New UX Patterns for AI Design

🔮 Design Patterns For AI Interfaces (https://lnkd.in/dyyMKuU9), a practical overview with emerging AI UI patterns, layout considerations and real-life examples — along with interaction patterns and limitations. Neatly put together by Sharang Sharma. One of the major shifts is the move away from traditional “chat-alike” AI interfaces. As Luke Wroblewski wrote, when agents can use multiple tools, call other agents and run in the background, users orchestrate AI work — there’s a lot less chatting back and forth. In fact, chatbot widgets are rarely an experience paradigm that people truly enjoy and can fall in love with. Mostly because the burden of articulating intent efficiently lies on the user. It can be done (and we’ve learned to do that), but it takes an incredible amount of time and articulation to give AI enough meaningful context for it to produce meaningful insights. As it turned out, AI is much better at generating prompt based on user’s context to then feed it into itself. So we see more task-oriented UIs, semantic spreadsheets and infinite canvases — with AI proactively asking questions with predefined options, or where AI suggests presets and templates to get started. Or where AI agents collect context autonomously, and emphasize the work, the plan, the tasks — the outcome, instead of the chat input. All of it are examples of great User-First, AI-Second experiences. Not experiences circling around AI features, but experiences that truly amplify value for users by sprinkling a bit of AI in places where it delivers real value to real users. And that’s what makes truly great products — with AI or without. ✤ Useful Design Patterns Catalogs: Shape of AI: Design Patterns, by Emily Campbell 👍 https://shapeof.ai/ AI UX Patterns, by Luke Bennis 👍 https://lnkd.in/dF9AZeKZ Design Patterns For Trust With AI, via Sarah Gold 👍 https://lnkd.in/etZ7mm2Y AI Guidebook Design Patterns, by Google https://lnkd.in/dTAHuZxh ✤ Useful resources: Usable Chat Interfaces to AI Models, by Luke Wroblewski https://lnkd.in/d-Ssb5G7 The Receding Role of AI Chat, by Luke Wroblewski https://lnkd.in/d8xcujMC Agent Management Interface Patterns, by Luke Wroblewski https://lnkd.in/dp2H9-HQ Designing for AI Engineers, by Eve Weinberg https://lnkd.in/dWHstucP #ux #ai #design

AI products like Cursor, Bolt and Replit are shattering growth records not because they're "AI agents". Or because they've got impossibly small teams (although that's cool to see 👀). It's because they've mastered the user experience around AI, somehow balancing pro-like capabilities with B2C-like UI. This is product-led growth on steroids. Yaakov Carno tried the most viral AI products he could get his hands on. Here are the surprising patterns he found: (Don't miss the full breakdown in today's bonus Growth Unhinged: https://lnkd.in/ehk3rUTa) 1. Their AI doesn't feel like a black box. Pro-tips from the best: - Show step-by-step visibility into AI processes - Let users ask, “Why did AI do that?” - Use visual explanations to build trust. 2. Users don’t need better AI—they need better ways to talk to it. Pro-tips from the best: - Offer pre-built prompt templates to guide users. - Provide multiple interaction modes (guided, manual, hybrid). - Let AI suggest better inputs ("enhance prompt") before executing an action. 3. The AI works with you, not just for you. Pro-tips from the best: - Design AI tools to be interactive, not just output-driven. - Provide different modes for different types of collaboration. - Let users refine and iterate on AI results easily. 4. Let users see (& edit) the outcome before it's irreversible. Pro-tips from the best: - Allow users to test AI features before full commitment (many let you use it without even creating an account). - Provide preview or undo options before executing AI changes. - Offer exploratory onboarding experiences to build trust. 5. The AI weaves into your workflow, it doesn't interrupt it. Pro-tips from the best: - Provide simple accept/reject mechanisms for AI suggestions. - Design seamless transitions between AI interactions. - Prioritize the user’s context to avoid workflow disruptions. -- The TL;DR: Having "AI" isn’t the differentiator anymore—great UX is. Pardon the Sunday interruption & hope you enjoyed this post as much as I did 🙏 #ai #genai #ux #plg

Work on designing AI-first assistant and agent experiences has been eye opening. AI UX is both fundamentally the same and widely different, especially for vertical use cases. There are clear and emerging patterns that will likely continue to scale: 1. Comfort will start with proactive intelligence and hyper personalization. The biggest expectation customers have of AI is that it’s smart and it knows them based on their data. Personalization will become a key entry point where a recommendation kicks off a “thread” of inquiry. Personalization should only get better with “memory”. Imagine a pattern where an assistant or an agent notifies you of an anamoly, advice that’s specific to your business, or an area to dig deeper into relative to peers. 2. There are two clear sets of UX patterns that will emerge: assistant-like experiences and transformative experiences. Assistant-like experiences will sound familiar by now. Agents will complete a task partially either based on input or automation and the user confirms their action. You see this today with experiences like deep search. Transformative experiences will often start by human request and will then become background experiences that are long running. Transformative experiences, in particular, will require associated patterns like audit trails, failure notifications, etc. 3. We will start designing for agents as much as we design for humans. Modularity and building in smaller chunks becomes even more important. With architecture like MCP, the way you think of the world in smaller tools becomes a default. Understanding the human JTBD will remain core but you’ll end up building experiences in pieces to enable agents to pick and choose what parts to execute in what permutation of user asks. 4. It’ll become even more important to design and document existing standard operating procedures. One way to think about this is a more enhanced more articulated version of a customer journey. You need to teach agents the way not just what you know. Service design will become an even more important field. 5. There will be even less tolerance for complexity. Anything that feels like paperwork, extra clicks, or filler copy will be unacceptable; the new baseline is instant, crystal‑clear, outcome‑focused guidance. No experience, no input, no setting should start from zero. Just to name a few. The underlying piece is that this will all depend on the culture design teams, in particular, embrace as part of this transition. What I often hear is that design teams are already leading the way in adoption of AI. The role of Design in a world where prototyping is far more rapid and tools evolve so quickly will become even more important. It’ll change in many ways (some of it is by going back to basics) but will remain super important nonetheless. Most of the above will sound familiar on the surface but there’s so much that changes in the details of how we work. Exciting times.

𝗜𝗻𝘁𝗲𝗻𝘁 𝗯𝘆 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆: Designing the AI User Experience AI is not just a better chat box. It changes the user’s role from operator to supervisor, which forces UX to move from command-based interaction toward intent-based delegation, new usability metrics, orchestration layers, calibrated friction, and ultimately exploration-based interaction to clarify the user’s needs. As software shifts from apps to AI agents, mature intent-based systems will settle into a triple-layered design model: 🎯 𝗜𝗻𝘁𝗲𝗻𝘁 𝗦𝘂𝗿𝗳𝗮𝗰𝗲: Where users state outcomes. Context-aware and multimodal, this layer increasingly infers implicit intent from ambient signals: drafting the prompt so users just confirm. 🔍 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗦𝘂𝗿𝗳𝗮𝗰𝗲: The negotiation layer. Agents reveal plans, seek consent, and provide post-action receipts. In enterprises, it resolves collaborative intent: flagging conflicts, enforcing policies, and showing who's affected before execution. 🖐️ 𝗗𝗶𝗿𝗲𝗰𝘁 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗦𝘂𝗿𝗳𝗮𝗰𝗲: The GUI lives on as a fallback for inspection, correction, and override. But users now manipulate plans, not raw controls: retaining hands-on agency at a higher level of abstraction. My full article 👉 https://lnkd.in/grRVAhTe

Most #AI interfaces still assume users are prompt whisperers. But let’s be honest: In the real world, people don’t want to “#tune” prompts. They want to get things done — precisely, quickly, reliably. That’s why I found Microsoft Research’s latest work on Promptions worth highlighting. It’s a lightweight framework that wraps dynamic UI controls around prompts, so users can steer generative AI without starting from scratch every time. What stood out 👇 1️⃣ Prompting is becoming interactive Users get buttons, sliders, toggles — not just a blinking cursor. 2️⃣ Controls evolve with context As the conversation flows, so do the options — making interaction feel intelligent, not rigid. 3️⃣ No more prompt fatigue Users in early studies got more accurate outputs with less mental strain. 4️⃣ Works across models Promptions is model-agnostic. Use it with GPT, local LLMs, or internal enterprise agents. 5️⃣ Developer-friendly It’s open-source (MIT license) and easily pluggable into existing agent UIs. 6️⃣ Built for productivity Whether you're generating copy, answering support queries, or analyzing data — it guides users to outcomes faster. 7️⃣ Reimagines prompting as design This moves the interface from words-as-code to controls-as-clarity. Bottom line: Promptions shifts the paradigm — from prompt engineering to prompt experience design. And that opens doors for scalable, low-friction AI use across the enterprise. 🔗 Read the full research blog here: https://lnkd.in/g4KMRTWu #UXForAI #PromptDesign #ProductivityAI #AgentUX #AIInteraction #MicrosoftResearch

Agentic AI Design Patterns are emerging as the backbone of real-world, production-grade AI systems, and this is gold from Andrew Ng Most current LLM applications are linear: prompt → output. But real-world autonomy demands more. It requires agents that can reflect, adapt, plan, and collaborate, over extended tasks and in dynamic environments. That’s where the RTPM framework comes in. It's a design blueprint for building scalable agentic systems: ➡️ Reflection ➡️ Tool-Use ➡️ Planning ➡️ Multi-Agent Collaboration Let’s unpack each one from a systems engineering perspective: 🔁 1. Reflection This is the agent’s ability to perform self-evaluation after each action. It's not just post-hoc logging—it's part of the control loop. Agents ask: → Was the subtask successful? → Did the tool/API return the expected structure or value? → Is the plan still valid given current memory state? Techniques include: → Internal scoring functions → Critic models trained on trajectory outcomes → Reasoning chains that validate step outputs Without reflection, agents remain brittle, but with it, they become self-correcting systems. 🛠 2. Tool-Use LLMs alone can’t interface with the world. Tool-use enables agents to execute code, perform retrieval, query databases, call APIs, and trigger external workflows. Tool-use design involves: → Function calling or JSON schema execution (OpenAI, Fireworks AI, LangChain, etc.) → Grounding outputs into structured results (e.g., SQL, Python, REST) → Chaining results into subsequent reasoning steps This is how you move from "text generators" to capability-driven agents. 📊 3. Planning Planning is the core of long-horizon task execution. Agents must: → Decompose high-level goals into atomic steps → Sequence tasks based on constraints and dependencies → Update plans reactively when intermediate states deviate Design patterns here include: → Chain-of-thought with memory rehydration → Execution DAGs or LangGraph flows → Priority queues and re-entrant agents Planning separates short-term LLM chains from persistent agentic workflows. 🤖 4. Multi-Agent Collaboration As task complexity grows, specialization becomes essential. Multi-agent systems allow modularity, separation of concerns, and distributed execution. This involves: → Specialized agents: planner, retriever, executor, validator → Communication protocols: Model Context Protocol (MCP), A2A messaging → Shared context: via centralized memory, vector DBs, or message buses This mirrors multi-threaded systems in software—except now the "threads" are intelligent and autonomous. Agentic Design ≠ monolithic LLM chains. It’s about constructing layered systems with runtime feedback, external execution, memory-aware planning, and collaborative autonomy. Here is a deep-dive blog is you would like to learn more: https://lnkd.in/dKhi_n7M

Most UX designers are obsessing over the wrong thing. They're still polishing the chrome on a car that's about to be replaced by a teleporter. After 25 years in UX, I can tell you the future of design is invisible. And it’s coming faster than you think. For decades, we obsessed over the visible. Buttons. Menus. Layouts. Pixels. (And we got really, really good at it.) We built beautiful, intuitive interfaces to guide users from A to B. But we were perfecting the art of giving people options, not outcomes. That entire paradigm is about to break. AI is dissolving the UI. The best interface is becoming no interface. We’re moving from a world of interaction to a world of intent. Old UX: “Here are 12 ways to get what you want.” New UX: “Just tell me what you want.” This isn't a prediction. It's a pattern I've watched build for years. And it leads to a future that looks less like a screen and more like a conversation with reality itself. Here’s where it gets wild: 🧠 Mind-Reading Interfaces: Forget clicks. Think brainwaves. Your learning app will sense you’re losing focus and simplify the content in real-time. Your game will dial up the difficulty when it senses you're locked in. The interface adapts to your cognitive state, not the other way around. 👃 Scent-Enabled Experiences: Imagine a meditation app that releases a calming lavender scent, or a travel app that lets you smell the salt air of the beach you're booking. We're about to move from audiovisual design to multi-sensory reality. It's the final frontier of immersion. ❤️ Emotion-Aware Empathy: The system knows you’re stressed (from your voice tone or biometrics) and automatically simplifies the UI, hiding non-essential features. It doesn't ask you what's wrong. It senses it, and it helps. This is UX with emotional intelligence. This isn’t about sci-fi. It's about designing with a profound respect for human attention and cognitive load. It’s about getting the technology out of the way so people can live their lives. Your job is no longer to be a screen architect. It's to be an architect of understanding. Your next big project isn't a redesign. It's rethinking how to deliver an outcome with the least friction possible. What's the one app on your phone you wish was completely invisible? #userexperience #uxdesigners #uxdesigner #futureofdesign #design

Six new words just entered our design vocabulary — and most design briefs haven't caught up yet. AI didn't just add features to our products. It rewrote what an "interface" even means. Inputs aren't just inputs anymore, they're prompts. Workflows aren't just workflows, they're agents acting on our behalf. And screens are starting to write themselves. Here are 6 terms I think every product designer needs in their vocabulary right now: → Prompt UX — designing how users write, refine and understand prompts → Agentic UX — designing for AI that plans and takes multi-step action → Multimodal UX — text, voice, image and files, one seamless flow → Generative UI — interfaces that adapt and personalize themselves → Human in the Loop — AI assists, humans still review and approve → Intent-based UX — designing for the goal, not just the command typed Two years ago, none of these were in a standard design brief. Today, they're already shaping the products we use every day. Which one are you already designing for, and which one is brand new to you? Let me know in the comments. #UXDesign #ProductDesign #AIDesign #UXUI #DesignThinking

🔥 Why Half of Agentic Projects Still Fail (And the 4 Patterns That Actually Work) The future is agentic but without the right architecture, you're setting up for disappointment. Quick pattern design framework to execute successful AI @ work Pattern #1: The Self-Checking System - The problem: AI confidently delivers wrong answers. - The solution: Build in quality checks. How it works: After generating output, the AI reviews its own work with prompts like "Check this response for accuracy" or "What assumptions might be incorrect?" Apply here: Content teams use this for fact-checking articles. Legal teams apply it to contract reviews. Marketing teams validate campaign copy. Try this: Add "Please review your answer for potential errors" to any complex AI request. Pattern #2: The Connected Intelligence - The problem: Your AI operates in a data vacuum. - The solution: Connect it to live systems and APIs. How it works: AI agents call external tools; web search for research, databases for current information, APIs for system integration. Apply here: Customer service bots that check order status, scheduling assistants that access calendars, research tools that pull live market data. Try this: Start by connecting your AI to one external data source this week. Pattern #3: Planner Approach - The problem: AI jumps to conclusions without thinking through the process. - The solution: Force systematic planning before execution. How it works: Before starting, the AI creates a step-by-step approach: define objectives → gather requirements → outline methodology → execute → review. Apply here: Financial modeling (plan analysis framework first), content strategy (outline before writing), project management (break down complex tasks). Try this: Ask "What's your step-by-step plan to solve this?" before any multi-part request. Pattern #4: Multi-agent collaboration - The problem: One AI trying to be everything to everyone. - The solution: Deploy specialized agents for different capabilities. How it works: Different agents handle their areas of expertise; one for data analysis, another for writing, another for fact-checking and then consolidate their outputs. Apply here: Research projects using separate agents for data gathering, analysis and report writing. Product development with agents for market research, technical feasibility and competitive analysis. Multi-agent approach is more complex to manage but often superior results for multifaceted challenges. Most successful implementations combine patterns: • Customer support: Tool Use (CRM access) + Reflection (response validation) • Content creation: Planning (strategy first) + Reflection (quality check) • Business analysis: Multi-agent (specialists) + Tool Use (data sources) + Planning (structured approach) Pick the pattern that addresses your biggest AI challenge. Test it on one workflow this week. Success isn't about the latest AI model; it's about thoughtful architectural choices. #AIinWork

AI Agent Patterns: The Missing Design Layer in AI Systems (2026) Everyone is building AI agents. Very few are designing them properly. The gap between a demo and production AI isn’t the model. It’s the agent pattern behind it. Here are the core ones you should know: 👉 Reflex Agents → Rule-based, fast, predictable → Email triage, auto-replies → No learning, no memory 👉 Goal-Based Agents → Start with a goal, reason toward it → Planning, content, workflows → Smarter, more compute 👉 Utility-Based Agents → Score options, pick the best → Recommendations, trading, ranking → Depends on a strong scoring function 👉 Multi-Agent Systems → Specialized agents collaborating → Research, CI/CD, enterprise automation → Powerful but harder to coordinate 👉 Tool-Based Agents → Use APIs, browsers, databases → Turn AI into real-world action → Requires safety + permissions 👉 Memory-Based Agents → Remember context over time → Personalization, long-running tasks → Governance matters 👉 Planning & Reasoning Loops → Break tasks into steps (ReAct, Planner-Executor) → Reliable for complex problems → Slower but precise Bottom line: AI agents aren’t prompts with tools. They’re architected systems. If you’re building AI for fintech, platforms, or enterprise workflows — agent patterns are now a core design skill. More coming soon.