What is your primary use case for Azure AI Foundry?

My main use case is customers' implementation.

My main use cases for Azure AI Foundry revolve around employee productivity agents, building agents, and connecting it to the data ecosystem. Those are our primary use cases, and we are using that to build RAG architecture and RAG deployment for supply chain-related work as well. Document Intelligence is the primary AI service we use within Azure AI Foundry for pretty much all the use cases, and we are leveraging it for speech recognition too. Document Intelligence is the primary service that we use. The reason Document Intelligence is the most used one is that a bunch of our use cases go through tons and tons of documents. For instance, we recently initiated a project for all our supply chain contracts that Cox currently owns, which are close to around 60,000 documents, and then it has multiple sub-documents under that. Going through that and looking for some privacy language, whether it's defined as what we would anticipate or what we would expect, would have taken a couple of years for our supply chain team to do manually. With Document Intelligence, we just went through Foundry, enabled Document Intelligence, and we were able to get everything done in less than 90 days for the complete end-to-end solution we built on that. We see value there. All of them are impactful, but RAG seems to be the most impactful for us. RAG is the most impactful because it's the way we do the augmentation on the document and the way it processes the document. That's the primary thing. AI Search and RAG are complementary, so that's the primary use case for us. We are developing AI agents.

My main use cases for Azure AI Foundry include deploying AI applications to perform document comparison, translation services, and a chat feature, helping the digital AI team at our company. Currently, I am using some speech services, but Document Intelligence is probably one of the core services that I am using for my use cases today, as we have translation services that have been tremendously helpful in translating manuals and other materials that previously required manual effort. I use Azure AI Search to search existing components and data that is connected to my AI solutions. The most impactful services I utilize are Document Intelligence because that is where I see a lot of validation issues, problems, and discrepancies, especially regarding speed compared to what an actual human can do versus what Document Intelligence can produce. I am definitely considering developing an AI agent, as that seems to be something that will be core to building workflows and continuing automation and AI initiatives. I think we need to develop a roadmap and plan on how to incorporate Azure AI Foundry agent service at my company, and I believe it will be very useful to leverage that technology.

We are currently examining agentic data modeling and data lineage, focusing on the types of relationships between different data sets, jobs, scripts, data warehouses, and tables, and how we can build that into one knowledge graph. We are mainly exploring this area, but there are many different security use cases that we have been investigating with Sentinel and Sentinel Data Lake, including leveraging the MCP server and using agent-to-agent protocol for interoperability between different agents and tools. We are deploying AI applications on the cloud, attempting to get some of them on the Security Store with Security Copilot. The ones that deal more with data are being deployed more on Azure. Agent 365 is exciting because it allows for better governance, providing visibility into agent metrics and traceability. While we do not have agents deployed yet, we anticipate having them operational within a month.

My main use cases for Azure AI Foundry involve deploying large language models, and we use it heavily for observability, evaluation, and fine-tuning. Those are the major ones. Azure AI Foundry has improved decision-making in my clients' organizations, as they often use it for decision support, which is probably one of the primary use cases among many of my different clients that are working with Azure AI Foundry.

My main use cases for Azure AI Foundry are agents for specific workflows within the business.

My main use cases for Azure AI Foundry are agentic workflows. I do completions, such as chat completions, and other agent building and maintaining models within Azure. I am utilizing Document Intelligence and Content Understanding services of Azure AI Foundry. I use Azure AI Search for RAG capabilities. I use it to build knowledge bases for my models to work on so they have context of what our business processes are. I am using Azure AI Foundry models for accessing various types of AI models. I primarily use the GPT models right now, but I'm evaluating other options, especially with the new capabilities that have been built and released this week.

My main use case involves building multi-agent workflows.

My main use cases for Azure AI Foundry are AI models and then building AI agents for our client base.

We are still quite early in our understanding of what Azure AI Foundry can do for us, and we have plenty of use cases where we think AI can help. I am in the legal sector in the UK. For example, I think there are scenarios with lots of legal processes which could be digitized, and you could leverage AI for that, specifically looking at agents and how they can do some of that heavy lifting. We are developing and considering developing AI agents, and we plan to incorporate Azure AI Foundry Agent Service.

My main use case for Azure AI Foundry is deploying custom AI agents.

The main use cases are to house our agents, to keep our agents organized, and to check the lifecycle of our agents and if they're performing or have any errors.

My main use cases for Azure AI Foundry are developing agents that work with documents, including Q&A, RAG Q&A, and one that uses a code interpreter to work with data. I am developing agents and utilizing Azure Machine Learning for other use cases. I also utilize Azure AI Search.

Our main use case for Azure AI Foundry is that we built a chatbot and use Foundry as a control plane where we configured the content moderation and model precision, along with many guardrails. The Azure AI Foundry feature that has been most beneficial for enhancing customer experience is the integration of Azure Purview with Foundry because AI is data, context, and intelligence. Now that we have this integration, I want to ensure I can tag the data and model throughout the AI life cycle. I measure this improvement by looking at the deployment of the chatbot for an insurance customer, which was providing responses very slowly. We tried everything in Foundry, but it was not improving, so we deployed Redis Cache in front of it and made a copy of all the questions from the chatbot, which made the response faster. We made architectural changes within Foundry and integrated with Redis Cache to improve the speed of the chatbot. It's similar to how your phone with Maps works—if you stay in a hotel and look up the location once, the second time it automatically asks if you want to go there. We implemented caching before Foundry and the chatbot.

My main use of Azure AI Foundry is to create apps, our app backends, and integrating AI features into it, embedding AI features from there. We also use our tenant in Foundry to host the OpenAI models and then we basically have an API on a key. That is basically how we bring the AI usage in there. We are deploying the AI application in the cloud. Our apps are hosted in Azure, at a different part of the tenant, different resource group, and all of that. We also use it in integrating it back into Copilot 365 as well. I am not yet doing machine learning in Azure AI Foundry. I am just connecting to the models in Azure AI Foundry. We have explored it, but we haven't done anything specific. It is all really early days. The AI agent we are trying to create involves many agents. For example, we are creating research agents. We are a B2B transactional company, so with our Salesforce, we have 800 salespeople. They are always looking to see what is coming up for renewal, how they can go for alternative products where products are being discontinued. This is both hardware and software based products. We sell about 5,000 OEMs, 50,000 products every year to 45,000 businesses. There is a lot of data that is there, but it is very hard. We have built a simple agent that looks at the past orders in the last three months to see what is coming for renewal or anniversary dates, and then figures out what should be the top track an AM can have, and what should be the top products they can refer out to the customer. That is one example of an agent.

My main use cases on a day-to-day basis for Azure AI Foundry are to build proof of concepts, use cases, which include transcript summarization, document summarization, and knowledge retrieval.