How Semantic Kernel Is Changing the Game for Low-Code AI Development

At Microsoft Build 2025, AI and automation is in the spotlight, introducing a new era of intelligent agents and low-code development. Microsoft announced a wave of upgrades to its Copilot ecosystem – especially Copilot Studio, the platform for building custom AI copilots – aimed at making it easier for businesses to create powerful AI-driven solutions. These developments come as AI adoption surges: over 230,000 organizations (including 90% of Fortune 500 companies) have already used Copilot Studio to build AI agents and automation workflows. From Semantic Kernel-powered plugins to multi-agent systems, Build showcased how Microsoft is fusing low-code ease with cutting-edge AI to transform enterprise software development.

Copilot Studio is Microsoft’s low-code/no-code environment for crafting AI copilots (intelligent agents) that can automate tasks and assist users across Microsoft 365 and beyond. Build 2025’s announcements reinforce Copilot Studio as a central hub for enterprise AI development. In this article, we’ll explore the top Copilot Studio updates from Build 2025 – including multi-agent orchestration, maker controls, and end-user copilot enhancements – with a special focus on Semantic Kernel’s role in revolutionizing low-code AI. We’ll also discuss why these innovations are strategically important for enterprises, and how Digital Bricks can help organizations capitalize on Microsoft’s AI ecosystem. If you want to learn how to leverage Copilot Studio, it's included in our Key User's Guide to Microsoft Copilot E-learning.

Microsoft introduced several Copilot Studio enhancements at Build 2025 designed to empower both “makers” (low-code developers) and professional developers to build more capable, compliant, and interconnected AI agents. Below, we break down the most significant updates and what they mean:

Multi-Agent Orchestration: AI Agents Working as a Team

One headline feature is multi-agent orchestration, which allows organizations to build AI agent teams that can cooperate to accomplish complex tasks. Rather than relying on a single copilot to handle everything or keeping bots isolated in silos, Copilot Studio (now in preview) enables multiple agents to delegate tasks to each other and work in concert. These agents – whether built in Microsoft 365, Azure AI Foundry, or even new Microsoft Fabric – can now share data and invoke each other’s skills to achieve a shared goal, completing business-critical processes that span multiple systems, teams, and workflows.

For example, an enterprise could connect a sales copilot, a document drafting copilot, and a scheduling copilot to automate an end-to-end process. Imagine a Copilot Studio agent pulls customer data from a CRM, then hands off to a Microsoft 365 Copilot agent to draft a proposal in Word, and finally triggers an Outlook scheduling agent to set up follow-up meetings. In another scenario, agents across IT, HR, and Marketing might collaborate to onboard a new employee smoothly. In essence, multi-agent orchestration brings greater connectedness and scale – agents can operate in sync, each focusing on what it’s best at, to drive complex workflows forward. This capability is currently in private preview (with public preview coming soon), hinting that enterprise-grade AI agent ecosystems are on the horizon.

Importantly, Microsoft’s approach emphasizes human oversight and secure coordination. Agents exchange data and requests through a governed protocol, ensuring that as they “team up,” they respect security boundaries and organizational policies. This multi-agent design aligns with Microsoft’s vision of an “open agentic web” where AI agents seamlessly work on behalf of users and organizations. For enterprise leaders, the message is clear: your various AI assistants should not live in isolation – connected AI agents can tackle broader, cross-domain challenges, amplifying productivity in ways a single bot could not.

Maker Controls and Knowledge Integration: Fine-Tuning Your Copilots

Build 2025 also delivered new maker controls in Copilot Studio, giving low-code developers more dials and knobs to fine-tune how AI agents behave, reason, and interact. Now in public preview, the Generative AI settings in Copilot Studio include toggles for features like generative orchestration and deep reasoning, plus multiple categories of options to further “ground” and tailor an agent’s responses. In practice, this means makers can decide how an AI agent uses its knowledge and tools to answer queries. With generative orchestration enabled, an agent can intelligently choose among its available topics, actions, and knowledge sources to craft the best response (rather than simply matching predefined trigger phrases). The deep reasoning setting, meanwhile, allows agents to carry out more complex, multi-step reasoning processes – executing sophisticated business logic or multi-faceted analyses – to handle tougher requests.

Makers now have granular control over an agent’s knowledge sources and response style. Copilot Studio’s new features let you upload multiple files into a file collection and use them as a single knowledge base for an agent – for instance, a set of policy documents or product manuals the copilot can draw from. You can also instruct the agent on how to pick the most relevant document from that collection to ground each answer. In the Responses configuration, makers can choose the AI’s primary model (e.g., a specific OpenAI GPT version or a fine-tuned model), set response length preferences, and even provide custom instructions to shape the tone or format of replies. Notably, Copilot Studio now offers an advanced option for code interpreter and tenant graph grounding – meaning the agent can run Python code to analyze data or generate charts, and perform semantic searches over enterprise data via Microsoft Graph, respectively. We’ll discuss the new code interpreter in detail shortly.

Moderation and user feedback controls have also been added. Makers can define how the agent should handle any AI-generated response flagged for potential policy or content violations (for example, sending a custom apology or escalation message). They can also enable end-user feedback prompts and include a disclaimer, so that employees using the copilot can rate responses or flag issues – providing valuable insight to improve the agent over time. Additionally, Copilot Studio allows configuring whether the agent should answer using only the organization’s private knowledge sources, rely on the base AI model’s general knowledge, or even tap into Bing web search when appropriate. Makers can permit users to upload images during conversations if image-based queries are relevant (for example, asking a copilot to analyze a chart screenshot).

These maker-centric enhancements empower organizations to deliver more relevant, safe, and high-quality copilots. With richer tuning, enterprise AI agents can be customized to company policies, industry jargon, and specific workflows – all without writing code. This level of control is crucial for businesses that need AI to be accurate and on-brand. It also helps address compliance and risk concerns by letting IT admins and makers set boundaries on what data the AI can use and how it responds in sensitive scenarios. In short, Microsoft is giving low-code developers the toolkit to shape AI behavior in a governed way, which can increase trust and effectiveness of Copilot solutions.

Copilot Studio dramatically expanded the range of knowledge sources that agents can leverage. Beyond Dataverse and SharePoint content, agents can now directly reference OneDrive files and folders, SharePoint lists, and Microsoft Teams chats/channels – tapping into the everyday content where employees collaborate. Microsoft also announced connectors for popular third-party systems: agents can use unstructured data from platforms like Salesforce, ServiceNow, Zendesk, and fetch structured data from sources like Snowflake, Databricks, or SAP. Even Azure Cognitive Search indexes can be linked, enabling richer enterprise search capabilities in responses. (Microsoft’s existing Graph Connectors have been rebranded as Copilot connectors, reflecting their new role in powering AI copilots. Over 65 connectors are now available, spanning services like Gong, PagerDuty, and more.) The takeaway for IT leaders is that Microsoft’s AI agents are no longer limited to a narrow set of data – they can become truly knowledge-aware copilots that draw insight from across your enterprise data estate, as well as external SaaS apps, all with proper security and compliance in place.

Code Interpreter and Pro-Code Extensibility

While Copilot Studio is a low-code platform at heart, Microsoft recognizes that enterprise scenarios sometimes demand advanced logic or data processing beyond drag-and-drop capabilities. To address this, Build 2025 introduced features that bridge low-code and pro-code development, ensuring that power users and software engineers can extend Copilot Studio solutions with custom code when needed.

One standout addition is the new Code Interpreter feature for Copilot Studio agents. Inspired by the code execution capabilities popularized by tools like GitHub Copilot and OpenAI’s Code Interpreter, this feature lets AI agents dynamically generate and run Python code to solve problems or transform data on the fly. For example, if a user asks an agent to analyze a sales dataset or produce a chart, the copilot can now create Python scripts at runtime to perform the analysis and return results – complete with visualizations or calculations – within the conversation. Agents can analyze CSV/Excel files, create charts (line, bar, pie graphs with downloadable outputs), and tackle complex math or data transformations in context, by executing code behind the scenes.  All of this occurs within the managed Copilot Studio environment, so the code runs securely sandboxed.

There are two modes for using the code interpreter: a dynamic mode (the agent writes and executes Python as needed in response to user prompts) and a design-time mode via the Prompt Builder (makers can pre-write a Python snippet as part of an agent’s dialog flow for routine tasks). The latter is great for repeatable operations – for instance, automating CRUD actions on Dataverse tables or performing a standard data cleanup whenever a certain query is run. By bridging natural language prompts with real code execution, code interpreter unlocks new data analysis and reporting abilities inside Copilot Studio, helping teams get more insightful answers and automate complex processes. Early-adopter organizations can expect improved solution quality and faster turnaround on data-heavy tasks, since the AI can directly manipulate data rather than rely on static logic. For tech-savvy executives, this means your business analysts could ask a copilot to “show me a trend of our quarterly sales” and get a dynamically generated chart, all without waiting on a data scientist – the AI agent handles the heavy lifting within approved guardrails.

Copilot Studio’s new code interpreter feature allows AI agents to run real Python code for on-the-fly analysis and visualizations. In this example, the copilot writes a Python script to generate a line chart of temperature trends using an uploaded CSV file‍. This bridges low-code prompts with pro-code computation, enabling richer insights directly within a copilot’s conversation.

For professional developers, Microsoft also rolled out a Visual Studio Code extension for Copilot Studio, bringing the best of pro-code tooling to the low-code agent world. Available via the VS Code marketplace, this extension lets developers connect to Copilot Studio and edit their AI agents’ configurations, prompts, and logic using VS Code’s familiar interface. In practice, you can pull your Copilot Studio project into VS Code, leverage IntelliSense, version control, “find all references,” and other advanced editing features while building your agent – then push changes back to Copilot Studio’s managed cloud environment. This is a boon for dev teams that want the agility of low-code with the rigor of traditional development. There’s no need to retrain developers on a new tool; they can use their existing workflows and even integrate with GitHub CI/CD pipelines. Microsoft’s goal is to meet developers where they are: whether you prefer to design in the Copilot Studio web UI or edit as code, you now have the flexibility to do so, without sacrificing the benefits of Copilot Studio’s hosting and compliance. The VS Code integration underscores that low-code and code-first approaches are converging – enterprises can rapidly build AI solutions and still apply software engineering best practices.

Beyond editing, Microsoft introduced Microsoft 365 Copilot APIs and the Agents SDK/Toolkit to let developers extend Copilot functionality or embed Copilot experiences into other applications‍. For instance, developers can use APIs to programmatically call Copilot’s retrieval or chat capabilities from custom apps, or embed the Microsoft 365 Copilot chat interface into a line-of-business app, all while honoring organizational permissions. The Microsoft 365 Agents Toolkit provides project templates and libraries to streamline building enterprise-grade agents that can run in multiple channels (Copilot in Teams, web, etc.). This means if your dev team wants to build a specialized copilot for, say, Teams meetings or Dynamics 365, they have the scaffolding and tools to do it efficiently and securely. All these pro-code enhancements ensure that if the out-of-the-box low-code features ever fall short for a complex requirement, developers can step in and extend the solution – giving enterprises the best of both worlds in the AI development spectrum.

End-User Copilots and Deployment

The Build 2025 updates not only make it easier to build AI agents; they also focus on delivering these copilots directly to end-users in seamless ways. Microsoft is essentially turning Copilot Studio agents into readily deployable, enterprise-ready AI assistants that users can access in the tools they already use.

One example is the ability to instantly share or publish copilots across the organization. Copilot Studio now lets makers publish an agent to a SharePoint site with one click. Authentication and permissions are handled automatically via Azure AD, so anyone with access to that SharePoint page can start chatting with the embedded copilot immediately – no separate app or complex deployment needed. Coming in July 2025, makers will also be able to publish agents to WhatsApp, enabling organizations to offer conversational AI support or services to users on a familiar, mobile-friendly platform. Imagine a company publishing an internal HR copilot to a SharePoint portal for employees, or a customer-facing FAQ bot to a WhatsApp channel – these options lower the friction to get AI assistance where people already communicate.

Within the Microsoft 365 Copilot experience itself, Microsoft is improving how end-users discover and use agents. A new Agent Store is rolling out, where employees can find both Microsoft-provided agents and custom or partner-built copilots relevant to their work. For instance, Microsoft announced Researcher and Analyst – specialized “reasoning” agents for work – which enterprises can deploy via the Frontier early access program. Users will be able to pin these or other agents (like Jira or Monday.com copilots from partners) from the Agent Store to their Copilot interface. This concept of an internal marketplace means that once your team develops a useful copilot in Copilot Studio, making it available company-wide is straightforward. It also signals that end-users could soon juggle multiple copilots – each an expert in a domain – so Microsoft is smoothing the onboarding and management of those AI helpers.

Perhaps the most intriguing user-centric enhancement is in-conversation agent recommendations. Microsoft 365 Copilot can now intelligently suggest handing off a user’s query to a more specialized agent if it detects that another agent is better suited for the task. For example, an employee asking a generic copilot about a sales report might be prompted to switch to the Sales Insights agent that was built for that purpose. The system carries the full conversation context into the selected agent, so the user experiences a seamless transition and a more relevant answer. This dynamic routing ensures that no AI agent sits idle or hidden – the platform will surface the right copilot at the right time without the user needing to manually find it. For businesses, this means higher ROI on each agent developed: even niche agents become discoverable and usable when their expertise is needed, boosting overall productivity.

Microsoft 365 Copilot’s in-conversation agent recommendation feature in action. In this Copilot Chat example, the system recognizes the user’s request would be better handled by a specialized “Workplace Analytics” agent, and seamlessly hands off the conversation to that agent – carrying over context so the agent can respond intelligently. Such handoffs make AI copilots more accessible and useful for end-users by automatically matching queries to the best agent for the job.

In addition to these, Microsoft has reinforced the security and governance around Copilot Studio deployments. Each agent created via Copilot Studio or Azure AI Foundry can now be automatically assigned a unique Entra ID (Azure AD identity), allowing IT departments to manage AI agents just like they manage human employee identities. This “Entra Agent ID” ensures proper access control, auditing, and helps avoid “agent sprawl” by bringing agents into the organization’s identity management fold. Microsoft is also extending Purview Information Protection to Copilot agents that use Dataverse, so sensitive information can be automatically classified and protected even as the AI works with that data. These governance capabilities are key for enterprises – they mean you can adopt AI agents at scale without losing oversight of data security or compliance.

All told, the Build 2025 improvements make it easier not only to build AI copilots, but also to deploy them widely and responsibly. End-users will benefit from AI assistants that are more present in their everyday tools (Office, Teams, SharePoint, etc.), more context-aware, and tuned to enterprise data. For IT leaders, this opens up possibilities to infuse AI into business processes on every level – from an employee asking an Outlook Copilot to summarize last quarter’s sales, to a customer self-serving via a website chat agent – all built on the same secure Copilot Studio foundation.

Semantic Kernel: The AI Engine Empowering Low-Code Innovation

A pivotal element underpinning many of these advancements is Microsoft’s Semantic Kernel. While it might not always be visible on the surface, Semantic Kernel (SK) is the open-source AI SDK that powers core aspects of Copilot Studio and the broader copilot ecosystem. It provides the orchestration, memory, and plugin framework that allows AI agents to understand natural language, connect to data, and perform complex tasks reliably. In essence, Semantic Kernel is transforming low-code development by injecting sophisticated AI capabilities into simple interfaces.

Designed for versatility, scalability, and easy integration, Semantic Kernel empowers businesses to streamline complex workflows, democratize data access, and accelerate digital transformation. It acts as a bridge between high-level natural language commands and the underlying services or data sources needed to fulfill them. For example, when an agent uses generative orchestration to decide on actions, or when it calls out to a “Copilot connector” to fetch CRM data, it’s Semantic Kernel under the hood managing those AI skills and tool plugins. At Build 2025, Microsoft emphasized Semantic Kernel’s growing role. They announced a unified runtime combining the strengths of Semantic Kernel with their AutoGen system, which lets developers build and test multi-agent systems locally and then deploy them to cloud without changes. This means whether you prototype an AI agent in a sandbox or run it in production, the Semantic Kernel runtime ensures consistent behavior and composability across environments.

For low-code makers, Semantic Kernel’s impact is profound: it enables Copilot Studio to deliver powerful AI features with minimal coding. Thanks to SK, makers can simply toggle “deep reasoning” or “generative orchestration” on, and behind the scenes the AI will break down a complex query into steps, call the right APIs, or chain multiple prompts together to get a result. Makers can add a new data source (say, Salesforce) to an agent, and SK (via plugins/connectors) handles authentication, querying that source, and feeding the information to the language model. Microsoft even built “Copilot plugins” for Microsoft 365 data that developers can utilize through Semantic Kernel, so that smart 365-aware agents can be created with far less code. As noted in the release notes, ready-made plugins tapping into Microsoft 365 data and actions are available through Semantic Kernel, allowing teams to build smarter agents “with far less code.” This significantly lowers the barrier to integrate enterprise systems into your AI workflows – something that traditionally required extensive API programming can now be configured in a low-code manner.

Semantic Kernel also shines when extending Copilot Studio with custom logic. If a business needs an AI agent to perform highly specific computations or integrate with a niche system, developers can write a semantic function or a plugin using SK and plug it into Copilot Studio as a new action. In a recent Microsoft Semantic Kernel blog series, experts described how Semantic Kernel brings sophisticated AI processing and natural language understanding to custom integrations, resulting in more intelligent, context-aware interactions for users. In other words, SK allows pro developers to inject custom AI skills into low-code agents – enabling a “low-code meets pro-code” synergy where you get the best of both. An enterprise can use Copilot Studio’s UI to orchestrate the overall conversation flow, while behind certain nodes, an SK function might execute advanced reasoning or call external APIs securely. This layered approach means no use case is off-limits: even if standard connectors don’t cover it, Semantic Kernel can be the Swiss army knife that developers use to fill the gap with bespoke AI logic, all integrated cleanly into the low-code framework.

From an enterprise IT perspective, Semantic Kernel’s role is strategically important. It essentially standardizes how AI agents connect to data and tools across Microsoft’s ecosystem – whether it’s in Copilot Studio, Azure AI, GitHub Copilot, or Dynamics, SK provides a consistent programming model (sometimes manifested as the Model Context Protocol or the plugin manifest system) for extending AI capabilities. This consistency is a boon for large organizations: your developers can learn Semantic Kernel once and apply those skills to build custom copilots or agents on multiple platforms. Moreover, SK being open-source means there’s a growing community and sample library (the “AI Skills” library) that enterprises can leverage. Microsoft’s adoption of open protocols like MCP (Model Context Protocol) further indicates that the company is fostering an open ecosystem where even non-Microsoft AI tools could interoperate. All these factors reduce the risk of vendor lock-in and increase flexibility for enterprise AI solutions.

In summary, Semantic Kernel transforms low-code AI development by providing the brains and connective tissue that make advanced AI functionality accessible in a drag-and-drop environment. It allows low-code solutions to scale up in sophistication – from reasoning with multiple agents to integrating proprietary data – without requiring each maker to become an AI expert or a cloud architect. As enterprises ramp up AI-driven automation, Semantic Kernel ensures that the solutions can be both highly customized and rapidly developed, a combination that is incredibly valuable in today’s fast-paced, AI-first landscape.

Strategic Impact for Businesses and the Path Forward

Microsoft’s Copilot Studio updates at Build 2025 represent more than just new features – they signal a strategic opportunity for enterprises to leap ahead in AI-driven automation and application development. By blending low-code ease of use with robust AI capabilities (backed by Semantic Kernel and Azure AI), Microsoft is essentially lowering the entry barrier for organizations to build their own AI copilots tailored to their business. Here are key takeaways for enterprise IT leaders and decision-makers:

• Accelerated Innovation with Low-Code AI: Business units and “citizen developers” can use Copilot Studio to spin up prototypes and solutions in days, not months. The new maker controls and knowledge integrations mean these solutions can be rich and context-aware from the start. This agility allows companies to quickly automate manual processes or create new intelligent services, driving innovation at a much lower cost and lead time than traditional software projects.
• AI Agents as Collaborative Team Members: With multi-agent orchestration, businesses can design swarms of specialized agents that mirror the structure of their teams or workflows. Each copilot can handle a segment of a process and hand off tasks to the next. This approach can radically improve efficiency in scenarios like customer onboarding, incident management, or financial reporting – anywhere multiple departments or tools are involved. It’s a step toward AI-assisted operations where routine handoffs between people can be partially or fully managed by AI, under human supervision. Companies that invest in these AI agent ecosystems may gain a competitive edge through faster response times and 24/7 automated operations.
• Personalized and Empowering User Experiences: The enhancements to end-user copilot experiences (like in-context agent suggestions and the Agent Store) mean employees will have AI assistance more pervasively and contextually in their daily work. Instead of hunting through dashboards or waiting on support tickets, users can ask a copilot and get immediate results or be routed to an agent that knows the answer. This not only boosts productivity but can also improve employee satisfaction by reducing frustration and letting them focus on higher-value tasks. Enterprises should see these copilots as digital coworkers that can augment every role – from an HR assistant that helps prepare org announcements, to a finance analyst copilot that digs up insights from ERP data on request.
• Governance, Security, and Compliance Built-in: Microsoft’s integration of Entra ID, compliance controls, and content moderation tools in Copilot Studio addresses one of the biggest concerns for enterprise AI – maintaining control and oversight. IT admins can now manage who or what an AI agent can access, monitor agent interactions, and ensure sensitive data isn’t leaking. This makes it more feasible to deploy AI widely within an organization without stepping into regulatory or security minefields. Enterprises should still apply due diligence (e.g. reviewing AI outputs for bias or errors), but the platform provides guardrails that were previously manual or non-existent. This enterprise-ready posture of Copilot Studio may give more conservative industries (finance, healthcare, government) the confidence to adopt AI copilots where they previously held back.
• Semantic Kernel and the Power of Extensibility: The role of Semantic Kernel means that companies are not locked into whatever vanilla features Copilot Studio offers. If there’s a unique need – connecting to a legacy database, implementing a proprietary algorithm, or enforcing a custom approval step – developers can extend the copilot through SK and Azure AI services. Essentially, any investment in custom AI development (like an NLP model or a knowledge base) can be integrated into the Copilot framework. This extensibility ensures that adopting Copilot Studio doesn’t mean abandoning existing AI assets; rather, it provides a unifying canvas to bring them together with less effort. It’s a future-proofing factor: as new AI models or protocols emerge, Semantic Kernel can adapt to include them, and your low-code solutions can tap into the latest tech without a complete rewrite.

For enterprise customers, the announcements at Build 2025 serve as a clear call to action: now is the time to craft an AI strategy that includes low-code development and multi-agent automation. The technology has matured to a point where you can pilot use cases quickly and scale what works, while keeping governance in check. Whether it’s developing a suite of internal copilots to support employees or building AI-enhanced products for your clients, Microsoft’s ecosystem provides the building blocks. The companies that move early to adopt these tools stand to gain in efficiency, customer satisfaction, and even new revenue streams through AI-powered services.

Conclusion: Embracing AI Innovation with Digital Bricks as Your Partner

Microsoft Build 2025 showcased how Copilot Studio, Semantic Kernel, and the new wave of AI agent capabilities are set to revolutionize low-code development and enterprise automation. From enabling AI agents to collaborate on complex, cross-functional tasks, to giving makers unparalleled control over AI behavior and integration, Microsoft’s advancements paint a future where every enterprise can have a custom fleet of AI copilots driving productivity and innovation. The strategic importance is evident – companies that leverage these tools can streamline operations, make smarter decisions faster, and empower their workforce with AI assistants tailored to their unique needs.

Implementing this vision, however, requires more than just technology – it demands the right expertise and guidance. This is where Digital Bricks comes in. As a certified Microsoft Solutions Partner with deep experience in AI and cloud solutions, Digital Bricks is uniquely positioned to help enterprise customers navigate and capitalize on Copilot Studio’s new capabilities. Whether you’re looking to design your first AI agent, integrate Semantic Kernel plugins with your data, or establish governance for a whole portfolio of copilots, our team can provide architectural guidance, best practices, and hands-on support. We understand the Microsoft ecosystem and how to align it with your business strategy.

In practical terms, partnering with Digital Bricks means you gain a trusted advisor in your AI journey – one who can rapidly prototype solutions, customize them to your industry, and ensure they are deployed securely and compliantly. We help you ask the right questions (Which processes are best suited for AI automation? How do we measure ROI? How do we train our staff to work alongside AI agents?) and then deliver concrete answers with working solutions. The result is a faster path from idea to impact, with fewer pitfalls along the way.

Microsoft’s Build 2025 announcements around Copilot Studio and Semantic Kernel offer a blueprint for the future of low-code AI in the enterprise. It’s a future where technology barriers are lower, but the potential benefits are higher than ever. By embracing these tools now – and partnering with experts like Digital Bricks – forward-thinking organizations can build their own AI copilots confidently, stay ahead of the competition, and unlock new levels of efficiency and innovation. Digital Bricks is ready to help you lay those digital foundations with Microsoft’s latest AI advancements, ensuring your enterprise reaps the rewards of this new era of AI-powered low-code development.

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