Design Project Management Tools

🪂 How To Make Your Design System AI-Ready (https://lnkd.in/dtnpy7CM), a practical guide on how to reduce drifts, minimize mistakes, maintain context and improve the quality of AI-generated prototypes — with structured spec files, automated auditing and token layers. Put together by Hardik Pandya from Atlassian. --- 🔹 1. Design Decisions Are Infrastructure AI-generated prototypes often don't deliver consistently decent results because of tiny inconsistencies scattered all across a design system. Often it's decisions made but not documented, hard-coded values never cleaned up, or relying too much on AI making sense of mock-ups or design flows on its own. Unsurprisingly, better AI prototypes come from better data — but also from better human guidance. We shouldn’t assume that AI knows how to choose the right component, and how to design with accessibility in mind. It needs priorities, a clear path on how we make decisions, design principles, examples, do's and don'ts. In fact, we should treat design decisions as infrastructure. That means that every time we make a decision — not just a design decision, but even decision on how actually prioritize our work and how we make decisions around here — it must find a path into the spec file that is then consumed by AI. --- 🔶 2. Three Layers: Spec Files + Token Layer + Audit To ensure quality, we establish design principles, guidelines, rules in a form of “spec files”). It's structured Markdown files that include spacing rules, color choices, component usage guidelines, priorities etc. AI is going to read and reuse that spec file every time it's going to generate a prototype. Because the spec files are text files, it's much more cost-effective, but also much more accurate just because we don't rely on AI recognizing or decoding patterns from mock-ups, but gets specific guidelines instead. In fact, extending code is often a more effective way than generating code from mock-ups. Token layer lists and keeps updated all tokens used throughout the design system. AI always chooses from a closed set of named variables instead of inventing plausible values ad-hoc. An audit script catches what AI gets wrong. It scans the prototype and flags every hard-coded value and flags it if necessary. It can be a regular software doing that, with AI waiting for its feedback to come back. Finally, when a design system ships updates, a sync routine flags which spec files need updating. The goal is to make sure that AI always reads up-to-date, current specs, not the ones written against an outdated version. --- 🔺 3. Examples of AI-Ready Design Systems ⌾ Atlassian: https://lnkd.in/dVsGc3Cp ⌾ Carbon: https://lnkd.in/d4zq4WWb ⌾ CMS Design System: https://lnkd.in/dHHzV3en ⌾ Nordhealth: https://lnkd.in/d8C4j2ZA Yet again, AI can’t magically resolve technical debt or design debt — it needs guidance, decisions, priorities and principles.

Build efficient Agents with Google's ADK and Skills: cut 90% of your agent's baseline context - and let it write new skills at runtime! Great write-up https://lnkd.in/gvWqPrcB by Lavi Nigam and Shubham Saboo Most AI agents carry their entire brain into every conversation. Compliance rules, style guides, API references, troubleshooting playbooks - all concatenated into one giant system prompt, loaded on every call, whether the user's question needs that context or not. The ADK team in Google Cloud published a great walkthrough of a better pattern and it's worth 5 minutes of your day. The idea: progressive disclosure, borrowed from the agentskills.io spec. Split knowledge into three layers. → L1: ~100 tokens of metadata per skill. Always loaded. A menu the agent scans. → L2: the full skill instructions. Loaded only when the agent activates that skill. → L3: reference docs and resources. Loaded only when the instructions call for them. An agent with 10 skills now starts each call with ~1,000 tokens of metadata instead of 10,000 tokens of prompt. Roughly a 90% cut in baseline context. But the part that actually changes how you think about agent design is the meta skill - a skill whose job is to write new skills. You embed the spec and a working example as L3 resources. When the agent hits a capability gap, it generates a spec-compliant SKILL.md on the spot. Ask for "a Python security review skill" and you get one back, ready to save and reload next session. An agent that can author its own skills at runtime isn't bounded by what you thought to put in the prompt on day one. And because it's all one spec, a skill generated in ADK runs unchanged in Gemini CLI, Claude Code, Cursor, and 40+ other tools. #ai #programming #softwareengineering

Are you still wasting time collecting site data from 5 different portals? Building footprints from one source. Wind patterns from another. Topography? Probably buried in a PDF somewhere. It’s 2025, and with AI tools around, we shouldn’t be spending hours stitching datasets together just to start a design. I use Aino to cut through the noise and get clean, reliable data fast. Here’s what makes it work so well for site studies: 👉 Building footprints and building use mapped in seconds 👉 Adjustable building heights visualised in a gradient 👉 Real-time wind movement overlays 👉 Street network identified and simplified 👉 Topography with contour clarity 👉 Open spaces sorted into categories My favourite features: ✅ Traffic Heatmaps ↳ See where bottlenecks occur and plan circulation with confidence. ✅ Clip and Export ↳ Crop any area and export in PNG, SVG, PDF, or DXF for design workflows. With Aino, you spend less time on data chaos and more time designing with clarity. Want to see how it works in real projects? I’ve added a short tutorial video below.

Over the years working in chemical processing, one of the recurring challenges I’ve faced is with heat exchangers. They are essential for energy efficiency, but even minor issues can create significant downtime and cost. Not long ago, we encountered a serious fouling issue in one of our exchangers. The deposits were reducing heat transfer efficiency, causing higher energy consumption and forcing frequent shutdowns for cleaning. 🔍Instead of treating it as just another maintenance task, we carried out a detailed root cause analysis: • Reviewed process conditions and flow patterns. • Checked velocity and temperature profiles. • Involved both the operations and maintenance teams in the discussion. The findings showed that low fluid velocity was the main driver for fouling. By redesigning the piping layout and adjusting the operating parameters, we were able to: ✅ Increase turbulence and reduce fouling. ✅ Extend cleaning cycles from every 3 months to once a year. ✅ Achieve over 15% improvement in efficiency. For me, the key takeaway is that every technical problem is also an opportunity to innovate and improve reliability. Collaboration and data-driven decisions can transform a recurring issue into a long-term success.

How can designers use Claude Code? Not as a chatbot. As a production engine! Tommaso Nervegna recently published a practical guide to move from static mockups to working software without becoming traditional developers. At first glance, it sounds like “AI helps you code.” It’s not that simple. This isn’t about asking AI to generate snippets and pasting them somewhere. It’s about using Claude Code as an execution layer, where design intent becomes runnable output. What’s happening in this workflow: 🔸 Designers describe outcomes, not syntax 🔸 Claude generates structured project scaffolding 🔸 Iteration happens conversationally, with persistent context 🔸 Components evolve into functional UI, not just visual artifacts 🔸 The feedback loop lives inside the AI workflow, not in Jira tickets That’s a different paradigm. This isn’t “design handoff improved.” It’s closer to: design-as-executable-logic. When AI understands the structure, constraints, and system intent, documentation becomes dynamic. It becomes operational. Still early? Definitely. Still messy? In parts. But directionally… this is big. Because if designers can reliably move from concept → structured logic → functional interface with AI as a collaborator, the bottleneck shifts. Less translation. More orchestration. More systems thinking. We’re getting closer to a world where: Design is infrastructure. Prompts are architecture. And iteration cycles collapse dramatically. 🔗 Check the Practical Guide: https://lnkd.in/d_C7Nad6 Would you use Claude Code as part of your design workflow, or does that blur a boundary you still want to keep? 👇 #DesignSystems #designsystem #ClaudeCode #GenerativeAI #AIDesign #DesignEngineering #DesignOps #ProductDesign #UXStrategy #VibeCoding

Top 6 AI tools for design & workflow in 2026 👇 Yes, not all of them are “design tools.” Yes, that’s exactly the point. I spent time exploring tools beyond just UI screens… Because real product work is not just design anymore. It’s workflows. Automation. AI orchestration. Here are 6 that actually matter right now: 1. Paperclip AI https://lnkd.in/dXkCrnbe Local-first AI for organizing research, notes, and work items. But it goes deeper. It acts like an orchestration layer for AI agents. Goals. Budgets. Audit logs. Agent “heartbeats.” If you deal with messy research or multi-step thinking, this is insanely powerful. 2. Flowstep https://flowstep.ai Prompt → UI designs. It generates wireframes and full interfaces on an infinite canvas. You can iterate fast. Refine layouts. Explore ideas visually. Feels like Figma + AI had a smarter child. 3. Moonchild AI https://moonchild.ai Turn PRDs into actual UI screens. It helps with: User flows UX problem solving Moodboards Design systems This is not just generation. It’s structured product thinking. 4. Dify https://dify.ai Visual builder for AI apps. Drag. Drop. Deploy. You can create: Chat apps Text-generation tools Custom AI workflows If you ever wanted to ship your own AI product without heavy coding, start here. 5. Flowise https://www.flowise.io Low-code builder for LLM workflows. Think: Connecting multiple models Creating agent flows Shipping APIs fast Great for prototyping AI features inside real products. 6. n8n https://n8n.io Automation on steroids. Connect apps. Trigger workflows. Automate repetitive ops. Designers ignore this. Smart designers don’t. Because real impact = design + systems. Here is the shift most designers are still missing. The future is not just UI design. It’s: Design + AI Design + automation Design + systems thinking Tools like Flowstep and Moonchild help you design faster. Tools like Dify, Flowise, and n8n help you build smarter. And tools like Paperclip help you think better. AI will not replace designers. But designers who understand workflows will replace designers who only push pixels. Use these tools for: Speed Exploration Systems thinking Execution Not just aesthetics. Because in 2026… The best designers are not just designing screens. They are designing how things work. If you had to pick ONE tool to explore this week, Which one are you trying first?

BIM Workflow From Client Brief to As-Built (with LOD for each stage) 1. Client Brief / Project Kick-off • Input: Client requirements, project goals, constraints • Output: Project charter, BIM Execution Plan (BEP) draft • LOD: 100 (Conceptual level) 2. Concept Design • Initial architectural massing, space planning • Preliminary coordination with structural and MEP concepts • LOD: 100–150 • BIM Use: Basic visualization, feasibility review 3. Schematic Design (SD) • Developed design intent models • Begin model federation • Clash Detection #1 (Major system conflicts) • LOD: 200 • BIM Use: Coordination, quantity take-off (early), cost estimation 4. Clash Resolution (Post-SD) • Coordination meetings • Clash reports resolved collaboratively • Model cleaned and updated 5. Design Development (DD) • Full system modeling with detailed architectural, structural, and MEP components • Accurate geometry, preliminary product selection • Clash Detection #2 (Detailed coordination) • LOD: 300 • BIM Use: Coordination, constructability review, client review 6. Clash Resolution (Post-DD) • Final cross-discipline coordination • Issue-free federated model for documentation 7. Tender / Construction Documentation (CD) • Final documentation with specifications, drawings, and schedules • Ready for pricing and bidding • LOD: 350–400 • BIM Use: Quantity take-off, tendering, shop drawing extraction 8. Construction Phase • Contractor updates models with construction methods and field conditions • Model used for sequencing (4D), cost tracking (5D) • LOD: 400–450 9. As-Built / Handover • Final model updated to reflect installed and verified components • Includes asset data, maintenance info, and commissioning results • LOD: 500 • BIM Use: Facilities management, lifecycle planning

Google’s Agent Development Kit (ADK) - an open-source, flexible framework designed to simplify the creation and deployment of AI agents and multi-agent systems. With ADK, developers can design intelligent agents that think, act, and coordinate, whether it’s for conversational assistants, automation, or complex multi-step workflows. What sets ADK apart is its model-agnostic and deployment-agnostic design. Though optimized for Google’s Gemini and cloud stack, it supports other LLMs, tools, and infrastructures as well. It makes agent development feel more like building software - structured, modular, and highly adaptable. Core Concepts An Agent in ADK is a self-contained unit capable of reasoning, using tools, and collaborating with other agents. Developers can create LLM-based agents for natural language tasks, workflow agents for automation, or custom agents for domain-specific logic. Tools & Ecosystem ADK supports a rich tool ecosystem, including pre-built utilities for search, code execution, and custom functions. Agents can even use other agents as tools, allowing highly modular and scalable AI architectures. Orchestration & Workflow Developers can control agent behavior using workflow types like Sequential, Parallel, or Loop, or rely on LLM-driven routing for dynamic orchestration. This combination enables hybrid systems that balance rule-based logic with adaptive intelligence. Deployment Options Once built, agents can be packaged into containers and deployed across environments, from Vertex AI Agent Engine and Cloud Run to custom infrastructures like Docker, GKE, or on-prem servers. Ready to experiment with AI agents? Install it with: pip install google-adk Then build your first multi-agent application using Python or Java and deploy it wherever you want. #AgentDevelopmentKit

We’ve mapped 𝗔𝗜-𝗶𝗻-𝗖𝗼𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻 pioneers across five breakthrough categories! As cities expand and infrastructure demand grows, AI is transforming construction — making it faster, safer, and more sustainable from the ground up. These innovators are redefining how we design, build, and manage projects — unlocking massive efficiency gains while reducing costs and carbon footprints. 𝗖𝗼𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 & 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 Robotic systems and autonomous equipment to accelerate builds, reduce labor bottlenecks, and enhance precision. → Buildroid → Built Robotics → Dusty Robotics → Canvas → Toggle Robotics 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 AI-powered scheduling, resource allocation, and cost forecasting tools for streamlined project delivery. → Buildots → ALICE Technologies → Mastt → Procore Technologies 𝗗𝗲𝘀𝗶𝗴𝗻 & 𝗕𝗜𝗠 Generative design and AI-enhanced Building Information Modeling for faster, smarter, and more sustainable planning. → Autodesk → Bentley Energy Infrastructure Solutions → TestFit 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗖𝗮𝗽𝘁𝘂𝗿𝗲 & 𝗦𝗶𝘁𝗲 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 AI-driven imaging and LiDAR solutions for real-time site tracking, progress measurement, and quality assurance. → OpenSpace → Doxel → Versatile 𝗦𝗮𝗳𝗲𝘁𝘆 & 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 Computer vision and AI models to detect safety hazards, automate compliance checks, and process critical construction documents. → Newmetrix → Document Crunch 𝗪𝗵𝘆 𝗻𝗼𝘄? → Urbanization is driving demand for faster, more cost-efficient builds → AI enables predictive safety, reduced delays, and lower costs → Robotics and automation are addressing skilled labor shortages → Sustainable construction is now a competitive advantage We’re curating the 2026 #WeTheAtlas Report: Construction & AI — mapping the companies redefining how we design, build, and manage the built environment. If you know a breakthrough construction or AI company (or you’re building one), 𝘁𝗮𝗴 𝘁𝗵𝗲𝗺 𝗯𝗲𝗹𝗼𝘄 or share their details so we can include them in the report.

This week, I had a great time building agents on Google Agent Development Kit (ADK) at Google Cloud’s Agentic AI Live + Lab. I have been using ADK since its launch earlier this year. Some of my observations on the current version of Google's ADK for agent builders:- Google ADK is No Longer Just a Kit! It's a Blueprint for Production-Ready AI Teams. As an Agent Builder, the last six months of Google ADK evolution have fundamentally shifted what's possible. It's time to stop building isolated ‘super-agents’ and start orchestrating intelligent systems. Here are the 5 game-changers for builders:- 1. The Rise of Multi-Agent Systems (MAS):- ADK now makes building hierarchical ‘Agent Teams’, with specialized agents delegating tasks via Workflow Agents (Sequential/Parallel/Loop) the standard. This means better reliability and modularity than ever before. 2. Native Production Readiness:- With the stable Python ADK v1.0.0 and robust built-in features for IAM, Audit Logging, and Data Governance, the path from a local prototype to an enterprise-grade, secure deployment is drastically shortened. 3. Debugging Via X-Ray Vision:- The new Trace View in the Developer UI provides an X-ray of the agent's full execution path and reasoning. Finally, we can debug multi-step workflows with visual clarity, not just print statements! 4. Agent-as-a-Tool:- Modularity just got an upgrade. You can now wrap an entire specialized agent and use it as a callable utility (AgentTool) inside another agent. This is the key to building truly scalable and reusable agent components. 5. Standardized Interoperability:- Updates to the Agent2Agent (A2A) Protocol and richer tooling support (including OpenAPI integration and compatibility with LangChain/CrewAI) mean your ADK agents can collaborate seamlessly with external systems and frameworks. Check it out - https://lnkd.in/dxYyYHcP #GoogleADK #AIAgents #AgentBuilder #GenAI #VertexAI