersta%40microsoft.com%7C58d505548e5b4aa449e208dd14c7e307%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C638689570924412339%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=lQRav1M%2BjZKh1ut3uUhiNcTxEfjfxuzmjDLlr4z6%2B0Y%3D&reserved=0 Get your questions answered by our Subject Matter Experts and learn how to leverage this powerful service for your organization. An AMA is a live text-based online event similar to an “Ask Me Anything” on Reddit. This AMA gives you the opportunity to connect with Microsoft product experts who will be on hand to answer your questions and listen to feedback. The AMA takes place entirely in the comments below. There is no additional video or audio link as this is text-based. Feel free to post your questions anytime in the comments below beforehand, if it fits your schedule or time zone better, though questions will not be answered until the live hour.Announcing Model Fine-Tuning Collaborations: Weights & Biases, Scale AI, Gretel and Statsig
As AI continues to transform industries, the ability to fine-tune models and customize them for specific use cases has become more critical than ever. Fine-tuning can enable companies to align models with their unique business goals, ensuring that AI solutions deliver results with greater precision However, organizations face several hurdles in their model customization journey: Lack of end-to-end tooling: Organizations struggle with fine-tuning foundation models due to complex processes, and the absence of tracking and evaluation tools for modifications. Data scarcity and quality: Limited access to large, high-quality datasets, along with privacy issues and high costs, complicate model training and fine-tuning. Shortage of fine-tuning expertise and pre-trained models: Many companies lack specialized knowledge and access to refined models for fine-tuning. Insufficient experimentation tools: A lack of tools for ongoing experimentation in production limits optimization of key variables like model diversity and operational efficiency. To address these challenges, Azure AI Foundry is pleased to announce new collaborations with Weights & Biases, Scale AI, Gretel and Statsig to streamline the process of model fine-tuning and experimentation through advanced tools, synthetic data and specialized expertise. Weights & Biases integration with Azure OpenAI Service: Making end-to-end fine-tuning accessible with tooling The integration of Weights & Biases with Azure OpenAI Service offers a comprehensive end-to-end solution for enterprises aiming to fine-tune foundation models such as GPT-4, GPT-4o, and GPT-4o mini. This collaboration provides a seamless connection between Azure OpenAI Service and Weights and Biases Models which offers powerful capabilities for experiment tracking, visualization, model management, and collaboration. With the integration, users can also utilize Weights and Biases Weave to evaluate, monitor, and iterate on the performance of their fine-tuned models powered AI applications in real-time. Azure's scalable infrastructure allows organizations to handle the computational demands of fine-tuning, while Weights and Biases offers robust capabilities for fine-tuning experimentation and evaluation of LLM-powered applications. Whether optimizing GPT-4o for complex reasoning tasks or using the lightweight GPT-4o mini for real-time applications, the integration simplifies the customization of models to meet enterprise-specific needs. This collaboration addresses the growing demand for tailored AI models in industries such as retail and finance, where fine-tuning can significantly improve customer service chatbots or complex financial analysis. Azure Open AI Service and Weights & Biases integration is now available in public preview. For further details on Azure OpenAI Service and Weights & Biases integration including real-world use-cases and a demo, refer to the blog here. Scale AI and Azure Collaboration: Confidently Implement Agentic GenAI Solutions in Production Scale AI collaborates with Azure AI Foundry to offer advanced fine-tuning and model customization for enterprise use cases. It enhances the performance of Azure AI Foundry models by providing high-quality data transformation, fine-tuning and customization services, end-to-end solution development and specialized Generative AI expertise. This collaboration helps improve the performance of AI-driven applications and Azure AI services such as Azure AI Agent in Azure AI Foundry, while reducing production time and driving business impact. "Scale is excited to partner with Azure to help our customers transform their proprietary data into real business value with end-to-end GenAI Solutions, including model fine-tuning and customization in Azure." Vijay Karunamurthy, Field CTO, Scale AI Checkout a demo in BRK116 session showcasing how Scale AI’s fine-tuned models can improve agents in Azure AI Foundry and Copilot Studio. In the coming months, Scale AI will offer fine-tuning services for Azure AI Agents in Azure AI Foundry. For more details, please refer to this blog and start transforming your AI initiatives by exploring Scale AI on the Azure Marketplace. Gretel and Azure OpenAI Service Collaboration: Revolutionizing data pipeline for custom AI models Azure AI Foundry is collaborating with Gretel, a pioneer in synthetic data and privacy technology, to remove data bottlenecks and bring advanced AI development capabilities to our customers. Gretel's platform enables Azure users to generate high-quality datasets for ML and AI through multiple approaches - from prompts and seed examples to differential privacy-preserved synthetic data. This technology helps organizations overcome key challenges in AI development including data availability, privacy requirements, and high development costs with support for structured, unstructured, and hybrid text data formats. Through this collaboration, customers can seamlessly generate datasets tailored to their specific use cases and industry needs using Gretel, then use them directly in Azure OpenAI Service for fine-tuning. This integration greatly reduces both costs and time compared to traditional data labeling methods, while maintaining strong privacy and compliance standards. The collaboration enables new use cases for Azure AI Foundry customers who can now easily use synthetic data generated by Gretel for training and fine-tuning models. Some of the new use cases include cost-effective improvements for Small Language Models (SLMs), improved reasoning abilities of Large Language Models (LLMs), and scalable data generation from limited real-world examples. This value is already being realized by leading enterprises. “EY is leveraging the privacy-protected synthetic data to fine-tune Azure OpenAI Service models in the financial domain," said John Thompson, Global Client Technology AI Lead at EY. "Using this technology with differential privacy guarantees, we generate highly accurate synthetic datasets—within 1% of real data accuracy—that safeguard sensitive financial information and prevent PII exposure. This approach ensures model safety through privacy attack simulations and robust data quality reporting. With this integration, we can safely fine-tune models for our specific financial use cases while upholding the highest compliance and regulatory standards.” The Gretel integration with Azure OpenAI Service is available now through Gretel SDK. Explore this blog describing a finance industry case study and checkout details in technical documentation for fine-tuning Azure OpenAI Service models with synthetic data from Gretel. Visit this page to learn more Statsig and Azure Collaboration: Enabling Experimentation in AI Applications Statsig is a platform for feature management and experimentation that helps teams manage releases, run powerful experiments, and measure the performance of their products. Statsig and Azure AI Foundry are collaborating to enable customers to easily configure and run experiments (A/B tests) in Azure AI-powered applications, using Statsig SDKs in Python, NodeJS and .NET. With these Statsig SDKs, customers can manage the configuration of their AI applications, manage the release of new configurations, run A/B tests to optimize model and application performance, and automatically collect metrics at the model and application level. Please check out this page to learn more about the collaboration and get detailed documentation here. Conclusion The new collaborations between Azure and Weights & Biases, Scale AI, Gretel and Statsig represent a significant step forward in simplifying the process of AI model customization. These collaborations aim to address the common pain points associated with fine-tuning models, including lack of end-to-end tooling, data scarcity and privacy concerns, lack of expertise and experimentation tooling. Through these collaborations, Azure AI Foundry will empower organizations to fine-tune and customize models more efficiently, ultimately enabling faster, more accurate AI deployments. Whether it’s through better model tracking, access to synthetic data, or scalable data preparation services, these collaborations will help businesses unlock the full potential of AI.Build recap: new Azure AI Foundry resource, Developer APIs and Tools
At Microsoft Build 2025, we introduced Azure AI Foundry resource, Azure AI Foundry API, and supporting tools to streamline the end-to-end development lifecycle of AI agents and applications. These capabilities are designed to help developers accelerate time-to-market; support production-scale workloads with scale and central governance; and support administrators with a self-serve capability to enable their teams’ experimentation with AI in a controlled environment. The Azure AI Foundry resource type unifies agents, models and tools under a single management grouping, equipped with built-in enterprise-readiness capabilities — such as tracing & monitoring, agent and model-specific evaluation capabilities, and customizable enterprise setup configurations tailored to your organizational policies like using your own virtual networks. This launch represents our commitment to providing organizations with a consistent, efficient and centrally governable environment for building and operating the AI agents and applications of today, and tomorrow. New platform capabilities The new Foundry resource type evolves our vision for Azure AI Foundry as a unified Azure platform-as-a-service offering, enabling developers to focus on building applications rather than managing infrastructure, while taking advantage of native Azure platform capabilities like Azure Data and Microsoft Defender. Previously, Azure AI Foundry portal’s capabilities required the management of multiple Azure resources and SDKs to build an end-to-end application. New capabilities include: Foundry resource type enables administrators with a consistent way of managing security and access to Agents, Models, Projects, and Azure tooling Integration. With this change, Azure Role Based Access Control, Networking and Policies are administered under a single Azure resource provider namespace, for streamlined management. ‘Azure AI Foundry’ is a renaming of the former ‘Azure AI Services’ resource type, with access to new capabilities. While Azure AI Foundry still supports bring-your-own Azure resources, we now default to a fully Microsoft-managed experience, making it faster and easier to get started. Foundry projects are folders that enable developers to independently create new environments for exploring new ideas and building prototypes, while managing data in isolation. Projects are child resources; they may be assigned their own admin controls but by default share common settings such as networking or connected resource access from their parent resource. This principle aims to take IT admins out of the day-to-day loop once security and governance are established at the resource level, enabling developers to self-serve confidently within their projects. Azure AI Foundry API is designed from the ground up, to build and evaluate API-first agentic applications, and lets you work across model providers agnostically with a consistent contract. Azure AI Foundry SDK wraps the Foundry API making it easy to integrate capabilities into code whether your application is built in Python, C#, JavaScript/TypeScript or Java. Azure AI Foundry for VS Code Extension complements your workflow with capabilities to help you explore models, and develop agents and is now supported with the new Foundry project type. New built-in RBAC roles provide up-to-date role definitions to help admins differentiate access between Administrator, Project Manager and Project users. Foundry RBAC actions follow strict control- and data plane separation, making it easier to implement the principle of least privilege. Why we built these new platform capabilities If you are already building with Azure AI Foundry -- these capabilities are meant to simplify platform management, enhance workflows that span multiple models and tools, and reinforce governance capabilities, as we see AI workloads grow more complex. The emergence of generative AI fundamentally changed how customers build AI solutions, requiring capabilities that span multiple traditional domains. We launched Azure AI Foundry to provide a comprehensive toolkit for exploring, building and evaluating this new wave of GenAI solutions. Initially, this experience was backed by two core Azure services -- Azure AI Services for accessing models including those from OpenAI, and Azure Machine Learning’s hub, to access tools for orchestration and customization. With the emergence of AI agents composing models and tools; and production workloads demanding the enforcement of central governance across those, we are investing to bring the management of agents, models and their tooling integration layer together to best serve these workload’s requirements. The Azure AI Foundry resource and Foundry API are purposefully designed to unify and simplify the composition and management of core building blocks of AI applications: Models Agents & their tools Observability, Security, and Trust In this new era of AI, there is no one-size-fits-all approach to building AI agents and applications. That's why we designed the new platform as a comprehensive AI factory with modular, extensible, and interoperable components. Foundry Project vs Hub-Based Project Going forward, new agents and model-centric capabilities will only land on the new Foundry project type. This includes access to Foundry Agent Service in GA and Foundry API. While we are transitioning to Azure AI Foundry as a managed platform service, hub-based project type remains accessible in Azure AI Foundry portal for GenAI capabilities that are not yet supported by the new resource type. Hub-based projects will continue to support use cases for custom model training in Azure Machine Learning Studio, CLI and SDK. For a full overview of capabilities supported by each project type, see this support matrix. Azure AI Foundry Agent Service The Azure AI Foundry Agent Service experience, now generally available, is powered by the new Foundry project. Existing customers exploring the GA experience will need the new AI Foundry resource. All new investments in the Azure AI Foundry Agent Service are focused on the Foundry project experience. Foundry projects act as secure units of isolation and collaboration — agents within a project share: File storage Thread storage (i.e. conversation history) Search indexes You can also bring your own Azure resources (e.g., storage, bring-your-own virtual network) to support compliance and control over sensitive data. Start Building with Foundry Azure AI Foundry is your foundation for scalable, secure, and production-grade AI development. Whether you're building your first agent or deploying a multi-agent workforce at Scale, Azure AI Foundry is ready for what’s next.The Future of AI: Harnessing AI agents for Customer Engagements
Discover how AI-powered agents are revolutionizing customer engagement—enhancing real-time support, automating workflows, and empowering human professionals with intelligent orchestration. Explore the future of AI-driven service, including Customer Assist created with Azure AI Foundry.
The Future of AI: Developing Code Assist – a Multi-Agent Tool
Discover how Code Assist, created with Azure AI Foundry Agent Service, uses AI agents to automate code documentation, generate business-ready slides, and detect security risks in large codebases—boosting developer productivity and project clarity.
