# Agent Just Got Eyes — Click. Describe. Done. Published: Mar 13, 2025 Updated: May 21, 2025 \ \ **Darsh Patel** Making changes to your app should be as fast as possible. Instead of manually describing what to edit, what if you could just **click**? Now you can. Meet **Element Selector**, a simple yet powerful way to select any UI element in your app and modify it instantly with Agent or Assistant. No need to type out descriptions or search through files — just **Click. Describe. Done**. ### How It Works - **Toggle the Element Selector** — You can find it alongside the chat input tools in Agent and Assistant. - **Click to select** — Want to edit a button? Change a heading? Just **click it**. - **Describe changes** — Assistant updates text, colors, or styles in seconds. Use Agent to add features like turning a static list into a searchable one. ### Click. Describe. Done. **Quick Edits with Assistant** **Powerful Upgrades with Agent** Behind the scenes, we map each component in your app's interface to its exact location in the codebase. When you select an element, the agent receives: - Precise file and line number references - Style properties and current state - Associated functionality and event handlers This means Agent doesn't just guess—it _knows_ exactly what you want to change. ### Why this changes everything Instead of describing what to change, **just show it**. With **Element Selector**, Agent and Assistant now **understand your UI in context**, making edits effortless. Whether you're a beginner or an expert, **pointing is faster than typing** — and now, that power is yours. _Currently available for new JavaScript-based Apps. Support for additional tech stacks and older Apps coming soon._ **Click. Describe. Done.** More - Sep 10, 2026 **Replit  Databricks Integration is Now Generally Available with Native Lakebase Support** The Replit  Databricks integration gives enterprise teams a faster path to utilizing governed data for production-ready applications. Today, we’re excited to announce the integration is generally available alongside a new capability: native Databricks Lakebase support. This builds on the foundation introduced from our public preview announcement this past June. The result is one seamless path from idea to production: Replit accelerates application creation, while Databricks hosts and provides governed access and security to live enterprise data. Through Lakebase, Databricks also delivers a managed database for storing and updating application data. With native Lakebase support, teams can build full-stack, context-rich apps that combine live Databricks warehouse data with information created and updated through the app. When an app built with Replit is ready to deploy, Replit Agent automatically provisions its Lakebase database, eliminating the need for manual setup. New Capabilities for a Faster, Safer Launch Automated Preview Deploys: Test your apps before they go live. Replit automatically creates a separate preview environment that keeps test data isolated from live business data in Databricks, giving teams a safe place to build, review changes, and make adjustments. - Aug 3, 2026 **AI adoption starts with truth** The semantic layer is the foundation AI adoption is limited by trust. A user who gets burned by a confidently wrong answer will double-check the next one, eventually routing consequential work around the system entirely. Once that happens, AI remains a tool at the edges rather than infrastructure at the center… useful, but never trusted with the workflows where its value compounds. Before a company can benefit from more capable agents, those agents need a reliable way to know what the company considers true. A semantic layer tells an agent which tables are sources of truth and how they relate. That's the floor. It is necessary, and it isn't sufficient. A semantic layer is not plumbing. It is the first act of governance for an AI-native company: the shared definitions of the business, the canonical metrics, the sources of truth, and the relationships an agent is allowed to rely on. Without it, an agent does not have a data problem. It has a language problem: several tables can each look plausible, and the model has no grounded way to know which one means "revenue," "active user," or "customer." Getting that floor right changes the shape of everything above it. A semantic layer is not the product; it is the shared contract that lets a company safely add a system of specialized capabilities instead of one generic chatbot. Once an agent can ground itself in the right entities, metrics, and relationships, it can reliably run multi-step workflows, call focused tools, retain reviewed knowledge across runs, reuse validation and analysis code, and operate through durable services where work already happens. - Jul 29, 2026 **Introducing Replit Design** The next era of design, for everyone