An AI agent is a smart program that can think, make decisions, and do tasks. Sometimes it works alone, and sometimes it works with people or other agents. The main difference between an agent and a regular assistant is that agents can do things on their own. They don’t just help—you can give them a goal, and they’ll try to reach it. Every AI agent has three main parts: Agents can take input like a message or a prompt and respond with answers or actions.  For example, they might look something up or start a process based on what you asked. Azure AI Foundry is a platform that brings all these things together; so you can build, train, and manage AI agents easily. References What is Azure AI Foundry Agent Service? – Azure AI Foundry | Microsoft Learn Understanding deployment types in Azure AI Foundry Models – Azure AI Foundry | Microsoft Learnhttps://learn.microsoft.com/en-us/azure/ai-foundry/how-to/index-add Usage Firstly, we create a project in Azure AI Foundry. Click on Next and give a name to your project. Wait till the setup finishes. Once the project creation finishes we are greeted with this screen. Click on Agents tab and click on Next to choose the model. I’m currently using GPT-4o Mini. It also includes descriptions for all the available models. Then we configure the deployment details. There are multiple deployment types available such as – Global Deployments Data Zone Standard Deployments Standard deployments [Standard] follow a pay-per-use model perfect for getting started quickly.They’re best for low to medium usage with occasional traffic spikes.  However, for high and steady loads, performance may vary.Provisioned deployments [ProvisionedManaged] let you pre-allocate the amount of processing power you need.This is measured using Provisioned Throughput Units (PTUs).  Each model and version requires a different number of PTUs and offers different performance levels. Provisioned deployments ensure predictable and stable performance for large or mission-critical workloads. This is how the deployment details look for in Global Standard. I’ll be choosing Standard deployment for our use case. Click on deploy and wait for a few seconds. Once the deployment is completed, you can give your agent a name and some instructions for their behavior. You should specify the tone, end goal, verbosity, etc as well. You can also specify the Temperature and Top P values which are both a control on the randomness or creativeness of the model. Temperature controls how bold or cautious the model is. Lower temperature = Safer, more predictable answers. (Factual Q&A, Code Summarization)Higher temperature = More creative or surprising answers. (Poetry/Creative writing) Top P (Nucleus Sampling) controls how wide the model’s word choices are. Lower Top P = Only picks from the most likely words. (Legal or financial writing) Higher Top P = Includes less likely, more diverse words. (Brainstorming names) Next, I’ll add a knowledge base to my bot. For this example, I’ll just upload a single file.However, you have the option to add an sharepoint folder or files, connect it to Bing Search, MS Fabric, Azure AI search, etc as required. A Vector store in Azure AI Foundry helps your AI agent retrieve relevant information based on meaning rather than just keywords.It works by breaking your content (like a PDF) into smaller parts, converting them into numerical representations (embeddings), and storing them.When a user asks a question, the AI finds the most semantically similar parts from the vector store and uses them to generate accurate, context-aware responses. Once you select the file, click on Upload and save. At this point, you can start to interact with your model. To “play around” with your model, click on the “Try in Playground” button. And here, we can see the output based on our provided knowledge base. One more example, just because it is kind of fun. Every input that you provide to the agent is called as a “message”. Everytime the agent is invoked for processing the provided input is called a “run”. Every interaction session with the agent is called a “thread”. We can see all the open threads in the threads section. To conclude, Azure AI Foundry makes it easy to build and use AI agents without writing any code.  You can choose models, set how they behave, and connect your data all through a simple interface. Whether you’re testing ideas, automating tasks, or building custom bots, Foundry gives you the tools to do it.If you’re curious about AI or want to try building your agent, Foundry is a great place to begin. We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfonts.com

In today’s world, we use many different applications for our daily work. One single application can’t handle everything because some apps are designed for specific tasks. That’s why organizations use multiple applications, which often leads to data being stored separately or in isolation. In this blog, we’ll take you on a journey from siloed systems to connected systems through a customer success story. About BÜCHI Büchi Labortechnik AG is a Swiss company renowned for providing laboratory and industrial solutions for R&D, quality control, and production. Founded in 1939, Büchi specializes in technologies such as: Their equipment is widely used in pharmaceuticals, chemicals, food & beverage, and academia for sample preparation, formulation, and analysis. Büchi is known for its precision, innovation, and strong customer support worldwide.  Systems Used by BÜCHI To streamline operations and ensure seamless collaboration, BÜCHI leverages a variety of enterprise systems: Infor and SAP Business One are utilized for managing critical business functions such as finance, supply chain, manufacturing, and inventory. Reporting Challenges Due to Siloed Systems Organizations often rely on multiple disconnected systems across departments — such as ERP, CRM, marketing platforms, spreadsheets, and legacy tools. These siloed systems result in: The Need for a Single Source of Truth To solve these challenges, it’s critical to establish a Single Source of Truth (SSOT) — a central, trusted data platform where all key business data is: How We Helped Büchi Connect Their Systems To build a seamless and scalable integration framework, we leveraged the following Azure services: >Azure Logic Apps – Enabled no-code/low-code automation for integrating applications quickly and efficiently. >Azure Functions – Provided serverless computing for lightweight data transformations and custom logic execution. >Azure Service Bus – Ensured reliable, asynchronous communication between systems with FIFO message processing and decoupling of sender/receiver availability. >Azure API Management (APIM) – Secured and simplified access to backend services by exposing only required APIs, enforcing policies like authentication and rate limiting, and unifying multiple APIs under a single endpoint. BÜCHI’s case study was published on the Microsoft website, highlighting how CloudFronts helped connect their systems and prepare their data for insights and AI-driven solutions. Why a Single Source of Truth (SSOT) Is Important A Single Source of Truth means having one trusted location where your business stores consistent, accurate, and up-to-date data. Key Reasons It Matters: How we did this We used Azure Function Apps, Service Bus, and Logic Apps to seamlessly connect the systems. Databricks was implemented to build a Unity Catalog, establishing a Single Source of Truth (SSOT). On top of this unified data layer, we enabled advanced analytics and reporting using Power BI. In May, we hosted an event with BÜCHI at the Microsoft Office in Zurich. During the session, one of the attending customers remarked, “We are five years behind BÜCHI.” Another added, “If we don’t start now, we’ll be out of the race in the future.” This clearly reflects the urgent need for businesses to evolve. Today, Connected Systems, a Single Source of Truth (SSOT), Advanced Analytics, and AI are not optional — they are essential for sustainable growth and improved human efficiency. The pace of transformation has accelerated: tasks that once took months can now be achieved in days — and soon, perhaps, with just a prompt. To conclude, if you’re operating with multiple disconnected systems and relying heavily on manual processes, it’s time to rethink your approach. System integration and automation free your teams from repetitive work and empower them to focus on high impact, strategic activities. We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfonts.com.

In today’s world, AI is no longer a luxury—it’s a necessity for driving smarter decisions, faster innovation, and personalized experiences. We have come up with our requirements for AI to support the conversion from MQL (Marketing Qualified Lead) to SQL (Sales Qualified Lead). How did the idea originate? In our organization, whenever a prospect reaches out to us, we search for company information like company size, revenue, location, industry type, contact person details, designation, decision-maker, and LinkedIn profile. This information helps the Sales team prepare better and deliver a stronger pitch by understanding the customer before the call. Also, during the MQL to SQL stage, we look for things like: This information helps the Sales team convert the prospect into a client and increases our chances of winning the deal. Earlier, this entire process was manual and time-consuming. So, we decided to automate it with an AI agent that can gather this information for us in just a few minutes. Implementation approach After the project was approved internally, we started exploring how to make it happen. Initially, we didn’t know where or how to start. During our research, we came across Copilot Studio, which allows us to build custom agents from scratch based on our needs. We learned about Copilot Studio’s and began building our agent. We named it Elevator Pitch. Version 1 Highlights: This feedback led to the idea for Version 2, which would automate more steps and also pull information from the internet. Version 2 Enhancements: Version 2 Features: Company & Contact information with a single click on MQL to SQL, the agent now generates the document within minutes—something that earlier used to take hours or even a full day. Live demo in Zurich & New York On 22nd May 2025, we had an event scheduled at the Microsoft office in Zurich with one of our clients, where we shared the Buchi journey with CloudFronts. We discussed how we collaborated to connect their multiple systems and prepared their data for insights and AI initiatives. At the same event, we had the opportunity to demonstrate our Smart Pitch product, which caught the audience’s attention. It was a proud moment for us to showcase our first AI product at the Microsoft office—delivered within just a few months of hard work. Our second opportunity came on 06 June 2025 in New York, at the AI Community Conference, where we presented again in front of a global audience. What Next in Version 3: So far, we have built this solution using Microsoft’s inbuilt Knowledge Center, ChatGPT API, SharePoint, company websites, and Dataverse. Since we were working with both structured and unstructured data, we faced some inconsistencies and performance issues. This led us to reflect and identify the need for Version 3 (V3), which will include: The development of Smart Pitch V3 is currently in progress. We’ll share our thoughts once it goes live. A demo video has also been shared, so you can see how smart and fast our Agent is at delivering useful insights. Delivering Answers in Minutes—Thanks to Smart Pitch I’d also like to share a quick story. One day, our Practice Manager was on leave, and we received a prospect inquiry about Project Operations to Business Central (PO-BC) pricing. I wasn’t sure where that information was stored, and suddenly our CEO asked me for the details. I was a bit stressed, unsure where to search or how to respond. Then I decided to ask our Smart Pitch agent the same question. To my surprise, the agent quickly gave me the exact information I needed. It was a big relief, and I was able to share the details with our CEO in just a few minutes—without even knowing where the document was uploaded. We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfonts.com.