AI Agents: Key Principles and Guidelines - Part 3
This blog post, the third in a series on AI agents, focuses on user-centric design principles for building effective and trustworthy agentic systems. Drawing from the "Agentic Design Patterns" section of Microsoft's "AI Agents for Beginners" GitHub repository, the post outlines key principles categorized by Agent (Space), Agent (Time), and Agent (Core). These principles emphasize connection, accessibility, leveraging historical context, adapting to future needs, and establishing trust through transparency and control. Practical implementation guidelines are provided, along with a travel agent example to illustrate how these principles can be applied in real-world scenarios. The post also links to additional resources and previous installments in the series for a comprehensive learning experience.GPT-5 Family of Models & GPT OSS Are Now Available in AI Toolkit for VS Code
AI Toolkit v0.18.3 is here—and it’s a major milestone. This release introduces full support for: The latest GPT-5 family of models OpenAI’s open-source models (GPT OSS) via Azure AI Foundry and ONNX Runtime Whether you're building in the cloud, on the edge, or experimenting locally, this update gives you more flexibility, performance, and choice than ever.
Orchestrate multimodal AI insights within your healthcare data estate (Public Preview)
In today’s healthcare landscape, there is an increasing emphasis on leveraging artificial intelligence (AI) to extract meaningful insights from diverse datasets to improve patient care and drive clinical research. However, incorporating AI into your healthcare data estate often brings significant costs and challenges, especially when dealing with siloed and unstructured data. Healthcare organizations produce and consume data that is not only vast but also varied in format—ranging from structured EHR entries to unstructured clinical notes and imaging data. Traditional methods require manual effort to prepare and harmonize this data for AI, specify the AI output format, set up API calls, store the AI outputs, integrate the AI outputs, and analyze the AI outputs for each AI model or service you decide to use. Orchestrate multimodal AI insights is designed to streamline and scale healthcare AI within your data estate by building off of the data transformations in healthcare data solutions in Microsoft Fabric. This capability provides a framework to generate AI insights by connecting your multimodal healthcare data to an ecosystem of AI services and models and integrating structured AI-generated insights back into your data estate. When you combine these AI-generated insights with the existing healthcare data in your data estate, you can power advanced analytics scenarios for your organization and patient population. Key features: Metadata store lakehouse acts as a central repository for the metadata for AI orchestration to effectively capture and manage enrichment definitions, view definitions, and contextual information for traceability purposes. Execution notebooks define the enrichment view and enrichment definition based on the model configuration and input mappings. They also specify the model processor and transformer. The model processor calls the model API, and the transformer produces the standardized output while saving the output in the bronze lakehouse in the Ingest folder. Transformation pipeline to ingest AI-generated insights through the healthcare data solutions medallion lakehouse layers and persist the insights in an enrichment store within the silver layer. Conceptual architecture: The data transformations in healthcare data solutions in Microsoft Fabric allow you ingest, store, and analyze multimodal data. With the orchestrate multimodal AI insights capability, this standardized data serves as the input for healthcare AI models. The model results are stored in a standardized format and provide new insights from your data. The diagram below shows the flow of integrating AI generated insights into the data estate, starting as raw data in the bronze lakehouse and being transformed to delta tables in the silver lakehouse. This capability simplifies AI integration across modalities for data-driven research and care, currently supporting: Text Analytics for health in Azure AI Language to extract medical entities such as conditions and medications from unstructured clinical notes. This utilizes the data in the DocumentReference FHIR resource. MedImageInsight healthcare AI model in Azure AI Foundry to generate medical image embeddings from imaging data. This model leverages the data in the ImagingStudy FHIR resource. MedImageParse healthcare AI model in Azure AI Foundry to enable segmentation, detection, and recognition from imaging data across numerous object types and imaging modalities. This model uses the data in the ImagingStudy FHIR resource. By using orchestrate multimodal AI insights to leverage the data in healthcare data solutions for these models and integrate the results into the data estate, you can analyze your existing data alongside AI enrichments. This allows you to explore use cases such as creating image segmentations and combining with your existing imaging metadata and clinical data to enable quick insights and disease progression trends for clinical research at the patient level. Get started today! This capability is now available in public preview, and you can use the in-product sample data to test this feature with any of the three models listed above. For more information and to learn how to deploy the capability, please refer to the product documentation. We will dive deeper into more detailed aspects of the capability, such as the enrichment store and custom AI use cases, in upcoming blogs. Medical device disclaimer: Microsoft products and services (1) are not designed, intended or made available as a medical device, and (2) are not designed or intended to be a substitute for professional medical advice, diagnosis, treatment, or judgment and should not be used to replace or as a substitute for professional medical advice, diagnosis, treatment, or judgment. Customers/partners are responsible for ensuring solutions comply with applicable laws and regulations. FHIR® is the registered trademark of HL7 and is used with permission of HL7.Unleashing the Power of AI Agents: Transforming Business Operations
Let "Get Started with AI Agents," in this short blog I want explore the evolution, capabilities, and applications of AI agents, highlighting their potential to enhance productivity and efficiency. We take a peak into the challenges of developing AI agents and introduce powerful tools like Azure AI Foundry and Azure AI Agent Service that empower developers to build, deploy, and scale AI agents securely and efficiently. In today's rapidly evolving technological landscape, the integration of AI agents into business processes is becoming increasingly essential. Lets delve into the transformative potential of AI agents and how they can revolutionize various aspects of our operations. We begin by exploring the evolution of LLM-based solutions, tracing the journey from no agents to sophisticated multi-agent systems. This progression highlights the growing complexity and capabilities of AI agents, which are now poised to handle wide-scope, complex use cases requiring diverse skills. Lets now look at agentic AI capabilities. AI agents can significantly enhance employee productivity and process efficiency, making our operations faster and more effective. Lets examine the key applications of AI agents across industries, such as travel booking and expense management, employee onboarding, personalized customer support, and data analytics and reporting. However, developing AI agents is not without its challenges. Some of the primary considerations, including tool integration, interoperability, scalability, real-time processing, maintenance, flexibility, error handling, and security. These challenges underscore the need for robust platforms that enable rapid development and secure deployment of AI agents. To this end, we introduce Azure AI Foundry and Azure AI Agent Service. These tools empower developers to build, deploy, and scale AI agents securely and efficiently. Azure AI Foundry offers a comprehensive suite of tools, including model catalogs, content safety features, and machine learning capabilities. The Azure AI Agent Service, currently in public preview, provides flexible model selection, extensive data connections, enterprise-grade security, and rapid development and automation capabilities. When building multi agent or agentic based systems there is a huge importance of multi-agent orchestration. Tools like AutoGen and Semantic Kernel facilitate the orchestration of multi-agent systems, enabling seamless integration and collaboration between different AI agents. In conclusion, the transformative potential of AI agents in driving productivity, efficiency, and innovation. By leveraging the capabilities of Azure AI Foundry and Azure AI Agent Service, we can overcome the challenges of AI agent development and unlock new opportunities for growth and success. Resources Azure AI Discord - https://aka.ms/AzureAI/Discord Global AI community - https://globalai.community Generative AI for beginners – https://aka.ms/genai-beginners AI Agents for beginners - https://aka.ms/ai-agents-beginners Attend one of the Global AI Bootcamp near you - https://globalai.community/bootcamp/ Build AI Tour open content - https://aka.ms/aitour/repos Build your first Agent with Azure AI Agent Service - Slide deck and code - https://github.com/microsoft/aitour-build-your-first-agent-with-azure-ai-agent-service
Monitoring and Evaluating LLMs in Clinical Contexts with Azure AI Foundry
👀 Missed Session 02? Don’t worry—you can still catch up. But first, here’s what AI HLS Ignited is all about: What is AI HLS Ignited? AI HLS Ignited is a Microsoft-led technical series for healthcare innovators, solution architects, and AI engineers. Each session brings to life real-world AI solutions that are reshaping the Healthcare and Life Sciences (HLS) industry. Through live demos, architectural deep dives, and GitHub-hosted code, we equip you with the tools and knowledge to build with confidence. Session 02 Recap: In this session, we introduced MedEvals, an end-to-end evaluation framework for medical AI applications built on Azure AI Foundry. Inspired by Stanford’s MedHELM benchmark, MedEvals enables providers and payers to systematically validate performance, safety, and compliance of AI solutions across clinical decision support, documentation, patient communication, and more. 🧠 Why Scalable Evaluation Is Critical for Medical AI "Large language models (LLMs) hold promise for tasks ranging from clinical decision support to patient education. However, evaluating the performance of LLMs in medical contexts presents unique challenges due to the complex and critical nature of medical information." — Evaluating large language models in medical applications: a survey As AI systems become deeply embedded in healthcare workflows, the need for rigorous evaluation frameworks intensifies. Although large language models (LLMs) can augment tasks ranging from clinical documentation to decision support, their deployment in patient-facing settings demands systematic validation to guarantee safety, fidelity, and robustness. Benchmarks such as MedHELM address this requirement by subjecting models to a comprehensive battery of clinically derived tasks built on dataset (ground truth), enabling fine-grained, multi-metric performance assessment across the full spectrum of clinical use cases. However, shipping a medical LLM is only step one. Without a repeatable, metrics-driven evaluation loop, quality erodes, regulatory gaps widen, and patient safety is put at risk. This project accelerates your ability to operationalize trustworthy LLMs by delivering plug-and-play medical benchmarks, configurable evaluators, and CI/CD templates—so every model update triggers an automated, domain-specific “health check” that flags drift, surfaces bias, and validates clinical accuracy before it ever reaches production. 🚀 How to Get Started with MedEvals Kick off your MedEvals journey by following our curated labs. Newcomers to Azure AI Foundry can start with the foundational workflow; seasoned practitioners can dive into advanced evaluation pipelines and CI/CD integration. 🧪 Labs 🧪 Foundry Basics & Custom Evaluations: 🧾 Notebook Authenticate, initialize a Foundry project, run built-in metrics, and build custom evaluators with EvalAI and PromptEval. 🧪 Search & Retrieval Evaluations: 🧾 Notebook Prepare datasets, execute search metrics (precision, recall, NDCG), visualize results, and register evaluators in Foundry. 🧪 Repeatable Evaluations & CI/CD: 🧾 Notebook Define evaluation schemas, build deterministic pipelines with PyTest, and automate drift detection using GitHub Actions. 🏥 Use Cases 📝 Creating Your Clinical Evaluation with RevCycle Determinations Select a model and metric that best supports the determination behind the rationale made on AI-assisted prior authorizations based on real payor policy. This notebook use case includes: Selecting multiple candidate LLMs (e.g., gpt-4o, o1) Breaking down determinations both in deterministic results (approved vs rejected) and the supporting rationale and logic. Running evaluations across multiple dimensions Combining deterministic evaluators and LLM-as-a-Judge methods Evaluating the differential impacts of evaluators on the rationale across scenarios 🧾Get Started with the Notebook Why it matters: Enables data-driven metric selection for clinical workflows, ensures transparent benchmarking, and accelerates safe AI adoption in healthcare. 📝 Evaluating AI Medical Notes Summarization Applications Systematically assess how different foundation models and prompting strategies perform on clinical summarization tasks, following the MedHELM framework. This notebook use case includes: Preparing real-world datasets of clinical notes and summaries Benchmarking summarization quality using relevance, coherence, factuality, and harmfulness metrics Testing prompting techniques (zero-shot, few-shot, chain-of-thought prompting) Evaluating outputs using both automated metrics and human-in-the-loop scoring 🧾Get Started with the Notebook Why it matters: Ensures responsible deployment of AI applications for clinical summarization, guaranteeing high standards of quality, trustworthiness, and usability. 📣 Join Us for the Next Session Help shape the future of healthcare by sharing AI HLS Ignited with your network—and don’t miss what’s coming next! 📅 Register for the upcoming session → AI HLS Ignited Event Page 💻 Explore the code, demos, and architecture → AI HLS Ignited GitHub RepositoryUsing Advanced Reasoning Model on EdgeAI Part 1 - Quantization, Conversion, Performance
DeepSeek-R1 is very popular, and it can achieve the same capabilities as OpenAI o1 in advanced reasoning. Microsoft has also added DeepSeek-R1 models to Azure AI Foundry and GitHub Models. We can compare DeepSeek-R1 ith other available models through GitHub Models Playground Note This series revolves around deployment of SLMs to Edge Devices 'Edge AI' we will focus on the deployment advanced reasoning models, with different application scenarios. You can learn more in the following session AI Tour BRK453. In this experiement we want to deploy advanced reasoning models to the edge, so that they can run on edge devices with limited computing power and offline environments. At this time, the recommendation is to use the traditional ONNX model . We can use Microsoft Olive to convert the DeepSeek-R1 Distrill model. Getting started with Microsoft Olive is very straightforward. Install the Microsoft Olive library through the command line and Python 3.10+ (recommended) pip install olive-ai The DeepSeek-R1 Distrill model series has different parameters such as 1.5B, 7B, 8B, 14B, 32B, 70B, etc. This article is mainly based on the 1.5B, 7B, and 14B models (so a Small Language Model). CPU Inference Let's discuss 1.5B and 7B, which are models with lower parameter. We can directly use the CPU as computing for inference to test the effect (hardware environment Azure DevBox, AMD EPYC 7763 64-Core + 64GB Memory + 2T SSD) Quantization conversion olive auto-opt --model_name_or_path <Your DeepSeek-R1-Distill-Qwen-1.5B/7B local location> --output_path <Your Convert ONNX INT4 Model local location> --device cpu --provider CPUExecutionProvider --precision int4 --use_model_builder --log_level 1 You can download it directly from my Hugging face Repo (Note: This model is for testing and has not been fully tested by AI Content Safety or provided as an Offical Model) DeepSeek-R1-Distill-Qwen-1.5B-ONNX-INT4-CPU DeepSeek-R1-Distill-Qwen-7B-ONNX-INT4-CPU Running with ONNX Runtime GenAI Install ONNX Runtime GenAI and ONNX Runtime CPU support libraries pip install onnxruntime-genai pip install onnxruntime Sample Code https://github.com/kinfey/EdgeAIForAdvancedReasoning/blob/main/notebook/demo-1.5b.ipynb https://github.com/kinfey/EdgeAIForAdvancedReasoning/blob/main/notebook/demo-7b.ipynb Performance comparison 1.5B vs 7B We compare two different inference scenarios explain 1+1=2 1.5B quantized ONNX model memory occupied, time consumption and number of tokens generated: 7B quantized ONNX model memory occupied, time consumption and number of tokens generated 2. Find all pairwise different isomorphism groups with order 147 and no elements with order 49 1.5B quantized ONNX model memory occupied, time consumption and number of tokens generated: 7B quantized ONNX model memory occupied, time consumption and number of tokens generated Results of the numbers Through the test, we can see that the 1.5B model of DeepSeek is more suitable for use on CPU inference and can be deployed on traditional PCs or IoT devices. As for 7B, although it has better inference, it is not very effective on CPU operation. GPU Inference It is ideal if we have a GPU on the edge device. We can quantize and convert it to an ONNX model for CPU inference through Microsoft Olive. Of course, it can also be converted to a model for GPU inference. Here I take the 14B DeepSeek-R1-Distill-Qwen-14B as an example and make an inference comparison with Microsoft's Phi-4-14B Quantization conversion olive auto-opt --model_name_or_path <Your Phi-4-14B or DeepSeek-R1-Distill-Qwen-14B local path > --output_path <Your converted Phi-4-14B or DeepSeek-R1-Distill-Qwen-14B local path > --device gpu --provider CUDAExecutionProvider --precision int4 --use_model_builder --log_level 1 You can download it directly from my Hugging face Repo (Note: This model is for testing and has not been fully tested by AI Content Safety and not an Official Model) DeepSeek-R1-Distill-Qwen-14B-ONNX-INT4-GPU Phi-4-14B-ONNX-INT4-GPU Running with ONNX Runtime GenAI CUDA Install ONNX Runtime GenAI and ONNX Runtime GPU support libraries pip install onnxruntime-genai-cuda pip install onnxruntime-gpu Compare the results in the GPU environment with Gradio It is recommended to use a GPU with more than 8G memory To increase the comparison of the results, we compare it with Phi-4-14B-ONNX-INT4-GPU and DeepSeek-R1-Distill-Qwen-14B-ONNX-INT4-GPU to see the different results. We also show we use OpenAI o1-mini (it is recommended to use o1-mini through GitHub Models), Sample Code https://github.com/kinfey/EdgeAIForAdvancedReasoning/blob/main/notebook/Performance_AdvancedReasoning_ONNX_CPU.ipynb You can test any prompt on Gradio to compare the results of Phi-4-14B-ONNX-INT4-GPU, DeepSeek-R1-Distill-Qwen-14B-ONNX-INT4-GPU and OpenAI o1 mini. DeepSeek-R1 reduces the cost of inference models and produces more instructive results on professional problems, but Phi-4-14B also has advantages in reasoning and uses lower computing power to complete inference. As for OpenAI o1 mini, it is more comprehensive and can touch all problems. If you want to deploy to Edge Device, Phi-4-14B and quantized DeepSeek-R1 are good choices for you. This blog is just a simple test and the first in this series. Please share your feedback and continue the discussion in the Microsoft AI Discord Channel. Feel free to me a message or comment. We look forward to sharing more around the opportunity of EdgeAI and more content in this series. Resource DeepSeek-R1 in GitHub Models https://github.com/marketplace/models/azureml-deepseek/DeepSeek-R1 DeepSeek-R1 in Azure AI Foundry https://ai.azure.com/explore/models/DeepSeek-R1/version/1/registry/azureml-deepseek Phi-4-14B in Hugging face https://huggingface.co/microsoft/phi-4 Learn about Microsoft Olive https://github.com/microsoft/olive Learn about ONNX Runtime GenAI https://github.com/microsoft/onnxruntime-genai Microsoft AI Discord Channel BRK453 Exploring cutting-edge models: LLMs, SLMs, local development and more https://aka.ms/aitour/brk453How do I give my agent access to tools?
Welcome back to Agent Support—a developer advice column for those head-scratching moments when you’re building an AI agent! Each post answers a real question from the community with simple, practical guidance to help you build smarter agents. Today’s question comes from someone trying to move beyond chat-only agents into more capable, action-driven ones: 💬 Dear Agent Support I want my agent to do more than just respond with text. Ideally, it could look up information, call APIs, or even run code—but I’m not sure where to start. How do I give my agent access to tools? This is exactly where agents start to get interesting! Giving your agent tools is one of the most powerful ways to expand what it can do. But before we get into the “how,” let’s talk about what tools actually mean in this context—and how Model Context Protocol (MCP) helps you use them in a standardized, agent-friendly way. 🛠️ What Do We Mean by “Tools”? In agent terms, a tool is any external function or capability your agent can use to complete a task. That might be: A web search function A weather lookup API A calculator A database query A custom Python script When you give an agent tools, you’re giving it a way to take action—not just generate text. Think of tools as buttons the agent can press to interact with the outside world. ⚡ Why Give Your Agent Tools? Without tools, an agent is limited to what it “knows” from its training data and prompt. It can guess, summarize, and predict, but it can’t do. Tools change that! With the right tools, your agent can: Pull live data from external systems Perform dynamic calculations Trigger workflows in real time Make decisions based on changing conditions It’s the difference between an assistant that can answer trivia questions vs. one that can book your travel or manage your calendar. 🧩 So… How Does This Work? Enter Model Context Protocol (MCP). MCP is a simple, open protocol that helps agents use tools in a consistent way—whether those tools live in your app, your cloud project, or on a server you built yourself. Here’s what MCP does: Describes tools in a standardized format that models can understand Wraps the function call + input + output into a predictable schema Lets agents request tools as needed (with reasoning behind their choices) This makes it much easier to plug tools into your agent workflow without reinventing the wheel every time! 🔌 How to Connect an Agent to Tools Wiring tools into your agent might sound complex, but it doesn’t have to be! If you’ve already got a MCP server in mind, there’s a straightforward way within the AI Toolkit to expose it as a tool your agent can use. Here’s how to do it: Open the Agent Builder from the AI Toolkit panel in Visual Studio Code. Click the + New Agent button and provide a name for your agent. Select a Model for your agent. Within the Tools section, click + MCP Server. In the wizard that appears, click + Add Server. From there, you can select one of the MCP servers built my Microsoft, connect to an existing server that’s running, or even create your own using a template! After giving the server a Server ID, you’ll be given the option to select which tools from the server to add for your agent. Once connected, your agent can call tools dynamically based on the task at hand. 🧪 Test Before You Build Once you’ve connected your agent to an MCP server and added tools, don’t jump straight into full integration. It’s worth taking time to test whether the agent is calling the right tool for the job. You can do this directly in the Agent Builder: enter a test prompt that should trigger a tool in the User Prompt field, click Run, and observe how the model responds. This gives you a quick read on tool call accuracy. If the agent selects the wrong tool, it’s a sign that your system prompt might need tweaking before you move forward. However, if the agent calls the correct tool but the output still doesn’t look right, take a step back and check both sides of the interaction. It might be that the system prompt isn’t clearly guiding the agent on how to use or interpret the tool’s response. But it could also be an issue with the tool itself—whether that’s a bug in the logic, unexpected behavior, or a mismatch between input and expected output. Testing both the tool and the prompt in isolation can help you pinpoint where things are going wrong before you move on to full integration. 🔁 Recap Here’s a quick rundown of what we covered: Tools = external functions your agent can use to take action MCP = a protocol that helps your agent discover and use those tools reliably If the agent calls the wrong tool—or uses the right tool incorrectly—check your system prompt and test the tool logic separately to isolate the issue. 📺 Want to Go Deeper? Check out my latest video on how to connect your agent to a MCP server—it’s part of the Build an Agent Series, where I walk through the building blocks of turning an idea into a working AI agent. The MCP for Beginners curriculum covers all the essentials—MCP architecture, creating and debugging servers, and best practices for developing, testing, and deploying MCP servers and features in production environments. It also includes several hands-on exercises across .NET, Java, TypeScript, JavaScript and Python. 👉 Explore the full curriculum: aka.ms/AITKmcp And for all your general AI and AI agent questions, join us in the Azure AI Foundry Discord! You can find me hanging out there answering your questions about the AI Toolkit. I'm looking forward to chatting with you there! Whether you’re building a productivity agent, a data assistant, or a game bot—tools are how you turn your agent from smart to useful.I want to show my agent a picture—Can I?
Welcome to Agent Support—a developer advice column for those head-scratching moments when you’re building an AI agent! Each post answers a question inspired by real conversations in the AI developer community, offering practical advice and tips. To kick things off, we’re tackling a common challenge for anyone experimenting with multimodal agents: working with image input. Let’s dive in! Dear Agent Support, I’m building an AI agent, and I’d like to include screenshots or product photos as part of the input. But I’m not sure if that’s even possible, or if I need to use a different kind of model altogether. Can I actually upload an image and have the agent process it? Great question, and one that trips up a lot of people early on! The short answer is: yes, some models can process images—but not all of them. Let’s break that down a bit. 🧠 Understanding Image Input When we talk about image input or image attachments, we’re talking about the ability to send a non-text file (like a .png, .jpg, or screenshot) into your prompt and have the model analyze or interpret it. That could mean describing what’s in the image, extracting text from it, answering questions about a chart, or giving feedback on a design layout. 🚫 Not All Models Support Image Input That said, this isn’t something every model can do. Most base language models are trained on text data only, they’re not designed to interpret non-text inputs like images. In most tools and interfaces, the option to upload an image only appears if the selected model supports it, since platforms typically hide or disable features that aren't compatible with a model's capabilities. So, if your current chat interface doesn’t mention anything about vision or image input, it’s likely because the model itself isn’t equipped to handle it. That’s where multimodal models come in. These are models that have been trained (or extended) to understand both text and images, and sometimes other data types too. Think of them as being fluent in more than one language, except in this case, one of those “languages” is visual. 🔎 How to Find Image-Supporting Models If you’re trying to figure out which models support images, the AI Toolkit is a great place to start! The extension includes a built-in Model Catalog where you can filter models by Feature—like Image Attachment—so you can skip the guesswork. Here’s how to do it: Open the Model Catalog from the AI Toolkit panel in Visual Studio Code. Click the Feature filter near the search bar. Select Image Attachment. Browse the filtered results to see which models can accept visual input. Once you've got your filtered list, you can check out the model details or try one in the Playground to test how it handles image-based prompts. 🧪 Test Before You Build Before you plug a model into your agent and start wiring things together, it’s a good idea to test how the model handles image input on its own. This gives you a quick feel for the model’s behavior and helps you catch any limitations before you're deep into building. You can do this in the Playground, where you can upload an image and pair it with a simple prompt like: “Describe the contents of this image.” OR “Summarize what’s happening in this screenshot.” If the model supports image input, you’ll be able to attach a file and get a response based on its visual analysis. If you don’t see the option to upload an image, double-check that the model you’ve selected has image capabilities—this is usually a model issue, not a UI bug. 🔁 Recap Here’s a quick rundown of what we covered: Not all models support image input—you’ll need a multimodal model specifically built to handle visual data. Most platforms won’t let you upload an image unless the model supports it, so if you don’t see that option, it’s probably a model limitation. You can use the AI Toolkit’s Model Catalog to filter models by capability—just check the box for Image Attachment. Test the model in the Playground before integrating it into your agent to make sure it behaves the way you expect. 📺 Want to Go Deeper? Check out my latest video on how to choose the right model for your agent—it’s part of the Build an Agent Series, where I walk through the building blocks of turning an idea into a working AI agent. And if you’re looking to sharpen your model instincts, don’t miss Model Mondays—a weekly series that helps developers like you build your Model IQ, one spotlight at a time. Whether you’re just starting out or already building AI-powered apps, it’s a great way to stay current and confident in your model choices. 👉 Explore the series and catch the next episode: aka.ms/model-mondays/rsvp If you're just getting started with building agents, check out our Agents for Beginners curriculum. And for all your general AI and AI agent questions, join us in the Azure AI Foundry Discord! You can find me hanging out there answering your questions about the AI Toolkit. I'm looking forward to chatting with you there! Whatever you're building, the right model is out there—and with the right tools, you'll know exactly how to find it.
