Topic

Open Standards

All digests tagged Open Standards

Evolving AI chat with MCP Apps - Phil Nash - NDC Copenhagen 2026 thumbnail

· 38:00

Evolving AI chat with MCP Apps - Phil Nash - NDC Copenhagen 2026

The talk introduces MCP Apps, a proposed open standard designed to evolve AI chat interfaces beyond plain text. By integrating rich, interactive web UIs (built with HTML/CSS/JavaScript) directly into the conversation flow, MCP Apps allow agents to render mini-applications for tasks like booking hotels or managing playlists. This approach moves interaction from boring 'walls of text' to engaging, visual experiences, making AI more useful for complex user workflows.

Key takeaways

  1. The Need for Interactive UIs in Chat 18:02

    Traditional chat interactions are limited to text (or code/tool calls), which is insufficient for tasks requiring visual exploration, configuration of multiple options, or viewing real-time data. MCP Apps solve this by bringing web-powered interfaces into the chat environment.

  2. MCP Apps as an Open Standard 22:40

    MCP Apps is a standard inspired by community efforts (like MCP-UI) and commercial SDKs (e.g., OpenAI's Apps SDK), aiming to provide a unified way for agents to render UIs across different model providers.

  3. Core Functionality: Sandboxed Web Views 26:00

    MCP Apps are implemented as sandboxed web applications (HTML, CSS, JavaScript) loaded within an iframe. This isolation keeps the UI safe while allowing it to interact with the agent host via tool calls and a JSON RPC mechanism.

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Why AI Agents Need a Data Harness, Not Just a Lakehouse thumbnail

· 37:07

Why AI Agents Need a Data Harness, Not Just a Lakehouse

The talk outlines the necessity of a 'data harness'—a modern data foundation optimized for conversational-pace querying by AI agents. Traditional data pipelines built for minutes-to-hours turnaround fail under this rapid load, leading to errors and wasted tokens. The solution requires combining an open lakehouse architecture (using standards like Apache Iceberg and Apache Arrow) with a robust semantic layer, advanced caching (like Columnar Cloud Cache/C3), and federation capabilities to ensure data is accessible, understandable, and performant for AI use cases.

Key takeaways

  1. AI Agents Require Subsecond Performance 6:32

    Conversational analytics moves at a conversational pace. Legacy systems designed for minutes-to-hours turnaround cannot support this speed; subsecond response time is a critical requirement for agentic workflows.

  2. Three Pillars of AI-Ready Data Foundation 9:32

    Any modern data foundation must be accessible (seeing the whole estate), understandable (having clear, standardized definitions via a semantic layer), and performant (querying data where it lives without moving or cleaning it).

  3. Open Standards Prevent Vendor Lock-in 24:12

    The use of open standards like Apache Iceberg, Apache Arrow, and Apache Polaris ensures that the data platform remains vendor-neutral. This allows users to build performance without sacrificing portability.

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