Power Platform: New Low-Code Tools

Key insights

• Power Platform overview — This video summarizes four new features that bring AI deeper into Microsoft’s low-code tools.
  It explains why these updates matter for developers, makers, and business teams wanting faster AI-driven apps.
• Dataverse Prompt Columns — New column types let you store and manage AI prompts directly with your data.
  They make prompts reusable, keep prompt logic consistent, and help scale generative scenarios safely across apps.
• Form filler in model-driven apps — Auto-fill capabilities speed data entry and reduce manual errors on complex forms.
  Expect faster onboarding for users and higher data quality in enterprise processes.
• Generative Pages (aka Vibe coding) — Create pages and UI components from natural-language prompts to accelerate app design.
  This lowers the barrier to build interfaces and lets teams prototype faster with AI-assisted layouts and content.
• Copilot Studio Agents calling Agents — Agents can invoke other agents to orchestrate multi-step workflows and hand off tasks programmatically.
  This enables complex automation, clearer responsibility splits, and modular AI services across business flows.
• Get ready — Learn these features now: experiment with prompt columns, test form filler on a sample app, try a generative page, and design simple agent chains.
  Early adoption builds a strong foundation for AI in the Microsoft ecosystem and prepares teams for future updates.

Overview

In a recent YouTube video, Shane Young [MVP] reviews four new capabilities in the Microsoft Power Platform and explains how they could shape future development workflows. The video highlights Dataverse Prompt Columns, a Form filler for model-driven apps, Generative Pages (sometimes called "vibe coding"), and Copilot Studio Agents calling Agents. Shane frames these features as building blocks for integrating AI across the Microsoft ecosystem, and he emphasizes practical scenarios where makers can start experimenting today. Overall, the video aims to make these advances approachable for both individual developers and enterprise teams.

Dataverse Prompt Columns

Shane describes Dataverse Prompt Columns as a way to attach AI-driven prompts to data fields so that contextual content can be generated or suggested directly in tables and views. As a result, users can receive dynamic suggestions or summaries based on the data in a record, which speeds up content creation and reduces repetitive tasks. Moreover, the feature integrates closely with Dataverse security and model logic, so it follows existing data access rules rather than creating new silos. Consequently, teams can prototype AI experiences without rearchitecting their data layer.

However, there are tradeoffs to consider: prompt columns can increase API usage and compute costs when they generate content frequently, and they introduce governance questions about where generated content is stored and who can edit it. Additionally, prompt-driven content can be unpredictable, so monitoring and post-edit workflows are essential to maintain quality and compliance. Therefore, organizations should balance convenience with controls, establishing thresholds for when to generate content and how to verify it. In short, prompt columns speed tasks but require careful observability and cost management.

Form Filler in model-driven apps

The video shows Shane using the new Form filler capability to populate complex forms in model-driven apps, which can dramatically cut data entry time for end users. For example, the tool can infer values from context, suggest completions, and even fill multi-field sections based on a single prompt or existing record relationships. This approach increases productivity for users who routinely handle detailed forms, while also reducing input errors that come from manual typing. As a result, teams can deliver richer user experiences with less front-end code.

On the other hand, form automation introduces challenges such as preserving audit trails and ensuring that auto-filled values meet business rules. If automated inputs bypass validation or are accepted without user review, downstream processes may break or regulatory requirements might be violated. Therefore, designers must implement review steps or incremental acceptance flows, and they should log changes to maintain traceability. Ultimately, the benefit is clear, but operational discipline is necessary to prevent accidental data drift.

Generative Pages (Vibe coding)

Shane highlights Generative Pages, a feature that uses AI to scaffold whole pages and components from simple prompts, a workflow sometimes called "vibe coding." By describing a layout or behavior in plain language, makers can get a working page shell that they can refine, which accelerates prototyping and lowers the bar for builders who are less experienced with code. In practice, this reduces the time between idea and testable experience, enabling faster iteration and clearer conversations with stakeholders. Consequently, teams can explore more design alternatives and validate concepts quickly.

Nevertheless, generated pages are not a substitute for deliberate design and accessibility work, and they may require manual tuning to meet performance and branding standards. In particular, generated UI might include unnecessary components or inefficient markup that affects load times and maintainability. Therefore, teams should treat generative outputs as starting points rather than final artifacts, investing time to optimize and align them with standards. In short, generative pages boost speed, but quality control remains crucial.

Copilot Studio Agents Calling Agents

Finally, Shane explains how Copilot Studio Agents calling Agents enables agent orchestration, where different AI agents can call each other to solve multi-step tasks. This architecture supports complex workflows by delegating subtasks to specialized agents, which can improve reliability and modularity in automated processes. For instance, one agent can handle data retrieval while another focuses on synthesis or content generation, and a supervisory agent coordinates the sequence. As a result, teams gain flexibility in composing capabilities and reusing components across scenarios.

However, agent chaining raises operational tradeoffs such as increased latency, error handling complexity, and the need for robust telemetry to trace execution paths. Furthermore, security boundaries between agents require careful design to avoid unintended data exposure during handoffs. Therefore, organizations should invest in observability, retry logic, and clear interface contracts between agents to ensure resilience. In summary, agent orchestration unlocks powerful workflows but demands maturity in design and monitoring.

Implications for Makers and Organizations

Overall, Shane Young’s video frames these features as a practical roadmap for adopting AI-assisted development within the Power Platform. For makers, the immediate takeaway is to experiment with small, controlled projects that demonstrate value while exposing governance gaps. For organizations, the priority is to balance speed and innovation with security, cost control, and data quality; this often means defining guardrails, monitoring usage, and teaching teams how to validate AI outputs.

In conclusion, the four features discussed present real opportunities to improve productivity and customer experiences, but they also bring new responsibilities. By combining rapid experimentation with clear governance and observability, teams can leverage these tools effectively and prepare for a future where AI becomes a standard part of the maker toolkit. The video serves as a helpful guide for anyone looking to get ahead in the evolving Microsoft ecosystem, whether you're an individual developers or part of an enterprise team.

Keywords

Power Platform updates, Microsoft Power Platform innovations, Power Apps new features, Power Automate enhancements, Power BI integration tools, Power Platform AI tools, low-code development platforms, citizen developer tools