feat: event-driven ui updates

.living_spec/specs/functional/UI_UX.md

@@ -0,0 +1,24 @@
+# Functional Spec: UI/UX Responsiveness
+
+## Problem
+Currently, the `chat` command in Rust is an async function that performs a long-running, blocking loop (waiting for LLM, executing tools). While Tauri executes this on a separate thread from the UI, the frontend awaits the *entire* result before re-rendering. This makes the app feel "frozen" because there is no feedback during the 10-60 seconds of generation.
+
+## Solution: Event-Driven Feedback
+Instead of waiting for the final array of messages, the Backend should emit **Events** to the Frontend in real-time.
+
+### 1. Events
+*   `chat:token`: Emitted when a text token is generated (Streaming text).
+*   `chat:tool-start`: Emitted when a tool call begins (e.g., `{ tool: "git status" }`).
+*   `chat:tool-end`: Emitted when a tool call finishes (e.g., `{ output: "..." }`).
+
+### 2. Implementation Strategy (MVP)
+For this story, we won't fully implement token streaming (as `reqwest` blocking/async mixed with stream parsing is complex). We will focus on **State Updates**:
+
+*   **Refactor `chat` command:**
+    *   Instead of returning `Vec<Message>` at the very end, it accepts a `AppHandle`.
+    *   Inside the loop, after every step (LLM response, Tool Execution), emit an event `chat:update` containing the *current partial history*.
+    *   The Frontend listens to `chat:update` and re-renders immediately.
+
+### 3. Visuals
+*   **Loading State:** The "Send" button should show a spinner or "Stop" button.
+*   **Auto-Scroll:** The chat view should stick to the bottom as new events arrive.

.living_spec/specs/tech/STACK.md

@@ -22,6 +22,7 @@ The application follows a **Tool-Use (Function Calling)** architecture:
     *   Validates the request against the **Safety Policy**.
     *   Executes the native code (File I/O, Shell Process, Search).
     *   Returns the output (stdout/stderr/file content) to the LLM.
+    *   **Event Loop:** The backend emits real-time events (`chat:update`) to the frontend to ensure UI responsiveness during long-running Agent tasks.
 
 ## LLM Provider Abstraction
 To support both Remote and Local models, the system implements a `ModelProvider` abstraction layer.