Revert "don't need this"

This reverts commit 93121c18e0.

CHANGELOG.md

@@ -0,0 +1,33 @@
+# Changelog
+
+## [Unreleased]
+
+### Added
+
+**Interactive Requirements Mode**
+- **AI-Enhanced Interactive Requirements**: New `--interactive-requirements` flag for autonomous mode
+  - User enters brief description of what they want to build
+  - AI automatically enhances input into structured requirements.md document
+  - Generates professional markdown with:
+    - Project title and overview
+    - Organized requirements (functional, technical, quality)
+    - Acceptance criteria
+  - User can review, accept, edit manually, or cancel before proceeding
+  - Seamlessly transitions to autonomous mode
+
+**Autonomous Mode Configuration**
+- **Autonomous Mode Configuration**: Added ability to specify different models for coach and player agents in autonomous mode
+  - New `[autonomous]` configuration section in `g3.toml`
+  - `coach_provider` and `coach_model` options for coach agent
+  - `player_provider` and `player_model` options for player agent
+  - `Config::for_coach()` and `Config::for_player()` methods to generate role-specific configurations
+  - Comprehensive test suite for autonomous configuration
+
+### Changed
+- Autonomous mode now uses `config.for_player()` for the player agent
+- Coach agent creation now uses `config.for_coach()` for the coach agent
+
+### Benefits
+- **Cost Optimization**: Use cheaper models for execution, expensive models for review
+- **Speed Optimization**: Use faster models for iteration, thorough models for validation
+- **Specialization**: Leverage different providers' strengths for different roles

README.md

@@ -2,122 +2,14 @@
 
 G3 is a coding AI agent designed to help you complete tasks by writing code and executing commands. Built in Rust, it provides a flexible architecture for interacting with various Large Language Model (LLM) providers while offering powerful code generation and task automation capabilities.
 
-## Architecture Overview
-
-G3 follows a modular architecture organized as a Rust workspace with multiple crates, each responsible for specific functionality:
-
-### Core Components
-
-#### **g3-core**
-The heart of the agent system, containing:
-- **Agent Engine**: Main orchestration logic for handling conversations, tool execution, and task management
-- **Context Window Management**: Intelligent tracking of token usage with context thinning (50-80%) and auto-summarization at 80% capacity
-- **Tool System**: Built-in tools for file operations, shell commands, computer control, TODO management, and structured output
-- **Streaming Response Parser**: Real-time parsing of LLM responses with tool call detection and execution
-- **Task Execution**: Support for single and iterative task execution with automatic retry logic
-
-#### **g3-providers**
-Abstraction layer for LLM providers:
-- **Provider Interface**: Common trait-based API for different LLM backends
-- **Multiple Provider Support**: 
-  - Anthropic (Claude models)
-  - Databricks (DBRX and other models)
-  - Local/embedded models via llama.cpp with Metal acceleration on macOS
-- **OAuth Authentication**: Built-in OAuth flow support for secure provider authentication
-- **Provider Registry**: Dynamic provider management and selection
-
-#### **g3-config**
-Configuration management system:
-- Environment-based configuration
-- Provider credentials and settings
-- Model selection and parameters
-- Runtime configuration options
-
-#### **g3-execution**
-Task execution framework:
-- Task planning and decomposition
-- Execution strategies (sequential, parallel)
-- Error handling and retry mechanisms
-- Progress tracking and reporting
-
-#### **g3-computer-control**
-Computer control capabilities:
-- Mouse and keyboard automation
-- UI element inspection and interaction
-- Screenshot capture and window management
-- OCR text extraction via Tesseract
-
-#### **g3-cli**
-Command-line interface:
-- Interactive terminal interface
-- Task submission and monitoring
-- Configuration management commands
-- Session management
-
-### Error Handling & Resilience
-
-G3 includes robust error handling with automatic retry logic:
-- **Recoverable Error Detection**: Automatically identifies recoverable errors (rate limits, network issues, server errors, timeouts)
-- **Exponential Backoff with Jitter**: Implements intelligent retry delays to avoid overwhelming services
-- **Detailed Error Logging**: Captures comprehensive error context including stack traces, request/response data, and session information
-- **Error Persistence**: Saves detailed error logs to `logs/errors/` for post-mortem analysis
-- **Graceful Degradation**: Non-recoverable errors are logged with full context before terminating
-
 ## Key Features
 
-### Intelligent Context Management
+- **Multiple LLM Providers**: Anthropic (Claude), Databricks, OpenAI, and local models via llama.cpp
-- Automatic context window monitoring with percentage-based tracking
+- **Autonomous Mode**: Coach-player feedback loop for complex tasks
-- Smart auto-summarization when approaching token limits
+- **Intelligent Context Management**: Auto-summarization and context thinning at 50-80% thresholds
-- **Context thinning** at 50%, 60%, 70%, 80% thresholds - automatically replaces large tool results with file references
+- **Rich Tool Ecosystem**: File operations, shell commands, computer control, browser automation
-- Conversation history preservation through summaries
+- **Streaming Responses**: Real-time output with tool call detection
-- Dynamic token allocation for different providers (4k to 200k+ tokens)
+- **Error Recovery**: Automatic retry logic with exponential backoff
-
-### Tool Ecosystem
-- **File Operations**: Read, write, and edit files with line-range precision
-- **Shell Integration**: Execute system commands with output capture
-- **Code Generation**: Structured code generation with syntax awareness
-- **TODO Management**: Read and write TODO lists with markdown checkbox format
-- **Computer Control** (Experimental): Automate desktop applications
-  - Mouse and keyboard control
-  - UI element inspection
-  - Screenshot capture and window management
-  - OCR text extraction from images and screen regions
-  - Window listing and identification
-- **Final Output**: Formatted result presentation
-
-### Provider Flexibility
-- Support for multiple LLM providers through a unified interface
-- Hot-swappable providers without code changes
-- Provider-specific optimizations and feature support
-- Local model support for offline operation
-
-### Task Automation
-- Single-shot task execution for quick operations
-- Iterative task mode for complex, multi-step workflows
-- Automatic error recovery and retry logic
-- Progress tracking and intermediate result handling
-
-## Language & Technology Stack
-
-- **Language**: Rust (2021 edition)
-- **Async Runtime**: Tokio for concurrent operations
-- **HTTP Client**: Reqwest for API communications
-- **Serialization**: Serde for JSON handling
-- **CLI Framework**: Clap for command-line parsing
-- **Logging**: Tracing for structured logging
-- **Local Models**: llama.cpp with Metal acceleration support
-
-## Use Cases
-
-G3 is designed for:
-- Automated code generation and refactoring
-- File manipulation and project scaffolding
-- System administration tasks
-- Data processing and transformation  
-- API integration and testing
-- Documentation generation
-- Complex multi-step workflows
-- Desktop application automation and testing
 
 ## Getting Started
 
@@ -125,56 +17,234 @@ G3 is designed for:
 # Build the project
 cargo build --release
 
-# Run G3
+# Execute a single task
-cargo run
-
-# Execute a task
 g3 "implement a function to calculate fibonacci numbers"
+
+# Start autonomous mode with interactive requirements
+g3 --autonomous --interactive-requirements
 ```
 
+## Configuration
+
+Create `~/.config/g3/config.toml`:
+
+```toml
+[providers]
+default_provider = "databricks"
+
+[providers.anthropic]
+api_key = "sk-ant-..."
+model = "claude-3-5-sonnet-20241022"
+max_tokens = 4096
+
+[providers.databricks]
+host = "https://your-workspace.cloud.databricks.com"
+model = "databricks-meta-llama-3-1-70b-instruct"
+max_tokens = 4096
+use_oauth = true
+
+[agent]
+max_context_length = 8192
+enable_streaming = true
+
+# Optional: Use different models for coach and player in autonomous mode
+[autonomous]
+coach_provider = "anthropic"
+coach_model = "claude-3-5-sonnet-20241022"  # Thorough review
+player_provider = "databricks"
+player_model = "databricks-meta-llama-3-1-70b-instruct"  # Fast execution
+```
+
+## Autonomous Mode (Coach-Player Loop)
+
+G3 features an autonomous mode where two agents collaborate:
+- **Player Agent**: Executes tasks and implements solutions
+- **Coach Agent**: Reviews work and provides feedback
+
+### Option 1: Interactive Requirements with AI Enhancement (Recommended)
+
+```bash
+g3 --autonomous --interactive-requirements
+```
+
+**How it works:**
+1. Describe what you want to build (can be brief)
+2. Press **Ctrl+D** (Unix/Mac) or **Ctrl+Z** (Windows)
+3. AI enhances your input into a structured requirements document
+4. Review the enhanced requirements
+5. Choose to proceed, edit manually, or cancel
+6. If accepted, autonomous mode starts automatically
+
+**Example:**
+```
+You type: "build a todo app with cli in python"
+
+AI generates:
+# Todo List CLI Application
+
+## Overview
+A command-line todo list application built in Python...
+
+## Functional Requirements
+1. Add tasks with descriptions
+2. Mark tasks as complete
+3. Delete tasks
+...
+```
+
+### Option 2: Direct Requirements
+
+```bash
+g3 --autonomous --requirements "Build a REST API with CRUD operations for user management"
+```
+
+### Option 3: Requirements File
+
+Create `requirements.md` in your workspace:
+
+```markdown
+# Project Requirements
+
+1. Create a REST API with user endpoints
+2. Use SQLite for storage
+3. Include input validation
+4. Write unit tests
+```
+
+Then run:
+
+```bash
+g3 --autonomous
+```
+
+### Why Different Models for Coach and Player?
+
+Configure different models in the `[autonomous]` section to:
+- **Optimize Cost**: Use cheaper model for execution, expensive for review
+- **Optimize Speed**: Use fast model for iteration, thorough for validation
+- **Specialize**: Leverage provider strengths (e.g., Claude for analysis, Llama for code)
+
+If not configured, both agents use the `default_provider` and its model.
+
+## Command-Line Options
+
+```bash
+# Autonomous mode
+g3 --autonomous --interactive-requirements
+g3 --autonomous --requirements "Your requirements"
+g3 --autonomous --max-turns 10
+
+# Single-shot mode
+g3 "your task here"
+
+# Options
+--workspace <DIR>          # Set workspace directory
+--provider <NAME>          # Override provider (anthropic, databricks, openai)
+--model <NAME>             # Override model
+--quiet                    # Disable log files
+--webdriver                # Enable browser automation
+--show-prompt              # Show system prompt
+--show-code                # Show generated code
+```
+
+## Architecture Overview
+
+G3 is organized as a Rust workspace with multiple crates:
+
+- **g3-core**: Agent engine, context management, tool system, streaming parser
+- **g3-providers**: LLM provider abstraction (Anthropic, Databricks, OpenAI, local models)
+- **g3-config**: Configuration management
+- **g3-execution**: Task execution framework
+- **g3-computer-control**: Mouse/keyboard automation, OCR, screenshots
+- **g3-cli**: Command-line interface
+
+### Key Capabilities
+
+**Intelligent Context Management**
+- Automatic context window monitoring with percentage-based tracking
+- Smart auto-summarization when approaching token limits
+- Context thinning at 50%, 60%, 70%, 80% thresholds
+- Dynamic token allocation (4k to 200k+ tokens)
+
+**Tool Ecosystem**
+- File operations (read, write, edit with line-range precision)
+- Shell command execution
+- TODO management
+- Computer control (experimental): mouse, keyboard, OCR, screenshots
+- Browser automation via WebDriver (Safari)
+
+**Error Handling**
+- Automatic retry logic with exponential backoff
+- Recoverable error detection (rate limits, network issues, timeouts)
+- Detailed error logging to `logs/errors/`
+
 ## WebDriver Browser Automation
 
-G3 includes WebDriver support for browser automation tasks using Safari.
+**One-Time Setup** (macOS):
-
-**One-Time Setup** (macOS only):
-
-Safari Remote Automation must be enabled before using WebDriver tools. Run this once:
 
 ```bash
-# Option 1: Use the provided script
+# Enable Safari Remote Automation
-./scripts/enable-safari-automation.sh
-
-# Option 2: Enable manually
 safaridriver --enable  # Requires password
 
-# Option 3: Enable via Safari UI
+# Or via Safari UI:
 # Safari → Preferences → Advanced → Show Develop menu
 # Then: Develop → Allow Remote Automation
 ```
 
-**For detailed setup instructions and troubleshooting**, see [WebDriver Setup Guide](docs/webdriver-setup.md).
+**Usage**:
 
-**Usage**: Run G3 with the `--webdriver` flag to enable browser automation tools.
+```bash
+g3 --webdriver "scrape the top stories from Hacker News"
+```
+
+See [docs/webdriver-setup.md](docs/webdriver-setup.md) for detailed setup.
 
 ## Computer Control (Experimental)
 
-G3 can interact with your computer's GUI for automation tasks:
+Enable in config:
+
+```toml
+[computer_control]
+enabled = true
+require_confirmation = true
+```
+
+Grant accessibility permissions:
+- **macOS**: System Preferences → Security & Privacy → Accessibility
+- **Linux**: Ensure X11 or Wayland access
+- **Windows**: Run as administrator (first time)
 
 **Available Tools**: `mouse_click`, `type_text`, `find_element`, `take_screenshot`, `extract_text`, `find_text_on_screen`, `list_windows`
 
-**Setup**: Enable in config with `computer_control.enabled = true` and grant OS accessibility permissions:
+## Use Cases
-- **macOS**: System Preferences → Security & Privacy → Accessibility  
+
-- **Linux**: Ensure X11 or Wayland access
+- Automated code generation and refactoring
-- **Windows**: Run as administrator (first time only)
+- File manipulation and project scaffolding
+- System administration tasks
+- Data processing and transformation
+- API integration and testing
+- Documentation generation
+- Complex multi-step workflows
+- Desktop application automation
 
 ## Session Logs
 
-G3 automatically saves session logs for each interaction in the `logs/` directory. These logs contain:
+G3 automatically saves session logs to `logs/` directory:
 - Complete conversation history
 - Token usage statistics
 - Timestamps and session status
 
-The `logs/` directory is created automatically on first use and is excluded from version control.
+Disable with `--quiet` flag.
+
+## Technology Stack
+
+- **Language**: Rust (2021 edition)
+- **Async Runtime**: Tokio
+- **HTTP Client**: Reqwest
+- **Serialization**: Serde
+- **CLI Framework**: Clap
+- **Logging**: Tracing
+- **Local Models**: llama.cpp with Metal acceleration
 
 ## License
 
@@ -182,4 +252,4 @@ MIT License - see LICENSE file for details
 
 ## Contributing
 
-G3 is an open-source project. Contributions are welcome! Please see CONTRIBUTING.md for guidelines.
+Contributions welcome! Please see CONTRIBUTING.md for guidelines.

crates/g3-cli/src/lib.rs

@@ -302,6 +302,10 @@ pub struct Cli {
     #[arg(long, value_name = "TEXT")]
     pub requirements: Option<String>,
 
+    /// Interactive mode: prompt for requirements and save to requirements.md before starting autonomous mode
+    #[arg(long)]
+    pub interactive_requirements: bool,
+
     /// Use retro terminal UI (inspired by 80s sci-fi)
     #[arg(long)]
     pub retro: bool,
@@ -393,6 +397,113 @@ pub async fn run() -> Result<()> {
 
     // Create project model
     let project = if cli.autonomous {
+        // Handle interactive requirements mode with AI enhancement
+        if cli.interactive_requirements {
+            println!("\n📝 Interactive Requirements Mode");
+            println!("================================\n");
+            println!("Describe what you want to build (can be brief):");
+            println!("Press Ctrl+D (Unix) or Ctrl+Z (Windows) when done.\n");
+            
+            use std::io::{self, Read, Write};
+            let mut requirements_input = String::new();
+            io::stdin().read_to_string(&mut requirements_input)?;
+            
+            if requirements_input.trim().is_empty() {
+                anyhow::bail!("No requirements provided. Exiting.");
+            }
+            
+            println!("\n🤖 Enhancing your requirements with AI...\n");
+            
+            // Create a temporary agent to enhance the requirements
+            let temp_config = Config::load_with_overrides(
+                cli.config.as_deref(),
+                cli.provider.clone(),
+                cli.model.clone(),
+            )?;
+            
+            // Create a simple output writer for the enhancement task
+            let ui_writer = ConsoleUiWriter::new();
+            let mut temp_agent = Agent::new_with_readme_and_quiet(
+                temp_config,
+                ui_writer,
+                None,
+                true, // quiet mode for enhancement
+            ).await?;
+            
+            // Create enhancement prompt
+            let enhancement_prompt = format!(
+                r#"Convert the following user input into a well-structured requirements.md document.
+
+User Input:
+{}
+
+Create a professional requirements document with:
+1. A clear project title (# heading)
+2. An overview section explaining what will be built
+3. Organized requirements (functional, technical, quality)
+4. Acceptance criteria
+5. Any technical constraints or preferences mentioned
+
+Format as proper markdown. Be specific and actionable. If the user's input is vague, make reasonable assumptions but keep it focused on what they described.
+
+Output ONLY the markdown content, no explanations or meta-commentary."#,
+                requirements_input.trim()
+            );
+            
+            // Execute enhancement task
+            let result = temp_agent
+                .execute_task_with_timing(&enhancement_prompt, None, false, false, false, false)
+                .await?;
+            
+            let enhanced_requirements = result.response.trim().to_string();
+            
+            // Show the enhanced requirements
+            println!("\n📋 Enhanced Requirements Document:");
+            println!("{}\n", "=".repeat(60));
+            println!("{}", enhanced_requirements);
+            println!("{}\n", "=".repeat(60));
+            
+            // Ask for confirmation
+            println!("\n❓ Is this requirements document acceptable?");
+            println!("   [y] Yes, proceed with autonomous mode");
+            println!("   [e] Edit and save manually");
+            println!("   [n] No, cancel\n");
+            
+            print!("Your choice (y/e/n): ");
+            io::stdout().flush()?;
+            
+            let mut choice = String::new();
+            io::stdin().read_line(&mut choice)?;
+            let choice = choice.trim().to_lowercase();
+            
+            let requirements_path = workspace_dir.join("requirements.md");
+            
+            match choice.as_str() {
+                "y" | "yes" => {
+                    // Save enhanced requirements
+                    std::fs::write(&requirements_path, &enhanced_requirements)?;
+                    println!("\n✅ Requirements saved to: {}", requirements_path.display());
+                    println!("🚀 Starting autonomous mode...\n");
+                }
+                "e" | "edit" => {
+                    // Save enhanced requirements for manual editing
+                    std::fs::write(&requirements_path, &enhanced_requirements)?;
+                    println!("\n✅ Requirements saved to: {}", requirements_path.display());
+                    println!("📝 Please edit the file and run: g3 --autonomous");
+                    println!("   Exiting for now.\n");
+                    return Ok(());
+                }
+                "n" | "no" => {
+                    println!("\n❌ Cancelled. No files were saved.\n");
+                    return Ok(());
+                }
+                _ => {
+                    println!("\n❌ Invalid choice. Cancelled.\n");
+                    return Ok(());
+                }
+            }
+        }
+        
         if let Some(requirements_text) = cli.requirements {
             // Use requirements text override
             Project::new_autonomous_with_requirements(workspace_dir.clone(), requirements_text)?
@@ -451,7 +562,8 @@ pub async fn run() -> Result<()> {
     
     let mut agent = if cli.autonomous {
         Agent::new_autonomous_with_readme_and_quiet(
-            config.clone(),
+            // Use player-specific config in autonomous mode
+            config.for_player()?,
             ui_writer,
             combined_content.clone(),
             cli.quiet,
@@ -1522,14 +1634,15 @@ async fn run_autonomous(
 
         // Create a new agent instance for coach mode to ensure fresh context
         // Use the same config with overrides that was passed to the player agent
-        let config = agent.get_config().clone();
+        let base_config = agent.get_config().clone();
+        let coach_config = base_config.for_coach()?;
         
         // Reset filter suppression state before creating coach agent
         g3_core::fixed_filter_json::reset_fixed_json_tool_state();
         
         let ui_writer = ConsoleUiWriter::new();
         let mut coach_agent =
-            Agent::new_autonomous_with_readme_and_quiet(config, ui_writer, None, quiet).await?;
+            Agent::new_autonomous_with_readme_and_quiet(coach_config, ui_writer, None, quiet).await?;
 
         // Ensure coach agent is also in the workspace directory
         project.enter_workspace()?;

crates/g3-config/src/autonomous_config_tests.rs

@@ -0,0 +1,131 @@
+#[cfg(test)]
+mod autonomous_config_tests {
+    use crate::{Config, AnthropicConfig, DatabricksConfig};
+
+    #[test]
+    fn test_default_autonomous_config() {
+        let config = Config::default();
+        assert!(config.autonomous.coach_provider.is_none());
+        assert!(config.autonomous.coach_model.is_none());
+        assert!(config.autonomous.player_provider.is_none());
+        assert!(config.autonomous.player_model.is_none());
+    }
+
+    #[test]
+    fn test_for_coach_with_overrides() {
+        let mut config = Config::default();
+        
+        // Set up base config with anthropic
+        config.providers.anthropic = Some(AnthropicConfig {
+            api_key: "test-key".to_string(),
+            model: "claude-3-5-sonnet-20241022".to_string(),
+            max_tokens: Some(4096),
+            temperature: Some(0.1),
+        });
+        
+        // Set coach overrides
+        config.autonomous.coach_provider = Some("anthropic".to_string());
+        config.autonomous.coach_model = Some("claude-3-opus-20240229".to_string());
+        
+        let coach_config = config.for_coach().unwrap();
+        
+        // Verify coach uses overridden provider and model
+        assert_eq!(coach_config.providers.default_provider, "anthropic");
+        assert_eq!(
+            coach_config.providers.anthropic.as_ref().unwrap().model,
+            "claude-3-opus-20240229"
+        );
+    }
+
+    #[test]
+    fn test_for_player_with_overrides() {
+        let mut config = Config::default();
+        
+        // Set up base config with databricks
+        config.providers.databricks = Some(DatabricksConfig {
+            host: "https://test.databricks.com".to_string(),
+            token: Some("test-token".to_string()),
+            model: "databricks-meta-llama-3-1-70b-instruct".to_string(),
+            max_tokens: Some(4096),
+            temperature: Some(0.1),
+            use_oauth: Some(false),
+        });
+        
+        // Set player overrides
+        config.autonomous.player_provider = Some("databricks".to_string());
+        config.autonomous.player_model = Some("databricks-dbrx-instruct".to_string());
+        
+        let player_config = config.for_player().unwrap();
+        
+        // Verify player uses overridden provider and model
+        assert_eq!(player_config.providers.default_provider, "databricks");
+        assert_eq!(
+            player_config.providers.databricks.as_ref().unwrap().model,
+            "databricks-dbrx-instruct"
+        );
+    }
+
+    #[test]
+    fn test_no_overrides_uses_defaults() {
+        let mut config = Config::default();
+        config.providers.default_provider = "databricks".to_string();
+        
+        let coach_config = config.for_coach().unwrap();
+        let player_config = config.for_player().unwrap();
+        
+        // Both should use the default provider when no overrides
+        assert_eq!(coach_config.providers.default_provider, "databricks");
+        assert_eq!(player_config.providers.default_provider, "databricks");
+    }
+
+    #[test]
+    fn test_provider_override_only() {
+        let mut config = Config::default();
+        
+        config.providers.anthropic = Some(AnthropicConfig {
+            api_key: "test-key".to_string(),
+            model: "claude-3-5-sonnet-20241022".to_string(),
+            max_tokens: Some(4096),
+            temperature: Some(0.1),
+        });
+        
+        // Only override provider, not model
+        config.autonomous.coach_provider = Some("anthropic".to_string());
+        
+        let coach_config = config.for_coach().unwrap();
+        
+        // Should use overridden provider with its default model
+        assert_eq!(coach_config.providers.default_provider, "anthropic");
+        assert_eq!(
+            coach_config.providers.anthropic.as_ref().unwrap().model,
+            "claude-3-5-sonnet-20241022"
+        );
+    }
+
+    #[test]
+    fn test_model_override_only() {
+        let mut config = Config::default();
+        config.providers.default_provider = "databricks".to_string();
+        
+        config.providers.databricks = Some(DatabricksConfig {
+            host: "https://test.databricks.com".to_string(),
+            token: Some("test-token".to_string()),
+            model: "databricks-meta-llama-3-1-70b-instruct".to_string(),
+            max_tokens: Some(4096),
+            temperature: Some(0.1),
+            use_oauth: Some(false),
+        });
+        
+        // Only override model, not provider
+        config.autonomous.player_model = Some("databricks-dbrx-instruct".to_string());
+        
+        let player_config = config.for_player().unwrap();
+        
+        // Should use default provider with overridden model
+        assert_eq!(player_config.providers.default_provider, "databricks");
+        assert_eq!(
+            player_config.providers.databricks.as_ref().unwrap().model,
+            "databricks-dbrx-instruct"
+        );
+    }
+}

crates/g3-config/src/lib.rs

@@ -2,12 +2,16 @@ use serde::{Deserialize, Serialize};
 use anyhow::Result;
 use std::path::Path;
 
+#[cfg(test)]
+mod autonomous_config_tests;
+
 #[derive(Debug, Clone, Serialize, Deserialize)]
 pub struct Config {
     pub providers: ProvidersConfig,
     pub agent: AgentConfig,
     pub computer_control: ComputerControlConfig,
     pub webdriver: WebDriverConfig,
+    pub autonomous: AutonomousConfig,
 }
 
 #[derive(Debug, Clone, Serialize, Deserialize)]
@@ -86,6 +90,20 @@ impl Default for WebDriverConfig {
     }
 }
 
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct AutonomousConfig {
+    pub coach_provider: Option<String>,
+    pub coach_model: Option<String>,
+    pub player_provider: Option<String>,
+    pub player_model: Option<String>,
+}
+
+impl Default for AutonomousConfig {
+    fn default() -> Self {
+        Self { coach_provider: None, coach_model: None, player_provider: None, player_model: None }
+    }
+}
+
 impl Default for ComputerControlConfig {
     fn default() -> Self {
         Self {
@@ -120,6 +138,7 @@ impl Default for Config {
             },
             computer_control: ComputerControlConfig::default(),
             webdriver: WebDriverConfig::default(),
+            autonomous: AutonomousConfig::default(),
         }
     }
 }
@@ -232,6 +251,7 @@ impl Config {
             },
             computer_control: ComputerControlConfig::default(),
             webdriver: WebDriverConfig::default(),
+            autonomous: AutonomousConfig::default(),
         }
     }
     
@@ -300,4 +320,78 @@ impl Config {
         
         Ok(config)
     }
+    
+    /// Create a config for the coach agent in autonomous mode
+    pub fn for_coach(&self) -> Result<Self> {
+        let mut config = self.clone();
+        
+        // Apply coach-specific overrides if configured
+        if let Some(ref coach_provider) = self.autonomous.coach_provider {
+            config.providers.default_provider = coach_provider.clone();
+        }
+        
+        if let Some(ref coach_model) = self.autonomous.coach_model {
+            // Apply model override to the coach's provider
+            match config.providers.default_provider.as_str() {
+                "anthropic" => {
+                    if let Some(ref mut anthropic) = config.providers.anthropic {
+                        anthropic.model = coach_model.clone();
+                    } else {
+                        return Err(anyhow::anyhow!(
+                            "Coach provider 'anthropic' is not configured. Please add anthropic configuration to your config file."
+                        ));
+                    }
+                }
+                "databricks" => {
+                    if let Some(ref mut databricks) = config.providers.databricks {
+                        databricks.model = coach_model.clone();
+                    } else {
+                        return Err(anyhow::anyhow!(
+                            "Coach provider 'databricks' is not configured. Please add databricks configuration to your config file."
+                        ));
+                    }
+                }
+                _ => {}
+            }
+        }
+        
+        Ok(config)
+    }
+    
+    /// Create a config for the player agent in autonomous mode
+    pub fn for_player(&self) -> Result<Self> {
+        let mut config = self.clone();
+        
+        // Apply player-specific overrides if configured
+        if let Some(ref player_provider) = self.autonomous.player_provider {
+            config.providers.default_provider = player_provider.clone();
+        }
+        
+        if let Some(ref player_model) = self.autonomous.player_model {
+            // Apply model override to the player's provider
+            match config.providers.default_provider.as_str() {
+                "anthropic" => {
+                    if let Some(ref mut anthropic) = config.providers.anthropic {
+                        anthropic.model = player_model.clone();
+                    } else {
+                        return Err(anyhow::anyhow!(
+                            "Player provider 'anthropic' is not configured. Please add anthropic configuration to your config file."
+                        ));
+                    }
+                }
+                "databricks" => {
+                    if let Some(ref mut databricks) = config.providers.databricks {
+                        databricks.model = player_model.clone();
+                    } else {
+                        return Err(anyhow::anyhow!(
+                            "Player provider 'databricks' is not configured. Please add databricks configuration to your config file."
+                        ));
+                    }
+                }
+                _ => {}
+            }
+        }
+        
+        Ok(config)
+    }
 }

test-ai-requirements.sh

@@ -0,0 +1,39 @@
+#!/bin/bash
+# Test script for AI-enhanced interactive requirements mode
+
+echo "Testing AI-enhanced interactive requirements mode..."
+echo ""
+
+# Create a test workspace
+TEST_WORKSPACE="/tmp/g3-test-interactive-$(date +%s)"
+mkdir -p "$TEST_WORKSPACE"
+
+echo "Test workspace: $TEST_WORKSPACE"
+echo ""
+
+# Create sample brief input
+BRIEF_INPUT="build a calculator cli in rust with basic operations"
+
+echo "Brief input:"
+echo "---"
+echo "$BRIEF_INPUT"
+echo "---"
+echo ""
+
+echo "This will:"
+echo "1. Send brief input to AI"
+echo "2. AI generates structured requirements.md"
+echo "3. Show enhanced requirements"
+echo "4. Prompt for confirmation (y/e/n)"
+echo ""
+
+echo "To test manually, run:"
+echo "cargo run -- --autonomous --interactive-requirements --workspace $TEST_WORKSPACE"
+echo ""
+echo "Then type: $BRIEF_INPUT"
+echo "Press Ctrl+D"
+echo "Review the AI-generated requirements"
+echo "Choose 'y' to proceed, 'e' to edit, or 'n' to cancel"
+echo ""
+
+echo "Test workspace will be at: $TEST_WORKSPACE"