Pantone’s Palette Generator enhances creative exploration with agentic AI on Azure
Color can be powerful. When creative professionals shape the mood and direction of their work, color plays a vital role because it provides context and cues for the end product or creation. For more than 60 years, creatives from all areas of design—including fashion, product, and digital—have turned to Pantone color guides to translate inspiration into precise, reproducible color choices. These guides offer a shared language for colors, as well as inspiration and communication across industries. Once rooted in physical tools, Pantone has evolved to meet the needs of modern creators through its trend forecasting, consulting services, and digital platform. Today, Pantone Connect and its multi-agent solution called the Pantone Palette Generator seamlessly bring color inspiration and accuracy into everyday design workflows (as well as the New York City mayoral race). Simply by typing in a prompt, designers can generate palettes in seconds. Available in Pantone Connect, the tool uses Azure services like Microsoft Foundry, Azure AI Search, and Azure Cosmos DB to serve up the company’s vast collection of trend and color research from the color experts at the Pantone Color Institute. “Pantone bridges art and industry—turning creative concepts into reality,” says Sky Kelley, President of Pantone. “Years of research and insights are reached in seconds instead of days. Now, with Microsoft Foundry, creatives can use agents to get instant color palettes and suggestions based on human insights and trend direction.” Turning Pantone’s color legacy into an AI offering The Palette Generator accelerates the process of researching colors and helps designers find inspiration or validate some of their ideas through trend-backed research. “Pantone wants to be where our customers are,” says Rohani Jotshi, Director of Software Engineering and Data at Pantone. “As workflows become increasingly digital, we wanted to give our customers a way to find inspiration while keeping the same level of accuracy and trust they expect from Pantone.” The Palette Generator taps into thousands of articles from Pantone’s Color Insider library, as well as trend guides and physical color books in a way that preserves the company’s color standards science while streamlining the creative process. Built entirely on Microsoft Foundry, the solution uses Azure AI Search for agentic retrieval-augmented generation (RAG) and Azure OpenAI in Foundry Models to reason over the data. It quickly serves up palette options in response to questions like “Show me soft pastels for an eco-friendly line of baby clothes” or “I want to see vibrant metallics for next spring.” Over the course of two months, the Pantone team built the initial proof of concept for the Palette Generator, using GitHub Copilot to streamline the process and save over 200 hours of work across multiple sprints. This allowed Pantone’s engineers to focus on improving prompt engineering, adding new agent capabilities, and refining orchestration logic rather than writing repetitive code. Building a multi-agent architecture that accelerates creativity The Pantone team worked with Microsoft to develop the multi-agent architecture, which is made up of three connected agents. Using Microsoft Agent Framework—an open source development kit for building AI orchestration systems—it was a straightforward process to bring the agents together into one workflow. “The Microsoft team recommended Microsoft Agent Framework and when we tried it, we saw how it was extremely fast and easy to create architectural patterns,” says Kristijan Risteski, Solutions Architect at Pantone. “With Microsoft Agent Framework, we can spin up a model in five lines of code to connect our agents.” When a user types in a question, they interact with an orchestrator agent that routes prompts and coordinates the more specialized agents. Behind the scenes an additional agent retrieves contextually relevant insights from Pantone’s proprietary Color Insider dataset. Using Azure AI Search with vectorized data indexing, this agent interprets the semantics of a user’s query rather than relying solely on keywords. A third agent then applies rules from color science to assemble a balanced palette. This agent ensures the output is a color combination that meets harmony, contrast, and accessibility standards. The result is a set of Pantone-curated colors that match the emotional and aesthetic tone of the request. “All of this happens in seconds,” says Risteski. To manage conversation flow and achieve long-term data persistence, Pantone uses Azure Cosmos DB, which stores user sessions, prompts, and results. The database not only enables designers to revisit past palette explorations but also provides Pantone with valuable usage intelligence to refine the system over time. “We use Azure Cosmos DB to track inputs and outputs,” says Risteski. “That data helps us fine-tune prompts, measure engagement, and plan how we’ll train future models.” Improving accuracy and performance with Azure AI Search With Azure AI Search, the Palette Generator can understand the nuance of color language. Instead of relying solely on keyword searches that might miss the complexity of words like “vibrant” or “muted,” Pantone’s team decided to use a vectorized index for more accurate palette results. Using the built-in vectorization capability of Azure AI Search, the team converted their color knowledge base—including text-based color psychology and trend articles—into numerical embeddings. “Overall, vector search gave us better results because it could understand the intent of the prompt, not just the words,“ says Risteski. “If someone types, ‘Show me colors that feel serene and oceanic,’ the system understands intent. It finds the right references across our color psychology and trend archives and delivers them instantly.” The team also found ways to reduce latency as they evolved their proof of concept. Initially, they encountered slow inference times and performance lags when retrieving search results. By switching from GPT-4.1 to GPT-5, latency improved. And using Azure AI Search to manage ranking and filtering results helped reduce the number of calls to the large language model (LLM). “With Azure, we just get the articles, put them in a bucket, and say ‘index it now,’ says Risteski. “It takes one or two minutes—and that’s it. The results are so much better than traditional search.” Moving from inspiration to palettes faster The Palette Generator has transformed how designers and color enthusiasts interact with Pantone’s expertise. What once took weeks of research and review can now be done in seconds. “Typically, if someone wanted to develop a palette for a product launch, it might take many months of research,” says Jotshi. “Now, they can type one sentence to describe their inspiration then immediately find Pantone-backed insight and options. Human curation will still be hugely important, but a strong set of starting options can significantly accelerate the palette development process.” Expanding the palette: The next phase for Pantone’s design agent Rapidly launching the Palette Generator in beta has redefined what the Pantone engineering team thought was possible. “We’re a small development team, but with Azure we built an enterprise-grade AI system in a matter of weeks,” says Risteski. “That’s a huge win for us.” Next up, the team plans to migrate the entire orchestration layer to Azure Functions, moving to a fully scalable, serverless deployment. This will allow Pantone to run its agents more efficiently, handle variable workloads automatically, and integrate seamlessly with other Azure products such as Microsoft Foundry and Azure Cosmos DB. At the same time, Pantone plans to expand its multi-agent system to include new specialized agents, including one focused on palette harmony and another focused on trend prediction.Model Mondays S2:E7 · AI-Assisted Azure Development
Welcome to Episode 7! This week, we explore how AI is transforming Azure development. We’ll break down two key tools—Azure MCP Server and GitHub Copilot for Azure—and see how they make working with Azure resources easier for everyone. We’ll also look at a real customer story from SightMachine, showing how AI streamlines manufacturing operations.Reimagining Telco with Microsoft: AI, TM Forum ODA, and Developer Innovation
The telecom industry is undergoing a seismic shift—driven by AI, open digital architectures, and the urgent need for scalable, customer-centric innovation. At the heart of this transformation is TM Forum Innovate Americas 2025, a flagship event bringing together global leaders to reimagine the future of connectivity. Microsoft’s presence at this year’s event is both strategic and visionary. As a key partner in the telecom ecosystem, Microsoft is showcasing how its technologies—spanning AI, cloud, and developer tools—are enabling Communication Service Providers (CSPs) to modernize operations, accelerate innovation, and deliver exceptional customer experiences. 🔑 Key Themes Shaping the Conversation Connected Intelligence: Microsoft is championing a new model of collaboration—one where AI systems, teams, and technologies work together seamlessly to solve real-world problems. This approach breaks down silos and enables intelligent decision-making across the enterprise. AI-First Mindset: From network optimization to customer service, Microsoft is helping telcos embed AI into the fabric of their operations. The focus is on building shared data platforms, connected models, and orchestration frameworks that scale. Customer Experience & Efficiency: With rising expectations and increasing complexity, CSPs must deliver faster, smarter, and more personalized services. Microsoft’s solutions are designed to enhance agility, reduce friction, and elevate the end-user experience. As the event unfolds, Microsoft’s sessions and showcases will highlight how these themes come to life—through real-world implementations, collaborative frameworks, and developer-first tools. Thought Leadership & Sessions At TM Forum Innovate Americas 2025, Microsoft is not just showcasing technology—it’s sharing a bold vision for the future of telecom. Through a series of thought-provoking sessions led by industry experts, Microsoft is demonstrating how AI, open standards, and developer tools can converge to drive meaningful transformation across the telco ecosystem. From enabling intelligent collaboration through the Azure AI Foundry, to operationalizing AI and Open Digital Architecture (ODA) for autonomous networks, and empowering developers with GitHub Copilot, Microsoft’s contributions reflect a deep commitment to innovation, scalability, and interoperability. Each session offers a unique lens into how Microsoft is helping Communication Service Providers (CSPs) modernize their IT stacks, accelerate development, and deliver exceptional customer experiences. Microsoft Thought Leadership Sessions CASE STUDY: Connected Intelligence: multiplying AI value across the enterprise 📅Sep 10 1:30pm CDT Peter Huang, Senior Director, Technology, Network Data and AI T-Mobile Andres Gil, Industry Advisor/Business Developer, Telco, Media and Gaming Industry Microsoft CASE STUDY: From hype to impact: operationalizing AI in telco with TM Forum’s ODA and Open APIs 📅Sep 11 1:30pm CDT Puja Athale, Director - Telco Global Azure AI Lead Microsoft Connected Intelligence & Azure AI Foundry: Scaling AI Across the Telco Enterprise T-Mobile and Microsoft are spotlighting a transformative approach to enterprise AI: Connected Intelligence. The joint session explores how telcos can break down silos and unlock the full potential of AI by enabling strategic collaboration across systems, teams, and technologies. The core challenge they address is clear: AI in isolation cannot answer even the simplest customer questions. Whether it's billing, device performance, or network coverage, fragmented systems lead to blind spots, duplication, and poor customer outcomes. To overcome this, they propose a unified framework that blends technology and culture—because tech alone doesn’t scale, and culture alone doesn’t transform. Azure AI Foundry: The Engine Behind Connected Intelligence At the heart of this vision is Microsoft’s Azure AI Foundry, a shared AI platform designed to scale intelligence across the enterprise and a core component of Microsoft’s recently announced Network Operations Agent Framework. Connected Intelligence integrates: Agent Frameworks and Agent Catalogs for modular AI deployment Hundreds of TBs of daily data from network switches, device logs, and location records Enterprise-grade orchestration and data governance AI/ML models aligned with customer-level time series events This architecture enables reuse, speed, and alignment across people, organizations, and systems—turning data into actionable intelligence. Model Context Protocol (MCP): AI-to-AI Collaboration A standout innovation is the Model Context Protocol (MCP), which goes beyond traditional APIs. While APIs connect systems through data, MCP connects intelligence through context. It allows AI agents to dynamically discover and chain APIs without custom coding, enabling real-time collaboration across network operations, device management, and deployment workflows. By integrating MCP into the API fabric, Microsoft is laying the groundwork for agentic AI—where intelligent systems can autonomously interact, adapt, and scale across the telco ecosystem. From Hype to Impact: Operationalizing AI in Telco with TM Forum’s ODA and Open APIs The telecom industry is moving from hype to impact by operationalizing AI through TM Forum’s Open Digital Architecture (ODA) and Open APIs. The session, From hype to impact: operationalizing AI in telco with TM Forum’s ODA and Open APIs, explores how telcos can build AI-ready architectures, unlock data value for automation and AI agents, and scale responsibly with governance and ethics at the core. Microsoft’s collaboration with TM Forum is enabling telcos to modernize OSS/BSS systems using the ODA Canvas—a modular, cloud-native execution environment orchestrated with AI and powered by Microsoft Azure. This architecture supports plug-and-play integration of differentiated services, reduces integration costs by over 30%, and boosts developer productivity by more than 40% with GitHub Copilot. Learn how leading telcos like Telstra are scaling AI solutions such as “One Sentence Summary” and “Ask Telstra” across their contact centers and retail teams. These solutions, built on Azure AI Foundry, have delivered measurable impact: 90% of employees reported time savings and increased effectiveness, with a 20% reduction in follow-up contacts. Telstra’s success is underpinned by a modernized data ecosystem and strong governance frameworks that ensure ethical and secure AI deployment. From Chaos to Clarity with Observability Despite advances in operational tooling, fragmented observability remains a persistent challenge. Vendors often capture telemetry in incompatible formats, forcing operations teams to rely on improvised log aggregators and custom parsers that drive up costs and hinder rapid incident resolution. Microsoft’s latest contribution to the Open Digital Architecture (ODA) initiative directly tackles this issue with the ODA Observability Operator, now available as open source on GitHub. By enforcing a standardized logging contract, integrating seamlessly with Azure Monitor, and surfacing health metrics through TM Forum nonfunctional APIs, the operator streamlines telemetry across systems. Early trials have shown promising results—carriers significantly reduced the time needed to detect billing anomalies, enabling teams to shift from reactive troubleshooting to proactive optimization. Accelerating TM Forum Open API Development with GitHub Copilot As the telecom industry embraces open standards and modular architectures, Microsoft is empowering developers to move faster and smarter with GitHub Copilot—an AI-powered coding assistant that’s transforming how TM Forum (TMF) Open APIs are built and deployed. Why GitHub Copilot for TM Forum Open APIs? TMF Open APIs are a cornerstone of interoperability in telecom, offering over 100 standardized RESTful interfaces across domains like customer management, product catalog, and billing. But implementing these APIs can be time-consuming and repetitive. GitHub Copilot streamlines this process by: Autocompleting boilerplate code for TMF endpoints Suggesting API handlers and data models aligned with TMF specs Generating test plans and documentation Acting as an AI pair programmer that understands your code context This means developers can focus on business logic while Copilot handles the heavy lifting. Real-World Uses Telco developers benefit from powerful features in GitHub Copilot that streamline the development of TMF Open API services. One such feature is Agent Mode, which automates complex, multi-step tasks such as implementing TMF API flows, running tests, and correcting errors—saving developers significant time and effort. Another key capability is Copilot Chat, which provides conversational support directly within the IDE, helping developers debug code, validate against TMF specifications, and follow best practices with ease. Together, these tools enhance productivity and reduce friction in building compliant, scalable telecom solutions. For example, when building a Customer Management microservice using the TMF629 API, Copilot can suggest endpoint handlers, validate field names against the spec, and even help write README documentation or unit tests. 📈 Proven Productivity Gains CSPs like Proximus have reported significant productivity improvements using GitHub Copilot in their Network IT functions: 20–30% faster code writing 25–35% faster refactoring 80–90% improvement in documentation 40–50% gains in code compliance Other telcos like Vodafone, NOS, Orange, TELUS, and Lumen Technologies are also leveraging Copilot to accelerate innovation and reduce development friction. Best Practices for TMF API Projects To get the most out of Copilot: Use it for repetitive tasks and pattern recognition Always validate generated code against TMF specs Keep relevant spec files open to improve suggestion accuracy Use Copilot Chat for guidance on security, error handling, and optimization GitHub Copilot is more than a coding assistant—it’s a catalyst for telco transformation. By combining AI with TMF’s open standards, Microsoft is helping developers build faster, smarter, and more consistently across the telecom ecosystem. Learn more about how to configure and use GitHub Copilot in your own TMF Open API projects in our latest tech community blog. Microsoft’s Broader Vision for Telco Transformation Microsoft’s contributions reflect a comprehensive strategy to reshape the telecom landscape through scalable intelligence, open collaboration, and developer empowerment. At the core of Microsoft’s vision is the idea that AI must be connected, contextual, and reusable. The Azure AI Foundry and Model Context Protocol (MCP) exemplify this approach by enabling telcos to: Harness massive volumes of time-series data from networks, devices, and customer interactions Deploy modular AI agents that can collaborate across systems Orchestrate workflows that adapt in real time to changing conditions This architecture transforms fragmented data into actionable insights, allowing CSPs to move from reactive operations to proactive intelligence. Conclusion: Microsoft’s Strategic Alignment with TM Forum Microsoft’s participation at TM Forum Innovate Americas 2025 reflects a deep commitment to transforming the telecom industry through AI-first innovation, open collaboration, and developer empowerment. From T-Mobile’s vision for Connected Intelligence, to Microsoft’s roadmap for operationalizing AI and ODA, and the developer-centric acceleration enabled by GitHub Copilot, Microsoft is helping Communication Service Providers (CSPs) move faster, scale smarter, and deliver better customer experiences. By aligning with TM Forum’s goals—standardization, interoperability, and autonomous operations—Microsoft is not just participating in the conversation; it’s helping lead it. 📣 Call to Action Join Microsoft and other industry leaders at TM Forum Innovate Americas 2025 to explore the future of telco transformation. Whether you're a strategist, technologist, or developer, this is your opportunity to connect, learn, and shape what’s next.Supercharge Your TM Forum Open API Development with GitHub Copilot
Developing applications that implement TM Forum (TMF) Open APIs can be greatly accelerated with the help of GitHub Copilot, an AI-based coding assistant. By combining Copilot’s code-generation capabilities with TMF’s standardized API specifications, developers can speed up coding while adhering to industry standards. In this blog post, we’ll walk through how to set up a project with GitHub Copilot to write TMF Open API-based applications, including prerequisites, configuration steps, an example workflow for building an API, best practices, and additional tips. Introduction: GitHub Copilot and TM Forum Open APIs GitHub Copilot is an AI-powered coding assistant developed by GitHub and OpenAI. It integrates with popular editors (VS Code, Visual Studio, JetBrains IDEs, etc.) and uses advanced language models to autocomplete code and even generate entire functions based on context and natural language prompts. For example, Copilot can turn a comment like “// fetch customer by ID” into a code snippet that implements that logic. It was first introduced in 2021 and is available via subscription for developers and enterprises. Copilot has the ability to interpret the code and comments in your current file and suggest code that fits, essentially acting as an AI pair programmer. TMF Open APIs refers to a set of standardized REST APIs for telecom and digital service providers. The APIs are designed to enable seamless connectivity and interoperability across complex service ecosystems. In practice, the TMF Open API program has defined over 100 RESTful interface specifications covering various domains (such as customer management, product catalog, billing, etc.). These APIs share a common design guideline (TMF630) and data model, ensuring that services can be managed end-to-end in a consistent way. Why use GitHub Copilot for TMF Open API development? Integrating Copilot with TMF Open API streamlines telecom app development. Copilot helps generate boilerplate code, suggests API handling snippets, and provides usage examples, all in line with TMF specs. For developers building services like Customer Management or Product Catalog, Copilot autocompletes endpoints, models, and business logic based on learned standards, maintaining TMF consistency. Developers review and edit outputs, but Copilot eases repetitive tasks. The following sections will guide you on setup and practical use with TMF Open API. "With GitHub Copilot, TM Forum members can accelerate API development — reducing boilerplate coding, improving consistency with our Open API standards, and freeing developers to focus on innovation rather than routine tasks. We’d love to hear from members already experimenting with Copilot — your experiences, lessons, and best practices will help shape how we embed AI-assisted coding into the wider TM Forum Open API community." - Ian Holloway, Chief Architect, TM Forum Prerequisites for Setting Up the Project Before configuring GitHub Copilot in your project, make sure you have the following prerequisites in place: GitHub Copilot Access: You will need an active GitHub Copilot subscription or trial linked to your GitHub account. Copilot is a paid service (with a free trial for new users), so ensure your account is signed up for Copilot access. If you haven’t done this, go to the https://github.com/features/copilot and activate your subscription or trial. Supported IDE or Code Editor: Copilot works with several development environments. For the best experience, use a supported editor such as Visual Studio Code, Visual Studio 2022, Neovim, or JetBrains IDEs (like IntelliJ, PyCharm, etc) GitHub Account: Obviously, you need a GitHub account to use Copilot (since you must sign in to authorize the Copilot plugin). Ensure you have your GitHub credentials handy. Programming Language Environment: Set up the programming language/framework you plan to use for your TMF Open API application. Copilot supports a wide range of languages, including JavaScript/TypeScript, Python, Java, C#, etc., so choose one that suits your project. TMF Open API Specification: Obtain the TMF Open API specifications or documentation for the APIs you plan to implement. TM Forum provides downloadable Open API (Swagger) specs for each API (for example, the Customer Management API, Product Catalog API, etc.). Basic Domain Knowledge: While not strictly required, it helps to have a basic understanding of the TMF Open API domain you're working with. For example, know what “Customer Management API” or “Product Catalog API” is supposed to do at a high level (reading the TMF user guide can help). This will make it easier to prompt Copilot effectively and to validate its suggestions. For more training, please refer to the TM Forum Education Programs. With these prerequisites met, you’re ready to configure GitHub Copilot in your development environment and integrate it into your project workflow. Step-by-Step Guide: Configuring GitHub Copilot in Your IDE Setting up GitHub Copilot for your project is a one-time process. Here is a step-by-step guide using Visual Studio Code as the example IDE: Step 1: Install the GitHub Copilot Extension. Open Visual Studio Code and navigate to the Extensions view (you can click the Extensions icon on the left toolbar or press Ctrl+Shift+X on Windows / Cmd+Shift+X on Mac). In the Extensions marketplace search bar, type “GitHub Copilot”. You should see the GitHub Copilot extension by GitHub. Click Install to add it to VS Code. This will download and enable the Copilot plugin in your editor. Step 2: Authenticate with GitHub. After installation, Copilot will prompt you to sign in to GitHub to authorize the extension. Click “Sign in with GitHub”. Log in with your GitHub credentials and grant permission to the Copilot extension. Step 3: Enable Copilot in your Workspace/Project. Now that Copilot is installed and linked to your account, you should ensure it’s enabled for your current project. In VS Code, open the command palette (Ctrl+Shift+P / Cmd+Shift+P) and type “Copilot”. Look for a command like “GitHub Copilot: Enable/Disable”. Make sure it’s enabled (it should be by default after installation). At this point, GitHub Copilot is fully configured in your development environment. The next step is to actually use it in developing a TMF Open API application. We will now walk through writing code with Copilot’s assistance, focusing on a TMF Open API use case. Writing TMF Open API Apps Using GitHub Copilot Now for the fun part – using GitHub Copilot to help write an application that implements a TMF Open API. In this section, we’ll provide a step-by-step example of how you might develop a simple service using a TMF Open API (say, a Customer Management API) with Copilot’s assistance. The principles can be applied to any TMF API or indeed any standard API. Scenario: Let’s assume we want to build a minimal Customer Management microservice that conforms to the TMF629 Customer Management API (version 5.0) – which manages customer records. We will implement a simple endpoint to retrieve customer information by ID, as defined in the TMF API spec. We’ll use Node.js with an Express framework for this example, but you could choose Python (FastAPI/Flask) or Java (Spring Boot) similarly. The emphasis is on how Copilot assists with the coding. Step 1: Referring to TMF Open API GitHub API specifications Before coding, ensure you have the TMF629 API specification open or accessible for reference. For example, the spec might say there’s a GET operation at /tmf-api/customerManagement/v5/customer/{id} for retrieving a customer, and defines a Customer data model. If you have the YAML/JSON file, open it in a VS Code tab – this provides Copilot with a bunch of context (resource paths, field names, etc.). Copilot can use this textual context to inform its suggestions. The spec files can be downloaded from below link (needs a TM Forum registration and login): Customer Management API REST API v5.0 Open API Directory (Link for all API specifications) Step 2: Set up the project scaffolding. Initialize a new Node.js project (e.g., run npm init -y for a Node project, and install Express by running npm install express). Then create a file index.js (or app.js). In that file, start with the basic Express server setup: const express = require('express'); const app = express(); app.use(express.json()); // Start server on port 3000 app.listen(3000, () => { console.log('TMF Customer API service is running on port 3000'); }); As you type the above, Copilot may autocomplete parts of it. For instance, after writing app.listen(3000, () => {, you might see it suggest a console.log line. It’s standard boilerplate, so nothing magical yet, but it confirms Copilot is active. Step 3: Implement an API endpoint using Copilot. Consider the TMF629 Customer Management API Customer Management API TMF629-v5.0 Now, according to the TMF specification, the GET Customer by ID endpoint should be something like: GET https://host:port/tmf-api/customerManagement/v5/customer/{customerId} -> returns customer details. Let’s write a handler for this. Start typing the Express route definition. For example: // GET customer by ID app.get('/tmf-api/customerManagement/v5/customer/:id', (req, res) => { // }); The moment you write the path string and arrow function, Copilot is likely to recognize this as a request handler and may suggest code inside. It has context from the route path (which is quite specific and likely uncommon except from the TMF spec) and the comment. Copilot might suggest something like: fetching the customer by ID from a database or returning a placeholder. Since we haven’t defined a database in this simple scenario, let’s see what it does. Often, for a new route, Copilot might guess you want to send a response. It could for example suggest: // ... inside the handler: const customerId = req.params.id; // TODO: fetch customer from database (this is a Copilot suggestion comment) res.status(200).json({ id: customerId, name: "Sample Customer" }); }); Of course, this is just an example of what Copilot might do. In practice Copilot may complete the code differently. The key is that Copilot can help stub out the logic. If it doesn’t automatically fill it, you can nudge it by writing a comment or function description inside the handler, such as: // Find customer by ID and return as JSON After writing that comment, pause and see if Copilot suggests a code block that finds a customer. If we had more context (like a Customer array or database connector imported), it might try to use it. For now, you can accept a basic implementation (like returning a dummy object as above). Accepting the suggestion, our route becomes: // GET customer by ID app.get('/tmf-api/customerManagement/v5/customer/:id', (req, res) => { const customerId = req.params.id; // For demo, return a dummy customer object res.json({ id: customerId, name: "John Doe", status: "ACTIVE" }); }); Here we assumed Copilot suggested returning an object with some fields. If the TMF spec defines fields for a Customer (e.g., name, status), and especially if the spec file is open in another tab, Copilot might use actual field names from the spec in its suggestion because it “saw” them in the YAML. This is a huge win: it helps ensure your code uses correct field names and structure as per the standard. For instance, if the spec says a Customer resource has id, name, status, Copilot might include those. Always verify against the spec, but it often aligns. You continue this way for other operations (PUT/PATCH to update a customer, etc.), each time leveraging Copilot to write the initial code which you then adjust. Copilot can also help with non-HTTP logic: for example, if you need a function to validate an email address, just write the function signature and a comment, and it will likely fill it in (because such patterns are common in its training). Step 5: Use Copilot for documentation and examples. Copilot can even assist in writing documentation-like content or tests for your API. For instance, you could create a README.md for your project. Step 6: Iterate and refine with Copilot Chat (if available). GitHub Copilot includes a Chat mode (Copilot Chat) in VS Code, which acts like an assistant you can converse with in natural language. If you have Copilot Chat enabled, you can ask it things like “How do I implement pagination in this API according to TMF guidelines?” or “Suggest improvements for error handling in my code”. The chat can analyze your code base and provide guidance or even write code snippets to apply. GitHub Copilot provides the capability to choose your own model (e.g. GPT-4.1, GPT-4o, GPT-5 or Claude 3.5 Sonnet, etc.). This provides additional flexibility to Telco developers building solutions on TM Forum (TMF) Open APIs. This flexibility means developers aren’t limited to one generic AI assistant – they can select the model best suited to each coding task, whether for rapid code suggestions or complex problem-solving. Step 7: Test and validate against the TMF spec. Once you have your endpoints coded with Copilot’s help, it’s crucial to test them against the TMF specification to ensure correctness. Use tools like Postman or curl to call your API endpoints. For instance, GET http://localhost:3000/tmf-api/customerManagement/v5/customer/123 should return either a dummy customer (if using in-memory data as above) or a 404 if not found, as per spec expectations. Compare response structures to the TMF API definition. If something is missing or named incorrectly (say Copilot used customerName but spec expects name), adjust your code accordingly. Copilot is not guaranteed to produce 100% correct or updated spec implementations – it provides a helpful draft, but you are responsible for aligning it exactly with TMF’s definitions. During testing, you might encounter bugs or mismatches. This is another point where Copilot can assist: if you get an error or exception, you can paste it into Copilot Chat or as a comment and prompt Copilot to help fix it. For example, if you see your server crashes on a null reference, you can write a comment // Copilot: fix null reference in customer lookup near the code, and it might suggest a null-check. Best Practices and Tips for Using Copilot with TMF Open APIs To use GitHub Copilot efficiently for TMF Open API development, follow these key practices: Apply Copilot for Repetitive Tasks: When implementing endpoints with similar logic (e.g., CRUD operations), use an initial example as a template. Copilot will recognise patterns and help adapt code for new entities. Prompt Clearly and Iterate: Refine prompts to get better suggestions; add specifics in comments for improved results. If output isn't right, adjust your instructions for more detail. Verify Against TMF Standards: Copilot's knowledge may not reflect the latest TMF specs. Double-check generated code against official documentation and provide context from newer specs when necessary. Incorporate Security and Quality Checks: Always validate Copilot’s code for security and proper input handling. Use Copilot Chat for advice on improving validation and ensure you meet industry standards (e.g., OAuth). Learn From Suggestions: Use Copilot to expand your skills, especially if you're new to a language or framework, but confirm that its examples suit your use case. Don’t Over Rely on Automation: Copilot is best for boilerplate and common patterns; customise business logic and architecture-specific code yourself. Keep Relevant Files Open: Copilot works best with focused context. Close unrelated files to improve suggestion quality. Update Copilot Regularly: Keep your extension up-to-date and try different AI models for improved performance. Following these principles will help make Copilot a productive partner in TMF Open API projects, offering speed while maintaining adherence to standards. CSPs Leveraging GitHub Copilot Multiple Telco customers across the globe have adopted GitHub Copilot and have achieved a significant boost in their developer productivity. In particular, Proximus has achieved below productivity benefits by adopting GitHub Copilot in their Network IT function. Code Test Write Code Refactor Code Documentation Code Review Code Compliance Unit Test ↑20-30% ↑25-35% ↑80 - 90% ↑5-10% ↑40 – 50% ↑20-30% More details here: (2) Transforming Telecommunications with Generative AI: Proximus and TCS's GitHub Copilot Journey | LinkedIn Other Telco Customer Stories NOS empowers developer collaboration and innovation on GitHub | Microsoft Customer Stories Orange: creating value for its lines of businesses in the age of generative AI with Azure OpenAI Service and GitHub Copilot | Microsoft Customer Stories With GitHub, Canadian company TELUS aims to bring ‘focus, flow and joy’ to developers - Source https://github.com/customer-stories/telus Lumen Technologies accelerates dev productivity, sees financial gains with GitHub Copilot, Azure DevOps, and Visual Studio | Microsoft Customer Stories Vodafone What's Next? Agent mode to autonomously complete tasks Telco developers can boost productivity with GitHub Copilot’s Agent Mode, which acts as an autonomous coding partner. Agent Mode handles multi-step coding tasks—such as implementing TMF Open API flows—reducing manual effort and speeding up feature delivery. It automates complex processes like file selection, testing, and error correction, allowing developers to concentrate on higher-level design while routine tasks run in the background. Write and execute test plans GitHub Copilot Chat can quickly generate test plans. Acting as an AI pair-tester, Copilot produces unit tests from your existing code or specs. Telco developers can highlight a method, request test generation, and instantly receive comprehensive test suggestions for different scenarios. Conclusion Setting up GitHub Copilot for TMF Open API projects streamlines productivity. This blog covered Copilot’s setup, its application to TMF-compliant services, and provided best practices like offering context and reviewing AI-generated code. Copilot speeds up development by handling boilerplate and suggesting standard patterns so you can focus on business logic. It fits seamlessly into your workflow, producing helpful suggestions when guided with clear specs and prompts. Developers report saving time and reducing complexity. Still, Copilot shouldn’t replace understanding TMF APIs or good engineering habits; always verify code accuracy. Combining your expertise with Copilot’s capabilities leads to efficient, high-quality implementations. Explore features like Copilot CLI and keep up-to-date via TM Forum resources, including the Open API Table and community forums. With the right setup and practices, you’re ready to develop robust TMF Open API apps, leveraging AI for faster results.Fix Broken Migrations with AI Powered Debugging in VS Code Using GitHub Copilot
Data is at the heart of every application. But evolving your schema is risky business. One broken migration, and your dev or prod environment can go down. We've all experienced it: mismatched columns, orphaned constraints, missing fields, or that dreaded "table already exists" error. But what if debugging migrations didn’t have to be painful? What if you could simply describe the error or broken state, and AI could fix your migration in seconds? In this blog, you’ll learn how to: Use GitHub Copilot to describe and fix broken migrations with natural language Catch schema issues like incorrect foreign keys before they block your workflow Validate and deploy your database changes using GibsonAI CLI Broken migrations are nothing new. Whether you're working on a side project or part of a large team, it’s all too easy to introduce schema issues that can block deployments or corrupt local environments. Traditionally, fixing them means scanning SQL files, reading error logs, and manually tracking down what went wrong. But what if you could skip all that? What if you could simply describe the issue in plain English and AI would fix it for you? That’s exactly what GitHub Copilot let you do, right from within VS Code. What You Need: Visual Studio Code Installed Account in GitHub Sign up with GitHub Copilot GibsonAI CLI installed and logged in Let’s Break (and Fix) a Migration: Here’s a common mistake. Say you create two tables: users and posts. CREATE TABLE users ( id UUID PRIMARY KEY, name TEXT, email TEXT UNIQUE ); CREATE TABLE posts ( id UUID PRIMARY KEY, title TEXT, user_id UUID REFERENCES user(id) ); The problem? The posts table refers to a table called user, but you named it users. This one-word mistake breaks the migration. If you've worked with relational databases, you’ve probably run into this exact thing. Just Ask a GitHub Copilot: Instead of troubleshooting manually, open Copilot Chat and ask: “My migration fails because posts.user_id references a missing user table. Can you fix the foreign key?” Copilot understands what you're asking. It reads the context and suggests the fix: CREATE TABLE posts ( id UUID PRIMARY KEY, title TEXT, user_id UUID REFERENCES users(id) ); It even explains what changed, so you learn along the way. Wait — how does Copilot know what I mean? GitHub Copilot is smart enough to understand your code, your errors, and even what you’re asking in plain English. It doesn’t directly connect to GibsonAI. You’ll use the GibsonAI CLI for that, but Copilot helps you figure things out and fix your code faster. Validating with GibsonAI Once Copilot gives you the fixed migration, it’s time to test it. Run: gibson validate This checks your migration and schema consistency. When you're ready to apply it, just run: gibson deploy GibsonAI handles the rest so no broken chains, no surprises. Why This Works Manual debugging of migrations is frustrating and error prone. GibsonAI with GitHub Copilot: Eliminates guesswork in debugging You don’t need to Google every error Reduces time to fix production schema issues You stay in one tool: VS Code You learn while debugging Whether you're a student learning SQL or a developer on a fast moving team, this setup helps you recover faster and ship safer. Fixing migrations used to be all trial and error, digging through files and hoping nothing broke. It was time-consuming and stressful. Now with GitHub Copilot and GibsonAI, fixing issues is fast and simple. Copilot helps you write and correct migrations. GibsonAI lets you validate and deploy with confidence. So next time your migration fails, don’t panic. Just describe the issue to GitHub Copilot, run a quick check with GibsonAI, and get back to building. Ready to try it yourself? Sign up atgibsonai.com Want to Go Further? If you’re ready to explore more powerful workflows with GibsonAI, here are two great next steps: GibsonAI MCP Server – Enable Copilot Agent Mode to integrate schema intelligence directly into your dev environment. Automatic PR Creation for Schema Changes – The in-depth guide on how to automate pull requests for database updates using GibsonAI. Want to Know More About GitHub Copilot? Explore these resources to get the most out of Copilot: Get Started with GitHub Copilot Introduction to prompt engineering with GitHub Copilot GitHub Copilot Agent Mode GitHub Copilot Customization Use GitHub Copilot Agent Mode to create a Copilot Chat application in 5 minutes Deploy Your First App Using GitHub Copilot for Azure: A Beginner’s Guide That's it, folks! But the best part? You can become part of a thriving community of learners and builders by joining the Microsoft Student Ambassadors Community. Connect with like minded individuals, explore hands-on projects, and stay updated with the latest in cloud and AI. 💬 Join the community on Discord here and explore more benefits on the Microsoft Learn Student Hub.Deploy Your First App Using GitHub Copilot for Azure: A Beginner’s Guide
Deploying an app for the first time can feel overwhelming. You may find yourself switching between tutorials, scanning documentation, and wondering if you missed a step. But what if you could do it all in one place? Now you can! With GitHub Copilot for Azure, you can receive real time deployment guidance without leaving the Visual Studio Code. While it won’t fully automate deployments, it serves as a step-by-step AI powered assistant, helping you navigate the process with clear, actionable instructions. No more endless tab switching or searching for the right tutorial—simply type, deploy, and learn, all within your IDE i.e. Visual Studio Code. If you are a student, you have access to exclusive opportunities! Whether you are exploring new technologies or experimenting with them, platforms like GitHub Education and the Microsoft Learn Student Hub provide free Azure credits, structured learning paths, and certification opportunities. These resources can help you gain hands-on experience with GitHub Copilot for Azure and streamline your journey toward deploying applications efficiently. Prerequisites: Before we begin, ensure you have the following: Account in GitHub. Sign up with GitHub Copilot. Account in Azure (Claim free credits using Azure for Students) Visual Studio Code installed. Step 1: Installation How to install GitHub Copilot for Azure? Open VS Code, in the leftmost panel, click on Extensions, type – ‘GitHub Copilot for Azure’, and install the first result which is by Microsoft. After this installation, you will be prompted to install – GitHub Copilot, Azure Tools, and other required installations. Click on allow and install all required extensions from the same method, as used above. Step 2: Enable How to enable GitHub Copilot in GitHub? Open GitHub click on top rightmost Profile pic, a left panel will open. Click on Your Copilot. Upon opening, enable it for IDE, as shown in the below Figure. Step 3: Walkthrough Open VSCode, and click on the GitHub Copilot icon from topmost right side. This will open the GitHub Copilot Chat. From here, you can customize the model type and Send commands. Type azure to work with Azure related tasks. Below figure will help to locate the things smoothly: Step 4: Generate Boilerplate Code with GitHub Copilot Let’s start by creating a simple HTML website that we will deploy to Azure Static Web Apps Service. Prompt for GitHub Copilot: Create a simple "Hello, World!" code with HTML. Copilot will generate a basic structure like this: Then, click on "Edit with Copilot." It will create an index.html file and add the code to it. Then, click on "Accept" and modify the content and style if needed before moving forward. Step 5: Deploy Your App Using Copilot Prompts Instead of searching for documentation, let’s use Copilot to generate deployment instructions directly within Visual Studio Code. Trigger Deployment Prompts Using azure To get deployment related suggestions, use azure in GitHub Copilot’s chat. In the chat text box at the bottom of the pane, type the following prompt after azure, then select Send (paper airplane icon) or press Enter on your keyboard: Prompt: azure How do I deploy a static website? Copilot will provide two options: deploying via Azure Blob Storage or Azure Static Web App Service. We will proceed with Azure Static Web Apps, so we will ask Copilot to guide us through deploying our app using this service. We will use the following prompt: azure I would like to deploy a site using Azure Static Web Apps. Please provide a step-by-step guide. Copilot will then return steps like: You will receive a set of instructions to deploy your website. To make it simpler, you can ask Copilot for a more detailed guide. To get a detailed guide, we will use the following prompt: azure Can you provide a more detailed guide and elaborate on GitHub Actions, including the steps to take for GitHub Actions? Copilot will then return steps like: See? That’s how you can experiment, ask questions, and get step-by-step guidance. Remember, the better the prompt, the better the results will be. Step 6: Learn as You Deploy One of the best features of Copilot is that you can ask follow-up questions if anything is unclear—all within Visual Studio Code, without switching tabs. Examples of Useful Prompts: What Azure services should I use with my app? What is GitHub Actions, and how does it work? What are common issues when deploying to Azure, and how can I fix them? Copilot provides contextual responses, guiding you through troubleshooting and best practices. You can learn more about this here. Conclusion: With GitHub Copilot for Azure, deploying applications is now more intuitive than ever. Instead of memorizing complex commands, you can use AI powered prompts to generate deployment steps in real time and even debug the errors within Visual Studio Code. 🚀 Next Steps: Experience with different prompts and explore how Copilot assists you. Try deploying more advanced applications, like Node.js or Python apps. GitHub Copilot isn’t just an AI assistant, it’s a learning tool. The more you engage with it, the more confident you’ll become in deploying and managing applications on Azure! Learn more about GitHub Copilot for Azure: Understand what GitHub Copilot for Azure Preview is and how it works. See example prompts for learning more about Azure and understanding your Azure account, subscription, and resources. See example prompts for designing and developing applications for Azure. See example prompts for deploying your application to Azure. See example prompts for optimizing your applications in Azure. See example prompts for troubleshooting your Azure resources. That's it, folks! But the best part? You can become part of a thriving community of learners and builders by joining the Microsoft Learn Student Ambassadors Community. Connect with like-minded individuals, explore hands-on projects, and stay updated with the latest in cloud and AI. 💬 Join the community on Discord here.Use GitHub Copilot Agent Mode to create a Copilot Chat application in 5 minutes
GitHub Copilot just got an upgrade—Agent Mode is here. 🚀 With GitHub Copilot Agent Mode, you can build applications faster than ever. In just 5 minutes, you can create a Facebook Messenger-style Copilot Chat app using Flask and GPT-4o. Simply upload a screenshot, prompt Copilot, and watch it generate your code. Fine-tune your UI, integrate GitHub Models, and deploy effortlessly. The future of AI-powered development is here—everyone can program. Are you ready to try it?Utilizando Slash commands en GitHub Copilot para Visual Studio
En este blog, demostraremos más información de los comandos de barra diagonal (slash commands). Como los llama Bruno Capuano en un video, "pequeños hechizos mágicos"; en otras palabras, al escribir una barra diagonal (/) en un símbolo del sistema de GitHub Copilot, se abre una opción en la que puede elegir algunos comandos que tendrán una acción predefinida. [Blog original en inglés creado por Laurent Bugnion y Bruno Capuano] Abriendo el menú de los comandos “Slash” Para abrir el menú de comandos de barra diagonal, puedes hacer clic en el botón Barra diagonal dentro de la ventana de chat de GitHub Copilot, como se muestra en la imagen inferior. Otra opción es simplemente escribir una barra diagonal en el área de GitHub Copilot Chat. Cualquiera de las dos acciones abrirá el menú que se ve así: Repasemos los comandos: doc: Este comando ayuda a crear un comentario de documentación relacionado con la selección determinada. Por ejemplo, si el cursor está dentro de un método, GitHub Copilot propondrá un comentario para este método. exp: Este comando comienza un nuevo hilo de conversación, con un contexto completamente nuevo. Después, puedes cambiar entre conversaciones desde un cuadro combinado en la parte superior de la ventana de chat. explain: Este comando explicará una parte del código. Si seleccionas un código, GitHub Copilot te explicará este código. También puedes utilizar el comando # para especificar un contexto diferente. fix: Este comando propondrá una corrección para el código seleccionado. generate: Este comando generará un código correspondiente a la pregunta que acabas de hacer. help: Este comando mostrará ayuda sobre GitHub Copilot. optimize: Este comando analizará el código en contexto y propondrá una optimización (en términos de rendimiento, líneas de código, etc.). tests: Este comando creará una prueba unitaria para el código seleccionado. Obtendremos más detalles sobre cada uno de estos comandos en futuras publicaciones. Más información Como siempre, puedes encontrar más información, en nuestra colección de Microsoft Learn. Mantente al tanto de este blog para obtener más contenido. Y, por supuesto, ¡también puedes suscribirte a nuestro canal de YouTube!Creación de pruebas con GitHub Copilot para Visual Studio
[Blog original escrito en inglés por Laurent Bugnion y Bruno Capuano] En nuestra industria, es común que nosotros los programadores enfrentemos diferentes desafíos como: documentar código y crear pruebas unitarias. GitHub Copilot puede ser de gran ayuda en estas áreas, facilitando y mejorando estos procesos. Los comandos de barra (slash), "hechizos mágicos" para Visual Studio Estos pueden aparecer escribiendo la barra diagonal '/' (slash command) en el chat de GitHub Copilot en Visual Studio. Luego, se abrirá un menú donde puede seleccionar un comando, por ejemplo, /fix para arreglar algún código, o /optimize. También tenemos /doc que, como su nombre indica, te ayudara a crear documentación para el código seleccionado, por ejemplo, un método o una propiedad. El último comando que se muestra en el screenshot anterior es /tests. Este es especialmente útil para ayudarle a empezar a trabajar con las pruebas unitarias para el código actual en contexto. En el video corto (en inglés) que publicó Laurent, Bruno Capuano muestra cómo GitHub Copilot puede proponer pruebas unitarias para toda una clase. Para hacer eso, Bruno comienza escribiendo /tests en la ventana de chat de Copilot, y luego escribe un hash '#' que abrirá otro menú contextual para seleccionar el contexto. La importancia del contexto Para los modelos de LLM, el contexto es crucial para la correcta ejecución de las solicitudes. El contexto se ingresa en el prompt junto con la entrada del usuario, lo que se conoce como metaprompts. En otras palabras, cualquier información que ayude al LLM a generar resultados más precisos es útil. En GitHub Copilot para Visual Studio, el contexto puede ser: el código seleccionado, otro archivo de programación o incluso toda la solución. Estas opciones se pueden seleccionar desde el ‘hash context menú”, como se muestra aquí: Creando las pruebas Una vez que se ejecute este comando, el chat de Copilot propondrá una sugerencia de cómo podrían ser las pruebas. Ten en cuenta que, al igual que con todas las demás características de GitHub Copilot, esto está destinado a ayudarte a ti, el programador a cargo, y no solo a hacer el trabajo por ti. El código creado debe ser verificado y aprobado por ti, y probado exhaustivamente para asegurarte de que realmente hace lo que esperaba (sí, vas a probar las pruebas unitarias). GitHub Copilot te ayuda al hacer más rápido el proceso y más eficiente, pero no está reemplazando tu función de verificar el código. La diferencia aquí, en comparación con las características anteriores que mostramos, es que GitHub Copilot propone crear las pruebas en un nuevo archivo. Esto es lo que normalmente hacemos para las pruebas unitarias, con clases y métodos dedicados. Asimismo, esto está disponible aquí con el botón Insertar en un nuevo archivo. Una vez creado el nuevo archivo, debes guardarlo en la solución. De hecho, es probable que desees organizar las pruebas en una biblioteca de clases independiente en la misma solución. Más información Laurent y Bruno han recolectado una lista de recursos aquí y esperamos que esta información sea útil para que empieces y mejores tus habilidades en Visual Studio. También puedes ver un video relacionado de 13 minutos aquí, y ver otros videos de la serie aquí. Mantente atento a más contenido suscribiéndote a este blog y al canal de YouTube de Visual Studio.¿Cómo instalar GitHub Copilot en Visual Studio?
[Blog original en inglés] GitHub Copilot es un asistente de programación impulsado por Inteligencia Artificial (IA) que puede ejecutarse en varios entornos, ayudándote a ser más eficiente en tus tareas diarias de programación. En este blog, te mostraremos específicamente cómo funciona GitHub Copilot en Visual Studio y cómo puede aumentar tu productividad. Comprendiendo la diferencia entre GitHub Copilot y GitHub Copilot Chat: GitHub Copilot funciona directamente en tus archivos de código, proporcionando sugerencias para tu código. Funciona de manera similar a IntelliSense, pero es capaz de proponer bloques completos de código en base a lo que estás escribiendo. También proporciona acceso a comandos, puede explicar el código y ofrecer funciones adicionales directamente respecto a tus archivos. GitHub Copilot Chat funciona en una ventana independiente dentro del entorno de Visual Studio. Proporciona un asistente de chat que puede recordar el contexto de la conversación y ofrecer sugerencias inteligentes. Ambas extensiones se pueden instalar por separado. Te recomendamos probar ambas para que puedas elegir la que prefieras. En próximas oportunidades, te mostraremos más detalles sobre cada una de estas extensiones. Instalando las extensiones de GitHub Copilot Ambas extensiones se pueden instalar directamente desde Visual Studio, a través del menú Extensiones / Administrar extensiones. Desde allí, busca GitHub Copilot y GitHub Copilot Chat. También puedes dirigirte a Visual Studio Marketplace, que contiene una gran cantidad de extensiones para mejorar tu experiencia con Visual Studio. Ten en cuenta que GitHub Copilot requiere Visual Studio 2022 17.5.5 o posterior. Más información Para obtener más información, consulta nuestra colección de recursos aquí. Mantente al tanto de este blog para más contenido sobre Visual Studio. Y, por supuesto, ¡también puedes suscribirte a nuestro canal de YouTube para más contenido!
