trax/BACKEND_DEVELOPER_AGENT_SUMMARY.md

Backend Developer Agent - Capabilities & Tools

🎯 Agent Overview

The Backend Python Developer Agent is a comprehensive representation of the first backend developer hire for the Trax media processing platform. This agent has access to specific tools and capabilities needed to build the protocol-based transcription pipeline from v1 to v4.

Agent Profile

• Name: Backend Python Developer
• Role: Senior Backend Developer
• Experience Level: Senior
• Salary Range: $150,000 - $200,000
• Current Focus: Phase 1: Foundation (Weeks 1-2)

🛠️ Available Tools by Category

1. Core Development Tools

Tools: 3 | Skills: 8

Python 3.11+ Development

• Async Programming: Write async/await code for concurrent operations
• Protocol Design: Create protocol-based service interfaces
• Type Hints: Use comprehensive type hints throughout

uv Package Manager

• Install Dependencies: Install project dependencies
• Compile Requirements: Generate requirements.txt from pyproject.toml
• Run Commands: Execute Python commands with uv

Click CLI Framework

• Create transcription commands
• Build batch processing interface
• Implement export functionality

2. Database Tools

Tools: 2 | Skills: 4

PostgreSQL + SQLAlchemy

• Model Definition: Define SQLAlchemy models with JSONB
• Database Migrations: Create and apply Alembic migrations
• JSONB Operations: Perform JSONB queries and operations

Database Registry Pattern

• Implement centralized model registry
• Handle multiple database connections
• Manage model relationships

3. ML Integration Tools

Tools: 3 | Skills: 6

Whisper Integration

• Model Loading: Load Whisper models with faster-whisper
• Audio Transcription: Transcribe audio files with Whisper
• Chunking Strategy: Handle large audio files with chunking

Protocol-Based Services

• Design service interfaces
• Implement version compatibility
• Create swappable components

DeepSeek API Integration

• Enhance transcript quality
• Implement structured outputs
• Handle API rate limits

4. Testing Tools

Tools: 2 | Skills: 4

pytest with Real Files

• Real File Testing: Test with actual audio files instead of mocks
• Test Fixtures: Create reusable test fixtures with real files
• Performance Testing: Benchmark transcription performance

Coverage Reporting

• Achieve >80% code coverage
• Identify untested code
• Track test quality

5. Architecture Tools

Tools: 3 | Skills: 3

Iterative Pipeline Design

• Version Management: Manage different pipeline versions
• Backward Compatibility: Ensure new versions work with old data
• Feature Flags: Enable/disable features by version

Batch Processing System

• Process multiple files
• Handle independent failures
• Track progress

Caching Strategy

• Cache expensive operations
• Implement different TTLs
• Handle cache invalidation

6. Performance Tools

Tools: 2 | Skills: 3

Performance Profiling

• Profile transcription speed
• Optimize memory usage
• Benchmark improvements

M3 Hardware Optimization

• Metal Performance Shaders: Use M3 GPU for Whisper inference
• Memory Optimization: Optimize memory usage for large files
• Performance Profiling: Profile and optimize performance

7. Deployment Tools

Tools: 2 | Skills: 2

Docker Containerization

• Create production images
• Handle dependencies
• Optimize image size

CI/CD Pipeline

• Automate testing
• Deploy to staging
• Monitor deployments

📊 Agent Statistics

• Total Tools Available: 17
• Required Skills: 30
• Categories: 7
• Development Phases: 4 (v1, v2, v3, v4)

🎯 Phase-Specific Tool Availability

Phase 1 (v1): Foundation

Focus: Basic Whisper transcription (95% accuracy, <30s for 5min audio) Tools: Core Development, Database, Testing

Phase 2 (v2): Enhancement

Focus: AI enhancement (99% accuracy, <35s processing) Tools: + ML Integration

Phase 3 (v3): Optimization

Focus: Multi-pass accuracy (99.5% accuracy, <25s processing) Tools: + Performance

Phase 4 (v4): Advanced Features

Focus: Speaker diarization (90% speaker accuracy) Tools: + Deployment

🚀 Success Metrics

The agent must achieve these targets:

Metric	Target
Processing Speed	5-minute audio in <30 seconds
Accuracy	99.5% transcription accuracy with multi-pass
Batch Capacity	Process 100+ files efficiently
Memory Usage	<4GB peak memory usage
Cost	<$0.01 per transcript
Code Coverage	>80% with real file testing
CLI Response	<1 second CLI response time
File Size	Handle files up to 500MB
Data Loss	Zero data loss on errors

💻 Development Workflow

1. Environment Setup

uv venv
source .venv/bin/activate
uv pip install -e .[dev]

2. Database Setup

alembic revision -m 'Initial schema'
alembic upgrade head

3. Core Development

class TranscriptionService(Protocol):
    async def transcribe(self, audio: Path) -> Transcript: ...

4. ML Integration

from faster_whisper import WhisperModel
model = WhisperModel('distil-large-v3', device='mps')

5. Testing

uv run pytest tests/
uv run pytest --cov=src

6. Performance Optimization

model.transcribe(audio_path, chunk_length=30, overlap=2)
python -m cProfile src/main.py

🔧 Key Capabilities

Protocol-Based Architecture

• Design clean service interfaces
• Implement dependency injection
• Create swappable components
• Maintain version compatibility

Real File Testing

• Test with actual audio files
• No mocks in test suite
• Benchmark real performance
• Handle edge cases

Performance Optimization

• M3 hardware acceleration
• Memory usage optimization
• Chunking for large files
• Profiling and benchmarking

Batch Processing

• Handle 100+ files efficiently
• Independent failure handling
• Progress tracking
• Queue management

📁 File Structure

src/agents/
├── backend_developer_agent.py          # Main agent definition
├── tools/
│   └── backend_developer_tools.py      # Detailed tool definitions
└── demo_backend_developer.py           # Demo script

🎮 Usage Examples

Running the Demo

cd src/agents
python demo_backend_developer.py

Checking Tool Availability

from agents.backend_developer_agent import check_tool_availability

# Check if agent can use a specific tool
can_use_whisper = check_tool_availability("Whisper Integration")
print(f"Can use Whisper: {can_use_whisper}")

Getting Tools by Category

from agents.tools.backend_developer_tools import get_tools_by_category

# Get all database tools
db_tools = get_tools_by_category("database")
for tool in db_tools:
    print(f"Database tool: {tool.name}")

Getting Phase-Specific Tools

from agents.tools.backend_developer_tools import get_tools_by_phase

# Get tools available in v1
v1_tools = get_tools_by_phase("v1")
for tool in v1_tools:
    print(f"v1 tool: {tool.name}")

🎯 Next Steps

1. Run the demo script to see all capabilities
2. Review the job posting for hiring
3. Set up development environment for the agent
4. Begin Phase 1 development with core tools
5. Implement protocol-based architecture from day one

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The Backend Developer Agent is ready to build the future of media processing with clean, scalable, and reliable architecture! 🚀