Close Menu
    Facebook X (Twitter) Instagram
    • Privacy Policy
    • Terms Of Service
    • Social Media Disclaimer
    • DMCA Compliance
    • Anti-Spam Policy
    Facebook X (Twitter) Instagram
    Deep Tech Ledger
    • Home
    • Crypto News
      • Bitcoin
      • Ethereum
      • Altcoins
      • Blockchain
      • DeFi
    • AI News
    • Stock News
    • Learn
      • AI for Beginners
      • AI Tips
      • Make Money with AI
    • Reviews
    • Tools
      • Best AI Tools
      • Crypto Market Cap List
      • Stock Market Overview
      • Market Heatmap
    • Contact
    Deep Tech Ledger
    Home»AI News»Dynamic UI for dynamic AI: Inside the emerging A2UI model
    Dynamic UI for dynamic AI: Inside the emerging A2UI model
    AI News

    Dynamic UI for dynamic AI: Inside the emerging A2UI model

    March 9, 20264 Mins Read
    Share
    Facebook Twitter LinkedIn Pinterest Email
    kraken



    With agentic AI, businesses are conducting business more dynamically. Instead of traditional pre-programmed bots and static rules, agents can now “think” and invent alternate paths when unseen conditions arise. For instance, using a business domain ontology like FIBO (financial industry business ontology) can help keep agents within guardrails and avoid unwanted behavior.

    The bottleneck is now in the user experience (UX) layer. While agents are dynamic and transform with the data drift guided by ontology, the user interface is still very much static. These experiences with fixed fields and configurations can hamper the creative freedom given to agents. Modern standards like AG-UI (agent User interface) help streamline communication between UX and agents — but still the screens must be pre-defined at design time.

    A newer technology is taking this to the next level, dynamically allowing agents to render their desired user screen based on specific content. One is A2UI – agent to user interface. With A2UI, we first define a UX schema for how components should be rendered. This loosely coupled schema allows agents to build screens as per the data.

    Agents now communicate with a A2Ui compliant “renderer” that dynamically renders screens based on JSON content that agents produce dynamically. Screens are fully interactive and can communicate back with respective agents using AG-UI. Companies like Copilotkit are actively building A2UI renderers that can dynamically build the UI from JSON spec and wire it together back to the agent via AG-Ui. 

    murf

    Moreover, using newer compression standards like token object notation (TOON) can help obtain highly efficient compression and include schema like ontology and A2UI into context prompts. Of course, as models get smarter, they will also include capability to auto generate screens compliant with A2UI and AG-UI via pre-training.

    The below schematic explains one view of this architecture.

    As shown, the A2UI specification is complementary to a business ontology and focused on rendering logic for user interface components. Taking an example of loan approval, ontology will define business concepts like loans, parties, interest terms, covenants, or conditions. This data is usually in multiple source systems in different forms and a common business ontology helps unifies this into a common “language." The A2UI specification will define how user experience components will be rendered.

    In the future, only the spec needs to change, rather than individual screens, because screens are generated with fresh content every time. Also, since A2UI uses AG-UI under the hood, screens maintain connection to the original agent that generated the content. So, events like button clicks and form submissions can be tracked and responded to. This entire experience happens inside of a single pane of glass — for instance, a traditional chatbot.

    The business deliverable is tying together ontology, agents, A2UI JSON, dynamic content screens and AG-UI message exchanges. Everything is driven by business logic and relations defined in the ontology, meaning less is left for interpretation by the UX designer and UI developer. We still need these roles on projects, but reusable components are defined and built just once. Rinse and repeat!

    For example, you could define that any communication message sent to a user (error, info, warning) be rendered inside a panel with your company logo and be compliant with ISO 9241-110. With agentic AI and A2UI, a dedicated agent can validate these messages and construct them on screen per standards.

    The chat interface still remains your primary interface for users, but A2UI components are rendered the same. More importantly, the existing user screens can be reused as templates to dynamically generate newer screens. This makes your business highly robust to business and regulatory changes.

    Patterns like A2UI lessen dependency on user interface and complement the dynamic nature of business. Imagine a company undergoes an acquisition and must add new logos to thousands of forms. Now, this logic can be configured in the A2UI spec and ontology and UI changes will be propagated when users access forms. This helps businesses be dynamic and improve employee productivity.

    Dattaraj Rao is innovation and R&D architect at Persistent Systems.



    Source link

    livechat
    Share. Facebook Twitter Pinterest LinkedIn Tumblr Email
    CryptoExpert
    • Website

    I’m someone who’s deeply curious about crypto and artificial intelligence. I created this site to share what I’m learning, break down complex ideas, and keep people updated on what’s happening in crypto and AI—without the unnecessary hype.

    Related Posts

    A better method for identifying overconfident large language models | MIT News

    March 20, 2026

    Xiaomi stuns with new MiMo-V2-Pro LLM nearing GPT-5.2, Opus 4.6 performance at a fraction of the cost

    March 19, 2026

    Trustpilot partners with big model vendors

    March 18, 2026

    How to Build High-Performance GPU-Accelerated Simulations and Differentiable Physics Workflows Using NVIDIA Warp Kernels

    March 17, 2026
    Add A Comment
    Leave A Reply Cancel Reply

    aistudios
    Latest Posts

    XLM Price Prediction: Stellar Eyes $0.18 Recovery by April 2026

    March 19, 2026

    Ethereum Foundation Deploys 3,400 ETH to Morpho Vaults

    March 19, 2026

    OP_NET Launches “SlowFi” DeFi Stack Directly on Bitcoin L1

    March 19, 2026

    Jane Street Is Trading Bitcoin Again: What You Should Know About This Major Player

    March 19, 2026

    Fed still expects to cut rates once this year despite spiking oil prices

    March 19, 2026
    synthesia
    LEGAL INFORMATION
    • Privacy Policy
    • Terms Of Service
    • Social Media Disclaimer
    • DMCA Compliance
    • Anti-Spam Policy
    Top Insights

    A better method for identifying overconfident large language models | MIT News

    March 20, 2026

    Sol Rally Toward $100 Fizzles As Solana Competitors Rise

    March 20, 2026
    10web
    Facebook X (Twitter) Instagram Pinterest
    © 2026 DeepTechLedger.com - All rights reserved.

    Type above and press Enter to search. Press Esc to cancel.