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:

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:

provisioning ai troubleshoot deployment-12345

Configuration Optimization AI reviews configurations and suggests performance and security improvements:

provisioning ai optimize workspaces/prod/config.ncl

Autonomous Agents

AI agents execute multi-step workflows with minimal human intervention:

provisioning ai agent --goal "Set up complete dev environment for Python app"

Documentation Structure

Architecture - AI system architecture and components
Natural Language Config - NL to Nickel generation
AI-Assisted Forms - typdialog-ai integration
AI Agents - typdialog-ag autonomous agents
Config Generation - typdialog-prov-gen details
RAG System - Retrieval-Augmented Generation
MCP Integration - Model Context Protocol
Security Policies - Cedar policies for AI
Troubleshooting with AI - AI debugging workflows
API Reference - AI service API documentation
Configuration - AI system configuration guide
Cost Management - Managing LLM API costs

Quick Start

Enable AI Features

# 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

# 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

# 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

# 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 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 for optimization strategies.

Architecture Decision Record

The AI integration is documented in:

ADR-015: AI Integration Architecture

Next Steps

Read Architecture to understand AI system design
Configure AI features in Configuration
Try Natural Language Config for your first AI-generated config
Explore AI Agents for automation workflows
Review Security Policies to understand access controls

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