> ## Documentation Index > Fetch the complete documentation index at: https://docs.gainable.dev/llms.txt > Use this file to discover all available pages before exploring further. # Build agents > Meet the build agents that turn your prompt into a working application ## The build agent system Gainable uses multiple specialized build agents, each with a specific role in turning your prompt into a working app. Think of them as a team of experts working together. **Build agents vs Gaia.** This page is about the agents that *build* your app: Planner, DataAnalyzer, Build, Validation, Conversation. Once your app is live, two more pieces of Gaia run *inside* it: [Gaia Copilot](/copilots/overview) for conversation and [Gaia Autopilot](/autopilot/overview) for autonomous work. ## Planner Agent The Planner is the first agent to see your request. It analyzes what you want and creates a structured plan. * Understands your intent (create, update, or seed data) * Identifies what data models you need * Determines which views and features to build * Recognizes when to use built-in skills (charts, kanban, chat) * Creates a checklist of what will be delivered ### Example: Planner in action When you say: *"Build a project tracker with tasks and due dates"* The Planner creates: ``` Intent: Create new application Summary: Build project tracker with task management Data models needed: - Project (name, description, status) - Task (title, due date, priority, linked to project) Views: - Project list with task counts - Task management within each project Features: - Create/edit/delete projects - Add tasks with due dates - Filter tasks by status ``` This plan is then handed to the Build Agent. ## DataAnalyzer Agent The DataAnalyzer is used when you [build from data](/build-from-data) instead of describing an app. It examines your synced data and designs an app spec through conversation. * Analyzes schema, field types, and sample data from connected sources * Identifies the domain (CRM, project management, finance, etc.) * Detects patterns that map to features (pipeline enums → kanban, currency → dashboards) * Asks clarifying questions through interactive multiple-choice options * Produces a multi-stage build specification * Decides where to place collaboration features based on domain context ### Example: DataAnalyzer in action When you connect a Google Sheet with deal data (name, amount, stage, close date), the DataAnalyzer: ``` Domain detected: CRM / Sales Key patterns: - "stage" enum with pipeline values → Kanban board - "amount" currency field → Dashboard KPIs + charts - "close_date" date field → Timeline filtering Recommended views: - Pipeline (kanban by stage) - Dashboard (revenue KPIs + charts) - Deals table (searchable, sortable) ``` It then asks 2-3 questions to refine the spec before handing off to the Build Agent. ## Build Agent The Build Agent is the workhorse that generates all your application code. * Creates database models to store your data * Builds API routes for data operations * Generates user interface pages * Implements forms, tables, and interactive elements * Follows proven patterns for reliability ### Skills and expertise The Build Agent has deep knowledge of: * **Data visualization** - Charts, graphs, KPIs * **Drag-and-drop** - Kanban boards, sortable lists * **Real-time updates** - Live data synchronization * **UI patterns** - Forms, modals, drawers, navigation * **Collaboration** - Chat, files, comments integration When you request a specific feature like "add a chart" or "add a kanban board", the Build Agent uses specialized knowledge to implement it correctly. ## Validation Agent Before your app is delivered, the Validation Agent checks for common issues. * Forms that don't save data correctly * Missing connections between data and views * UI patterns that could break on mobile * Common coding mistakes This happens automatically - you don't need to do anything. It's like having a quality assurance team review the code before you see it. ## Conversation Agent When you ask questions about your project (instead of requesting changes), the Conversation Agent helps. * Answers questions about what was built * Explains how features work * Helps troubleshoot issues * Provides guidance on next steps ### Example: Asking questions ```text Your question theme={null} "How does the task filtering work?" ``` ```text Conversation Agent response theme={null} "Tasks can be filtered by status using the dropdown menu at the top of the task list. The filter is applied client-side, so it updates instantly as you select different options. The available statuses are: pending, in progress, and completed." ``` ## How agents communicate The agents work in sequence, each building on the previous work: You describe what you want to build or change The system determines: build, question, or seed data For build requests, the Planner analyzes and structures the work Code is generated following the plan Generated code is checked for issues Your working app appears in the preview When you use the [Build from data](/build-from-data) path, the DataAnalyzer Agent replaces the Planner Agent in this sequence. Instead of analyzing a text prompt, it analyzes your data schema and produces a build specification through interactive conversation. The Build Agent and Validation Agent work the same way in both paths. ## Context preservation All agents share context about your project: * **What's already built** - Existing models, views, and features * **Your preferences** - Design choices, naming conventions * **Project history** - Previous changes and iterations This means you can say things like "add a filter to the deals page" and the agents understand exactly which page you mean. Context is preserved throughout your session. Each new request builds on everything that came before. ## Best practices To get the most from the agent system: 1. **Be specific** - The more detail you provide, the better the results 2. **One thing at a time** - Let each build complete before requesting the next 3. **Use natural language** - Write like you're explaining to a colleague 4. **Request by feature name** - "Add a pie chart" is clearer than "visualize this somehow" ## Gaia inside your finished apps The agents described above (Planner, Build, Validation, Conversation) are **build agents** that work behind the scenes to create your app. Once your app is live, two more pieces of Gaia run inside it: * **Gaia Copilot.** The conversational assistant. The user asks; the Copilot answers. It reads your app's live data and references uploaded knowledge. * **Gaia Autopilot.** The autonomous side. They watch data, draft work, and land it in Autopilot for approval. They run on their own. The two are designed to work side by side. Copilot is reactive. Agents are proactive. Conversational assistant with data access, knowledge bases, and an embedded chat interface Autonomous agents that watch data and draft work for approval in Autopilot ## Learn more Explore built-in features you can request Write better prompts for better results The conversational layer of every app The proactive, autonomous layer