# AI Agent Training Guide for Closed Source Software

## Overview

This guide outlines the process for integrating and training AI agents to work with closed source software applications. The approach focuses on establishing plaintext entrypoints and progressive learning phases.

## Prerequisites

### Plaintext Entrypoint Requirements

AI agents require accessible plaintext interfaces to interact with closed source software. Common entrypoint formats include:

- **Project Files**: JSON files representing component configurations
- **Import/Export Formats**:
  - JSON
  - CSV
  - YAML
  - XML
  - Other structured text formats

## Training Phases

### Phase 1: Integration Setup

**Priority**: Complete this phase first to validate technical feasibility before proceeding.

#### Key Steps

1. **Expose MCP Tool Entrypoint**
   - Create an MCP (Model Context Protocol) tool interface for the software
   - Enable HTTP-based MCP connections for remote agent access
   - Implement comprehensive error logging and reporting

2. **Error Feedback Implementation**
   - Ensure all error logs are returned to the MCP caller
   - Include detailed error messages and stack traces
   - Provide contextual information about failed operations
   - This feedback loop enables the AI agent to learn from mistakes

#### Success Criteria
- MCP tool successfully exposed and accessible
- Error reporting mechanism functioning properly
- Basic read/write operations validated

### Phase 2: Knowledge Acquisition

**Focus**: Build foundational understanding through documentation and examples.

#### Documentation Review
- Provide comprehensive software documentation
- Include API references, user guides, and technical specifications
- Ensure documentation covers all exposed functionality

#### Curated Examples
- Create a library of validated, working examples
- Cover common use cases and patterns
- Include both simple and complex scenarios
- Annotate examples with explanations of key concepts

#### Best Practices
- Start with simple, isolated functionality
- Progress to more complex, integrated features
- Maintain a clear mapping between documentation and examples

### Phase 3: Interactive Learning and Validation

**Focus**: Deepen understanding through hands-on interaction and systematic testing.

**Note**: This phase can be conducted in either supervised or unsupervised mode. Autonomous agents like Claude Code can execute this phase independently, making it highly scalable.

#### Unit Test Development
- Have the agent autonomously create comprehensive unit tests
- Define test cases for:
  - Expected successful operations
  - Expected failure scenarios
  - Edge cases and boundary conditions
  - Error handling verification

#### Direct MCP Interaction
- Enable autonomous interaction with the MCP server
- Test various input combinations and parameters
- Document successful patterns and common pitfalls
- Build a knowledge base of working configurations
- For unsupervised mode: implement safeguards and monitoring

#### Validation Requirements
- Implement automated regression testing to prevent knowledge degradation
- Maintain a test suite that validates agent understanding
- Automated review cycles to ensure continued accuracy
- Version control for test cases and validated patterns
- For autonomous operation: establish clear success/failure metrics

## Implementation Checklist

- [ ] Identify suitable plaintext entrypoints in the software
- [ ] Implement MCP tool wrapper with error reporting
- [ ] Validate Phase 1 integration before proceeding
- [ ] Compile and organize software documentation
- [ ] Create and validate example library
- [ ] Design unit test framework and templates
- [ ] Establish validation and regression testing procedures
- [ ] Set up monitoring for agent performance (especially critical for unsupervised mode)
- [ ] Configure safeguards for autonomous operation

## Best Practices

### Security Considerations
- Limit agent permissions to necessary operations only
- Implement audit logging for all agent actions
- Regular security reviews of exposed interfaces

### Performance Optimization
- Cache frequently accessed data
- Implement rate limiting for API calls
- Monitor resource usage and optimize as needed

### Continuous Improvement
- Collect metrics on agent performance (both supervised and unsupervised runs)
- Regular updates to documentation and examples
- Iterative refinement of training materials
- Feedback loops for identifying gaps in agent knowledge
- Analysis of autonomous operation logs to improve training

## Troubleshooting

### Common Issues

1. **Integration Failures**
   - Verify MCP tool configuration
   - Check network connectivity for HTTP-based connections
   - Review error logs for authentication issues

2. **Learning Plateaus**
   - Expand example coverage
   - Provide more diverse test scenarios
   - Review and clarify ambiguous documentation

3. **Regression Problems**
   - Strengthen validation test suite
   - Implement automated regression testing
   - Version control for agent knowledge base

## Example Implementation: Gaea2 Terrain Generation

For a practical example of this guide in action, see the Gaea2 MCP implementation:
- Phase 1: HTTP MCP server at `192.168.0.152:8007` with comprehensive error logging
- Phase 2: Extensive documentation in `tools/mcp/mcp_gaea2/docs/` with 31 analyzed reference projects
- Phase 3: Unit test framework in `tools/mcp/mcp_gaea2/tests/` with automated validation

See [Gaea2 MCP Documentation](/tools/mcp/mcp_gaea2/docs/README.md) for detailed implementation.
