# AI Integration - Intelligent Infrastructure Provisioning

The provisioning platform integrates AI capabilities to provide intelligent assistance for infrastructure configuration, deployment, and troubleshooting.
This section documents the AI system architecture, features, and usage patterns.

## Overview

The AI integration consists of multiple components working together to provide intelligent infrastructure provisioning:

- **typdialog-ai**: AI-assisted form filling and configuration
- **typdialog-ag**: Autonomous AI agents for complex workflows
- **typdialog-prov-gen**: Natural language to Nickel configuration generation
- **ai-service**: Core AI service backend with multi-provider support
- **mcp-server**: Model Context Protocol server for LLM integration
- **rag**: Retrieval-Augmented Generation for contextual knowledge

## Key Features

### Natural Language Configuration

Generate infrastructure configurations from plain English descriptions:
```bash
provisioning ai generate "Create a production PostgreSQL cluster with encryption and daily backups"
```

### AI-Assisted Forms

Real-time suggestions and explanations as you fill out configuration forms via typdialog web UI.

### Intelligent Troubleshooting

AI analyzes deployment failures and suggests fixes:
```bash
provisioning ai troubleshoot deployment-12345
```

###

 Configuration Optimization
AI reviews configurations and suggests performance and security improvements:
```bash
provisioning ai optimize workspaces/prod/config.ncl
```

### Autonomous Agents
AI agents execute multi-step workflows with minimal human intervention:
```bash
provisioning ai agent --goal "Set up complete dev environment for Python app"
```

## Documentation Structure

- [Architecture](architecture.md) - AI system architecture and components
- [Natural Language Config](natural-language-config.md) - NL to Nickel generation
- [AI-Assisted Forms](ai-assisted-forms.md) - typdialog-ai integration
- [AI Agents](ai-agents.md) - typdialog-ag autonomous agents
- [Config Generation](config-generation.md) - typdialog-prov-gen details
- [RAG System](rag-system.md) - Retrieval-Augmented Generation
- [MCP Integration](mcp-integration.md) - Model Context Protocol
- [Security Policies](security-policies.md) - Cedar policies for AI
- [Troubleshooting with AI](troubleshooting-with-ai.md) - AI debugging workflows
- [API Reference](api-reference.md) - AI service API documentation
- [Configuration](configuration.md) - AI system configuration guide
- [Cost Management](cost-management.md) - Managing LLM API costs

## Quick Start

### Enable AI Features

```bash
# Edit provisioning config
vim provisioning/config/ai.toml

# Set provider and enable features
[ai]
enabled = true
provider = "anthropic"  # or "openai" or "local"
model = "claude-sonnet-4"

[ai.features]
form_assistance = true
config_generation = true
troubleshooting = true
```

### Generate Configuration from Natural Language

```bash
# Simple generation
provisioning ai generate "PostgreSQL database with encryption"

# With specific schema
provisioning ai generate \
  --schema database \
  --output workspaces/dev/db.ncl \
  "Production PostgreSQL with 100GB storage and daily backups"
```

### Use AI-Assisted Forms

```bash
# Open typdialog web UI with AI assistance
provisioning workspace init --interactive --ai-assist

# AI provides real-time suggestions as you type
# AI explains validation errors in plain English
# AI fills multiple fields from natural language description
```

### Troubleshoot with AI

```bash
# Analyze failed deployment
provisioning ai troubleshoot deployment-12345

# AI analyzes logs and suggests fixes
# AI generates corrected configuration
# AI explains root cause in plain language
```

## Security and Privacy

The AI system implements strict security controls:

- ✅ **Cedar Policies**: AI access controlled by Cedar authorization
- ✅ **Secret Isolation**: AI cannot access secrets directly
- ✅ **Human Approval**: Critical operations require human approval
- ✅ **Audit Trail**: All AI operations logged
- ✅ **Data Sanitization**: Secrets/PII sanitized before sending to LLM
- ✅ **Local Models**: Support for air-gapped deployments

See [Security Policies](security-policies.md) for complete details.

## Supported LLM Providers

| Provider | Models | Best For |
| ---------- | -------- | ---------- |
| **Anthropic** | Claude Sonnet 4, Claude Opus 4 | Complex configs, long context |
| **OpenAI** | GPT-4 Turbo, GPT-4 | Fast suggestions, tool calling |
| **Local** | Llama 3, Mistral | Air-gapped, privacy-critical |

## Cost Considerations

AI features incur LLM API costs. The system implements cost controls:

- **Caching**: Reduces API calls by 50-80%
- **Rate Limiting**: Prevents runaway costs
- **Budget Limits**: Daily/monthly cost caps
- **Local Models**: Zero marginal cost for air-gapped deployments

See [Cost Management](cost-management.md) for optimization strategies.

## Architecture Decision Record

The AI integration is documented in:
- [ADR-015: AI Integration Architecture](../architecture/adr/adr-015-ai-integration-architecture.md)

## Next Steps

1. Read [Architecture](architecture.md) to understand AI system design
2. Configure AI features in [Configuration](configuration.md)
3. Try [Natural Language Config](natural-language-config.md) for your first AI-generated config
4. Explore [AI Agents](ai-agents.md) for automation workflows
5. Review [Security Policies](security-policies.md) to understand access controls

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**Version**: 1.0
**Last Updated**: 2025-01-08
**Status**: Active