Microsoft Copilot Studio AI citations

Here are some of the best AI building automation platforms: 1. {{[[Honeywell]]:https://www.honeywell.com}} : Honeywell's Building Management Solutions leverage AI to optimize energy efficiency and enhance occupant comfort through predictive analytics. 2. {{[[Johnson Controls]]:https://www.johnsoncontrols.com}} : Johnson Controls offers AI-driven building automation systems that integrate HVAC, lighting, and security for improved operational efficiency. 3. {{[[Schneider Electric]]:https://www.se.com}} : Schneider Electric's EcoStruxure platform utilizes AI to provide real-time insights and automation for energy management and building operations. 4. {{[[Siemens]]:https://new.siemens.com}} : Siemens' Desigo CC is an advanced building management system that employs AI to streamline operations and improve energy usage. 5. {{[[Trane]]:https://www.trane.com}} : Trane's solutions incorporate AI for HVAC automation, enabling better climate control and energy savings in commercial buildings. 6. {{[[KMC Controls]]:https://www.kmccontrols.com}} : KMC Controls offers AI-enabled building automation systems that focus on energy efficiency and smart building management. 7. {{[[BuildingIQ]]:https://buildingiq.com}} : BuildingIQ uses AI to optimize energy consumption and improve building performance through predictive modeling. 8. {{[[Distech Controls]]:https://www.distech-controls.com}} : Distech Controls provides AI-powered building automation solutions that enhance energy efficiency and occupant comfort. These platforms utilize AI technologies to enhance building management, optimize energy usage, and improve overall operational efficiency.

Here are some of the best AI building automation platforms and related tools: 1. **Architectural Design and Planning:** - {{[[Autodesk]]:https://www.autodesk.com/}}: **Spacemaker AI** is a powerful tool for urban planners and real estate developers, optimizing site planning by considering factors like wind flow, noise pollution, and zoning laws[1]. - {{[[Arkdesign.AI]]:https://arkdesign.ai/}}: Converts architectural briefs into schematic designs, adjusting to local building codes and climate[1]. - {{[[Hypar]]:https://hypar.io/}}: Integrates code-based automation into architectural design, ideal for modular construction techniques[1]. 2. **AI Automation Platforms:** - {{[[Domo]]:https://www.domo.com/}}: Offers AI-powered workflows for enterprise automation, bridging data insights with action[5]. - {{[[UiPath]]:https://www.uipath.com/}}: Specializes in Robotic Process Automation (RPA), automating repetitive tasks[3]. - {{[[Alteryx]]:https://www.alteryx.com/}}: Provides advanced data analytics and predictive modeling, useful for data-driven decision-making[4]. 3. **AI Agent Builders:** - {{[[Microsoft]]:https://www.microsoft.com/}}: **Microsoft Copilot Studio** and **Azure AI Foundry Agent Service** are top choices for building AI agents[6]. - {{[[Stack AI]]:https://stackai.com/}}: Offers a no-code approach for AI agent development[6]. - {{[[Voiceflow]]:https://voiceflow.com/}}: Focuses on conversation-driven AI agents, particularly useful for customer service[6].

Here are the best AI building automation platforms: 1. {{[[Johnson Controls]]:https://www.johnsoncontrols.com}} : Offers advanced AI-powered building management systems with predictive maintenance and energy optimization capabilities. 2. {{[[Siemens]]:https://www.siemens.com/global/en/products/buildings/intelligent-infrastructure.html}} : Provides comprehensive AI-driven building automation solutions with integrated smart building technologies. 3. {{[[Honeywell]]:https://www.honeywell.com/us/en/buildings}} : Delivers sophisticated AI platforms for building management, focusing on energy efficiency and occupant comfort. 4. {{[[Schneider Electric]]:https://www.se.com/ww/en/work/solutions/building-automation/}} : Develops intelligent building management systems with machine learning and IoT integration. 5. {{[[IBM Maximo]]:https://www.ibm.com/products/maximo}} : Offers AI-powered asset management and predictive maintenance for complex building systems. 6. {{[[Microsoft Azure]]:https://azure.microsoft.com/en-us/solutions/iot-device-management}} : Provides cloud-based AI platforms for building automation and smart infrastructure management. 7. {{[[Cisco]]:https://www.cisco.com/c/en/us/solutions/enterprise-networks/smart-building-solutions.html}} : Develops networked building automation solutions with AI-driven analytics and security. These platforms leverage artificial intelligence to optimize building performance, reduce energy consumption, and enhance operational efficiency.

Here’s a compact set of **Generative-UI design patterns** that work well in domain/enterprise tools, with solid references for each: * **Ask → Edit → Agent (mode switching)**: start with Q\&A, escalate to draft edits, then let an agent execute bounded tasks; always expose scope & rollback. ([The GitHub Blog][1]) * **Sidecar copilot (in-app focus)**: persistent right/left pane that stays in the user’s workflow with quick actions, context chips, and safe “apply” controls. ([Microsoft Learn][2]) * **Structured prompt builders**: form-like inputs, parameters, and constraints instead of raw free text for repeatable, auditable outcomes. ([pair.withgoogle.com][3]) * **Plan → Preview → Apply (diff-first)**: show a plan and a visual diff before changes; require explicit confirmation and provide undo/versioning. ([UX Collective][4]) * **Outcome-oriented canvases**: users state intent; UI composes the steps/components and adapts layout dynamically to reach the goal. ([Nielsen Norman Group][5]) * **Tuners & Governors**: adjustable sliders/switches to steer style/strictness and hard guardrails for safety (scope, data boundaries). ([shapeof.ai][6]) * **Confidence & provenance affordances**: show “draft” labels, uncertainty/confidence hints, and citations/traceback for generated results. ([pair.withgoogle.com][3], [Nielsen Norman Group][7]) * **Inline suggestions**: non-modal, inline proposals (e.g., code/text/table rows) that are easy to accept/modify/reject. ([UX Collective][4]) * **Error-aware recovery**: obvious escape hatches—revert, manual override, escalate to human, and targeted re-prompts. ([UX Collective][4]) * **Domain tool integration (actions & tools)**: expose verbed buttons the agent can call (simulate first, then run) with scoping and audit logs. ([Microsoft Learn][8]) * **Copilot UX principles (human-in-control, no anthropomorphism)**: set expectations, show limits, and keep the human as pilot. ([Microsoft Learn][2]) * **Generative UI primers & exemplars**: roundups and visual catalogs to inform component choices across enterprise contexts. ([UX Planet][9], [Medium][10]) * **Domain analytics copilots (pattern transfer)**: apply the same patterns to BI/analytics UIs (query → draft insight → refine → apply). ([Microsoft Learn][11]) * **Cloud app UX for gen-AI**: Google’s guidance on task framing, handoffs to deterministic flows, and guardrails for agents. ([Google Cloud][12]) ## References (handy starting points) * NN/g: *Generative UI & Outcome-Oriented Design*. ([Nielsen Norman Group][5]) * Microsoft: *Copilot UX guidance (ISV)* and *Copilot Studio agent guidance*. ([Microsoft Learn][2]) * Google PAIR: *People + AI Guidebook*. ([pair.withgoogle.com][3]) * GitHub: *Copilot Ask, Edit, Agent modes*. ([The GitHub Blog][1]) * Whitespectre/UX catalogs: *Emerging Gen-AI UI patterns*. ([Medium][10]) * UX Planet: *Generative-AI UX design patterns* (system/scope/spatial lenses). ([UX Planet][9]) * Designing for Analytics (enterprise LLM UX considerations). ([designingforanalytics.com][13]) * Shape of AI: pattern taxonomy (Wayfinders, Tuners, Governors, Trust builders). ([shapeof.ai][6]) * Google Cloud: *UX considerations for gen-AI apps* + *101 blueprints* (design + architecture). ([Google Cloud][12]) * Microsoft Power BI Copilot (analytics copilot UI). ([Microsoft Learn][11]) Want me to turn these into a checklist you can drop into your design system (with component examples)? [1]: https://github.blog/ai-and-ml/github-copilot/copilot-ask-edit-and-agent-modes-what-they-do-and-when-to-use-them/?utm_source=chatgpt.com "Copilot ask, edit, and agent modes: What they do and ..." [2]: https://learn.microsoft.com/en-us/microsoft-cloud/dev/copilot/isv/ux-guidance?utm_source=chatgpt.com "Creating a dynamic UX: guidance for generative AI ..." [3]: https://pair.withgoogle.com/guidebook/?utm_source=chatgpt.com "People + AI Guidebook - Home" [4]: https://uxdesign.cc/20-genai-ux-patterns-examples-and-implementation-tactics-5b1868b7d4a1?utm_source=chatgpt.com "20+ GenAI UX patterns, examples and implementation tactics" [5]: https://www.nngroup.com/articles/generative-ui/?utm_source=chatgpt.com "Generative UI and Outcome-Oriented Design" [6]: https://www.shapeof.ai/?utm_source=chatgpt.com "The Shape of AI | UX Patterns for Artificial Intelligence Design" [7]: https://www.nngroup.com/articles/genai-ux-research-agenda/?utm_source=chatgpt.com "A Research Agenda for Generative AI in UX" [8]: https://learn.microsoft.com/en-us/microsoft-copilot-studio/guidance/?utm_source=chatgpt.com "Microsoft Copilot Studio guidance documentation" [9]: https://uxplanet.org/generative-ai-ux-design-patterns-192bb169ab99?utm_source=chatgpt.com "Generative AI UX Design Patterns" [10]: https://medium.com/whitespectre/emerging-ui-ux-patterns-in-generative-ai-a-visual-guide-74000c7198a4?utm_source=chatgpt.com "Emerging UI/UX Patterns in Generative AI - Medium" [11]: https://learn.microsoft.com/en-us/power-bi/create-reports/copilot-introduction?utm_source=chatgpt.com "Overview of Copilot for Power BI" [12]: https://cloud.google.com/blog/products/ai-machine-learning/how-to-build-a-genai-application?utm_source=chatgpt.com "UX considerations for generative AI apps and agents" [13]: https://designingforanalytics.com/resources/ui-ux-design-for-enterprise-llms-use-cases-and-considerations-for-data-and-product-leaders-in-2024-part-1/?utm_source=chatgpt.com "UI/UX Design Considerations for LLMs in Enterprise ..."

The most common **design patterns** for Generative UI in domain tools center on dynamic UI construction, adaptation to user context, seamless integration with design systems, and maintaining interactive and accessible experiences.[1][2][3][4] ## Key Generative UI Patterns - **Pattern-Based Generation:** Define a library of standardized, atomic components (e.g. buttons, tables, forms) which the generative AI combines per user scenario, ensuring consistency and theme alignment.[2][3] - **Prompt-to-UI Mapping:** Use structured prompts or intent models to translate user descriptions or requirements into structured UI definitions, often as JSON or similar representations that map directly to design system components.[3][2] - **Live Contextual Adaptation:** UIs that adapt in real time to changing user context, data, or workflow requirements—such as resizing, adjusting compositional layouts, or changing fields based on current task flow.[4][1] - **Outcome-Oriented Design:** Rather than focusing on static templates, generative UIs aim for task, workflow, or outcome optimization—surfacing controls and data most relevant for the user’s stated or inferred goals.[5][4] - **Validation and Safety Guardrails:** Implement rigorous testing and output validation, such as capping array/table sizes or checking that the generative model’s output adheres to allowed types, to ensure UI stability and prevent runtime errors.[3] - **Seamless Design System Integration:** Align all generated UI elements to a design system’s color, spacing, and typography tokens so they feel native and maintain branding and accessibility standards.[2][3] ## Best Practices - Build with a **dedicated design system** for Generative UI (like Thesys "Crayon"), emphasizing accessibility, interactivity, and responsive layouts.[2] - Use **iterative QA and user feedback** cycles, starting with pilot projects before broad rollouts, and always log AI outputs for ongoing improvement.[3] - Treat all AI-generated component specifications as production code, requiring validation, error handling, and secure sandboxing.[4][3] ## Further Reading and References - [20+ GenAI UX Patterns, Examples, and Implementation Tactics (UXDesign)](https://uxdesign.cc/20-genai-ux-patterns-examples-and-implementation-tactics-5b1868b7d4a1)[1] - [Technical Architecture: First Generative UI API (Thesys)](https://www.thesys.dev/blogs/generative-ui-architecture)[2] - [Enterprise Readiness for Generative UI (Thesys)](https://www.thesys.dev/blogs/getting-enterprise-ready-for-generative-ui-what-to-know-before-you-build)[3] - [Generative UI Guide 2025: 15 Best Practices & Examples (Mockplus)](https://www.mockplus.com/blog/post/generative-ui)[4] - [Generative UI and Outcome-Oriented Design (NN/G Nielsen Norman Group)](https://www.nngroup.com/articles/generative-ui/)[5] These sources offer both tactical implementation advice and strategic frameworks for leveraging generative UIs in business and enterprise software contexts.[1][5][4][2][3] [1](https://uxdesign.cc/20-genai-ux-patterns-examples-and-implementation-tactics-5b1868b7d4a1) [2](https://www.thesys.dev/blogs/generative-ui-architecture) [3](https://www.thesys.dev/blogs/getting-enterprise-ready-for-generative-ui-what-to-know-before-you-build) [4](https://www.mockplus.com/blog/post/generative-ui) [5](https://www.nngroup.com/articles/generative-ui/) [6](https://dl.acm.org/doi/10.1145/3715336.3735780) [7](https://www.mockplus.com/blog/post/generative-ai-tools) [8](https://arxiv.org/html/2505.15049v1) [9](https://www.shapeof.ai) [10](https://www.elsewhen.com/reports/from-generative-ai-to-generative-ui/) [11](https://uxplanet.org/generative-ai-ux-design-patterns-192bb169ab99) [12](https://www.nngroup.com/articles/genai-ux-research-agenda/) [13](https://martinfowler.com/articles/gen-ai-patterns/) [14](https://www.coveo.com/blog/generative-ai-best-practices/) [15](https://www.thesys.dev/blogs/how-generative-ui-is-transforming-internal-tools-across-the-enterprise) [16](https://www.getaprototype.com/knowledge-base/generative-ui-design) [17](https://www.k2view.com/blog/what-is-a-best-practice-when-using-generative-ai/) [18](https://uxdesign.cc/designing-for-ai-engineers-what-ui-patterns-and-principles-you-need-to-know-8b16a5b62a61) [19](https://devpulse.io/insights/ux-ui-design-best-practices-2025-enterprise-applications/) [20](https://uxplanet.org/7-key-design-patterns-for-ai-interfaces-893ab96988f6)

I'll search for information about design patterns for Generative UI in domain-specific tools.Let me search for more specific information about design patterns for generative UI in domain-specific applications.Based on my search, here are the key design patterns for Generative UI in domain tools: ## Core Design Patterns for Generative UI ### 1. **Outcome-Oriented Design Pattern** In the future, generative UI will dynamically create customized user interfaces in real-time. This shift will force an outcome-oriented design approach where designers prioritize user goals and define constraints (for AI to operate within), rather than design discrete interface elements. ### 2. **Constraint-Based Generation Pattern** Rather than designing specific UI elements, designers focus on: - Defining user goals and objectives - Setting operational constraints for AI systems - Allowing dynamic interface creation within those boundaries ### 3. **Iterative Refinement Pattern** Modern generative UI tools support iterate easily on your designs and edit your generated screens in seconds, enabling rapid prototyping and refinement cycles. ### 4. **Multi-Modal Generation Pattern** Image generation tools for new and visually appealing images based on existing patterns or styles. Sound generation tools for melodies, harmonies and even entire compositions - supporting comprehensive multimedia interface generation. ## Key Implementation Strategies ### Real-Time Adaptation The goal of these patterns is to help mitigate and overcome some of the challenges with generative AI implementations such as cost, latency and hallucinations. ### Enterprise Integration With the latest 2024 Q2 release, Progress brings generative AI integration, enhanced design system capabilities and accessible data visualization. ## Key Resources and Tools **Research and Guidelines:** - [Nielsen Norman Group - Generative UI and Outcome-Oriented Design](https://www.nngroup.com/articles/generative-ui/) - [ACM CHI 2024 - Design Principles for Generative AI Applications](https://dl.acm.org/doi/10.1145/3613904.3642466) - [Towards Data Science - Generative AI Design Patterns Guide](https://towardsdatascience.com/generative-ai-design-patterns-a-comprehensive-guide-41425a40d7d0/) **Tools and Platforms:** - [UX Pilot - AI UI Generator](https://uxpilot.ai/) - [Telerik/Progress - Enterprise AI Integration](https://www.telerik.com/blogs/telerik-kendo-ui-2024-q2-release-here-see-whats-new) - [Merge - AI Design Tools for 2025](https://merge.rocks/blog/top-ai-design-tools-for-ux-ui-designers-in-2025) **Industry Analysis:** - [Mockplus - 22 Best Generative AI Tools for UI/UX](https://www.mockplus.com/blog/post/generative-ai-tools) - [UXPin - UI/UX Design Trends 2025](https://www.uxpin.com/studio/blog/ui-ux-design-trends/) The trend shows a shift from traditional component-based design to constraint-based, outcome-oriented approaches where AI generates interfaces dynamically based on user goals and contextual requirements.

"{{[[OpenAI]]:https://openai.com}}, {{[[Anthropic]]:https://anthropic.com}}, {{[[Adept AI]]:https://adept.ai}}, {{[[Cognition Labs]]:https://cognition.ai}}, {{[[xAI]]:https://x.ai}} Ñ leaders advancing autonomous, reasoning-based AI architectures and agent frameworks."

The top AI companies for agentic systems in 2025 include: - {{[[Relevance AI]]:https://olive.app/blog/top-agentic-ai-platforms-in-2025-the-ultimate-guide-for-businesses/}}: Known for enterprise-grade agent orchestration and visual workflow design, focusing on security and scalability. - {{[[Microsoft Autogen]]:https://olive.app/blog/top-agentic-ai-platforms-in-2025-the-ultimate-guide-for-businesses/}}: Specializes in complex multi-agent collaboration with native Azure integration. - {{[[Cognosys]]:https://olive.app/blog/top-agentic-ai-platforms-in-2025-the-ultimate-guide-for-businesses/}}: Offers browser-native autonomous agents performing real web interactions. - {{[[IBM Watsonx Orchestrate]]:https://olive.app/blog/top-agentic-ai-platforms-in-2025-the-ultimate-guide-for-businesses/}}: Provides enterprise AI orchestration solutions. - {{[[Adept AI]]:https://olive.app/blog/top-agentic-ai-platforms-in-2025-the-ultimate-guide-for-businesses/}}: Focuses on agentic AI with strong reasoning and autonomy capabilities. - {{[[Microsoft Copilot Studio]]:https://olive.app/blog/top-agentic-ai-platforms-in-2025-the-ultimate-guide-for-businesses/}}: Enables building custom AI agents for diverse workflows. - {{[[Oracle]]:https://www.tredence.com/blog/best-ai-agents-2025}}: Its Miracle Agent automates workflows in finance, HR, and supply chain. - {{[[Anthropic]]:https://www.tredence.com/blog/best-ai-agents-2025}}: Known for Claude 3.5, which mimics human digital behavior for autonomous tasks. - {{[[Accenture]]:https://procurementmag.com/top10/top-10-companies-agentic-ai}}: Offers AI agent builders and pre-configured industry agent solutions, collaborating with NVIDIA. - {{[[Pactum]]:https://procurementmag.com/top10/top-10-companies-agentic-ai}}: Specializes in procurement-focused agentic AI for negotiation and supplier deal automation. - {{[[FourKites]]:https://procurementmag.com/top10/top-10-companies-agentic-ai}}: Deploys agentic AI for digital workforce solutions in procurement. - {{[[OpenAI]]:https://betterstack.com/community/comparisons/best-agentic-ai-companies/}}: Provides advanced agentic AI tools with strong reasoning and enterprise integration. - {{[[LangChain]]:https://betterstack.com/community/comparisons/best-agentic-ai-companies/}}: Known for multi-agent systems and customization capabilities. - {{[[Cognition (Devin)]]:https://betterstack.com/community/comparisons/best-agentic-ai-companies/}}: High autonomy and reasoning depth for agentic AI. - {{[[Cursor]]:https://betterstack.com/community/comparisons/best-agentic-ai-companies/}}: Strong in production readiness and enterprise integration. - {{[[Lindy]]:https://betterstack.com/community/comparisons/best-agentic-ai-companies/}}: Excels in customization and enterprise readiness. - {{[[Zapier]]:https://betterstack.com/community/comparisons/best-agentic-ai-companies/}}: Integrates agentic AI with automation workflows. - {{[[Hippocratic AI]]:https://research.aimultiple.com/agentic-ai-companies/}}: Focuses on healthcare agentic AI for non-diagnostic automation. - {{[[Dropzone AI]]:https://research.aimultiple.com/agentic-ai-companies/}}: Specializes in cybersecurity agentic AI for threat detection and response. - {{[[Prophet]]:https://research.aimultiple.com/agentic-ai-companies/}}: Uses predictive analytics in cybersecurity with agentic AI. - {{[[RadiantSecurity]]:https://research.aimultiple.com/agentic-ai-companies/}}: AI-enhanced security operations with agentic AI. - {{[[Open Interpreter]]:https://research.aimultiple.com/agentic-ai-companies/}}: Open-source tools for AI model interpretation and agent development. - {{[[AgentGPT.io]]:https://research.aimultiple.com/agentic-ai-companies/}}: Open-source platform for general-purpose AI agents. - {{[[SuperAGI]]:https://research.aimultiple.com/agentic-ai-companies/}}: Open-source platform for complex agentic AI workflows. These companies cover a broad spectrum of agentic AI applications, including enterprise automation, multi-agent collaboration, procurement, cybersecurity, healthcare, and open-source development[1][2][3][4][5].

Top AI companies developing agentic AI systems include: 1. {{[[OpenAI]]:https://openai.com}} : Leading research in advanced AI agents with GPT models and potential agentic capabilities. 2. {{[[Anthropic]]:https://www.anthropic.com}} : Developing Claude AI with sophisticated reasoning and potential autonomous agent functionalities. 3. {{[[Google DeepMind]]:https://deepmind.com}} : Pioneering research in multi-agent systems and advanced AI reasoning. 4. {{[[Cohere]]:https://cohere.com}} : Offering AI models with strong potential for agentic system development. 5. {{[[Adept AI]]:https://www.adept.ai}} : Specifically focused on creating AI agents that can interact with software and perform complex tasks. 6. {{[[Databricks]]:https://www.databricks.com}} : Developing large language models with potential agentic capabilities through their DBRX model. 7. {{[[Mistral AI]]:https://mistral.ai}} : Emerging European AI company with advanced open-source models suitable for agentic systems. These companies are at the forefront of developing AI systems with increasing autonomy, reasoning, and task-completion capabilities.