Antigravity × Terraform: AI-Driven Infrastructure Automation — Let AI Generate, Review, and Deploy Your IaC

Setup and context — When AI Writes Your Infrastructure Code

Infrastructure as Code (IaC) has become the standard for managing cloud resources. But let's face it — writing Terraform's HCL (HashiCorp Configuration Language) from scratch means memorizing hundreds of resource definitions across cloud providers, keeping up with API changes, and manually checking for security misconfigurations.

What if your AI coding assistant could handle all of that?

By integrating Antigravity's AI agents with Terraform, you can describe infrastructure requirements in plain English, have production-ready HCL generated automatically, and get instant AI-powered reviews of your terraform plan output — all while enforcing your team's security and cost policies.

What you'll learn:

How to configure Antigravity for efficient Terraform project development
Techniques for AI-powered Terraform code generation
A practical workflow for AI-reviewing terraform plan diffs
Building automated security and cost optimization checklists with AI

Prerequisites: Familiarity with Terraform basics and at least one cloud provider (AWS examples used throughout)

Environment Setup

Required Tools

Make sure you have the following installed before getting started.

# Verify Terraform installation
terraform --version
# Terraform v1.10.x or later recommended
 
# Verify Antigravity IDE is up to date
# Check for latest version in the IDE
 
# AWS CLI (using AWS as our example provider)
aws --version
# aws-cli/2.x.x or later

Project Structure

A well-organized project structure helps the AI agent understand your infrastructure layout and generate contextually appropriate code.

infra/
├── environments/
│   ├── dev/
│   │   ├── main.tf
│   │   ├── variables.tf
│   │   └── terraform.tfvars
│   ├── staging/
│   └── production/
├── modules/
│   ├── networking/
│   ├── compute/
│   ├── database/
│   └── monitoring/
├── .antigravity/
│   └── agents.md          # AI agent configuration
└── scripts/
    └── plan-review.sh     # Automated AI review script

Configuring Antigravity's IaC Agent

Providing Context with agents.md

The key to getting high-quality Terraform code from Antigravity is giving the AI agent proper context. Create a .antigravity/agents.md file in your project root.

# Infrastructure Agent Configuration
 
## Project Context
- Cloud Provider: AWS (ap-northeast-1)
- Terraform Version: >= 1.10
- State Backend: S3 + DynamoDB locking
- Environments: dev, staging, production
 
## Coding Standards
- All resources must have Name and Environment tags
- Use modules for reusable components
- No hardcoded credentials — use AWS IAM roles
- Enable encryption at rest for all storage resources
- VPC flow logs must be enabled in all environments
 
## Security Requirements
- No public S3 buckets unless explicitly approved
- All security groups must have explicit egress rules
- RDS instances must not be publicly accessible
- Enable AWS CloudTrail in all accounts

With this configuration in place, the AI agent automatically factors in your project-specific requirements every time it generates or reviews Terraform code.

Custom Commands for Terraform Workflows

Set up custom commands in Antigravity to streamline common Terraform tasks.

{
  "customCommands": {
    "tf-gen": {
      "description": "Generate Terraform code from natural language",
      "prompt": "Generate Terraform code based on the following requirements. Follow the security requirements in agents.md and use proper variable parameterization: "
    },
    "tf-review": {
      "description": "AI review of terraform plan output",
      "prompt": "Review the following terraform plan output. Flag any security risks, cost implications, best practice violations, and rollback concerns: "
    }
  }
}

AI-Powered Terraform Code Generation

The Basic Generation Flow

Here's a real-world example of describing infrastructure requirements in natural language and having Antigravity generate the corresponding Terraform code.

Prompt:

"Create a production RDS PostgreSQL instance with Multi-AZ deployment, encryption enabled, and 7-day automated backup retention."

AI-generated code:

# modules/database/main.tf
 
resource "aws_db_subnet_group" "main" {
  name       = "${var.project_name}-${var.environment}-db-subnet"
  subnet_ids = var.private_subnet_ids
 
  tags = {
    Name        = "${var.project_name}-${var.environment}-db-subnet"
    Environment = var.environment
    ManagedBy   = "terraform"
  }
}
 
resource "aws_db_instance" "postgresql" {
  identifier     = "${var.project_name}-${var.environment}-postgres"
  engine         = "postgres"
  engine_version = var.postgres_version
  instance_class = var.db_instance_class
 
  # Storage configuration
  allocated_storage     = var.allocated_storage
  max_allocated_storage = var.max_allocated_storage
  storage_type          = "gp3"
  storage_encrypted     = true  # Encryption enabled
  kms_key_id            = var.kms_key_arn
 
  # Network configuration
  db_subnet_group_name   = aws_db_subnet_group.main.name
  vpc_security_group_ids = [var.db_security_group_id]
  publicly_accessible    = false  # No public access
 
  # High availability
  multi_az = var.environment == "production" ? true : false
 
  # Backup configuration
  backup_retention_period = 7
  backup_window           = "03:00-04:00"
  maintenance_window      = "sun:04:00-sun:05:00"
 
  # Deletion protection
  deletion_protection = var.environment == "production" ? true : false
  skip_final_snapshot = var.environment != "production"
  final_snapshot_identifier = var.environment == "production" ? "${var.project_name}-final-snapshot" : null
 
  tags = {
    Name        = "${var.project_name}-${var.environment}-postgres"
    Environment = var.environment
    ManagedBy   = "terraform"
  }
}
 
# Output: connection details
# endpoint = aws_db_instance.postgresql.endpoint
# port     = aws_db_instance.postgresql.port

Notice how the AI automatically incorporates encryption, disables public access, and adds environment-aware conditional logic — all based on the agents.md security requirements.

Generating Cross-Module Dependencies

When generating infrastructure that spans multiple modules, the AI agent resolves dependency chains automatically.

# environments/production/main.tf
 
module "networking" {
  source = "../../modules/networking"
 
  project_name = var.project_name
  environment  = "production"
  vpc_cidr     = "10.0.0.0/16"
 
  # AI suggests appropriate AZ distribution
  availability_zones = ["ap-northeast-1a", "ap-northeast-1c", "ap-northeast-1d"]
}
 
module "database" {
  source = "../../modules/database"
 
  project_name       = var.project_name
  environment        = "production"
  private_subnet_ids = module.networking.private_subnet_ids  # Dependency resolved
  db_security_group_id = module.networking.db_security_group_id
  kms_key_arn        = module.encryption.kms_key_arn
 
  postgres_version    = "16.4"
  db_instance_class   = "db.r6g.large"
  allocated_storage   = 100
  max_allocated_storage = 500
}
 
module "monitoring" {
  source = "../../modules/monitoring"
 
  project_name  = var.project_name
  environment   = "production"
  db_identifier = module.database.db_instance_id  # Dependency resolved
  alarm_sns_topic_arn = var.alarm_sns_topic_arn
}

AI-Powered terraform plan Review Workflow

Automated Review Script

Build a script that captures terraform plan output and feeds it to the AI agent for review.

#!/bin/bash
# scripts/plan-review.sh
# Feed terraform plan output to AI for review
 
set -euo pipefail
 
ENVIRONMENT="${1:-dev}"
PLAN_FILE="/tmp/tfplan-${ENVIRONMENT}.out"
 
echo "=== Terraform Plan for ${ENVIRONMENT} ==="
 
cd "environments/${ENVIRONMENT}"
 
# Run plan and save output
terraform plan -out="${PLAN_FILE}" -no-color 2>&1 | tee /tmp/plan-output.txt
 
# Extract change summary
CHANGES=$(terraform show -no-color "${PLAN_FILE}" 2>/dev/null)
 
echo ""
echo "=== AI Review Request ==="
 
cat <<EOF > /tmp/review-request.txt
## Terraform Plan Review Request
 
### Environment: ${ENVIRONMENT}
### Plan Output:
\`\`\`
${CHANGES}
\`\`\`
 
### Review Checklist:
1. Are there any security risks?
2. Is the cost impact reasonable?
3. What is the blast radius for production?
4. Does this follow infrastructure best practices?
5. Can these changes be safely rolled back?
EOF
 
echo "Review request saved to /tmp/review-request.txt"

What the AI Reviews

The Antigravity agent evaluates terraform plan output across several dimensions.

Security checks:

Security groups open to 0.0.0.0/0
S3 bucket public access settings
Encryption at rest and in transit
IAM policies following least-privilege principle

Cost analysis:

Instance type appropriateness for the workload
Comparison with Reserved Instance pricing
Identification of potentially unnecessary resources

Availability checks:

Multi-AZ configuration where appropriate
Backup and retention settings
Health check configuration

Security and Cost Optimization with AI

AI-Generated Sentinel Policies

Combine HashiCorp Sentinel with Antigravity to generate and manage infrastructure policies.

# sentinel/policies/enforce-encryption.sentinel
# AI-generated security policy
 
import "tfplan/v2" as tfplan
 
# Verify encryption on all S3 buckets
s3_buckets = filter tfplan.resource_changes as _, rc {
    rc.type is "aws_s3_bucket" and
    (rc.change.actions contains "create" or rc.change.actions contains "update")
}
 
encryption_check = rule {
    all s3_buckets as _, bucket {
        bucket.change.after.server_side_encryption_configuration is not null
    }
}
 
# Verify encryption on all RDS instances
rds_instances = filter tfplan.resource_changes as _, rc {
    rc.type is "aws_db_instance" and
    (rc.change.actions contains "create" or rc.change.actions contains "update")
}
 
rds_encryption_check = rule {
    all rds_instances as _, db {
        db.change.after.storage_encrypted is true
    }
}
 
main = rule {
    encryption_check and rds_encryption_check
}

Automated Cost Estimation

# Integrate Infracost with Antigravity for cost analysis
# AI analyzes cost estimates and suggests optimizations
 
infracost breakdown --path environments/production \
  --format json \
  --out-file /tmp/cost-estimate.json
 
# Feed results to Antigravity agent for optimization analysis
echo "Cost estimate saved — pass to Antigravity agent
for optimization recommendations"

Putting It Together: CI/CD Pipeline Integration

GitHub Actions × Antigravity × Terraform

# .github/workflows/terraform-ai-review.yml
name: Terraform AI Review
 
on:
  pull_request:
    paths:
      - 'infra/**'
 
jobs:
  plan-and-review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
 
      - name: Setup Terraform
        uses: hashicorp/setup-terraform@v3
        with:
          terraform_version: "1.10.0"
 
      - name: Terraform Init
        working-directory: infra/environments/dev
        run: terraform init
 
      - name: Terraform Plan
        working-directory: infra/environments/dev
        run: |
          terraform plan -no-color -out=tfplan 2>&1 | tee plan-output.txt
 
      - name: AI Security Review
        run: |
          # Send plan output to AI review
          echo "## AI Infrastructure Review" >> $GITHUB_STEP_SUMMARY
          echo "Plan output saved for AI agent review" >> $GITHUB_STEP_SUMMARY
 
      - name: Cost Estimation
        uses: infracost/actions/setup@v3
        with:
          api-key: ${{ secrets.INFRACOST_API_KEY }}
 
      - name: Generate Cost Report
        run: |
          infracost breakdown \
            --path infra/environments/dev \
            --format table

Common Errors and Troubleshooting

State Lock Conflicts

If the AI agent attempts to run multiple terraform apply commands simultaneously, state lock conflicts will occur.

# Error message
# Error: Error acquiring the state lock
 
# Fix: check lock info and release
terraform force-unlock <LOCK_ID>
 
# Prevention: limit concurrent runs in CI/CD
# Use GitHub Actions concurrency settings

Type Errors in AI-Generated Code

When AI-generated HCL has type mismatches, catch them early with validation.

# Always run after generation
terraform validate
 
# Expected output:
# Success! The configuration is valid.

Wrapping Up — The Future of AI-Powered Infrastructure Management

Integrating Antigravity with Terraform unlocks a fundamentally different approach to infrastructure management.

Faster code generation — Describe what you need in plain English, and the AI produces security-compliant Terraform code automatically
Automated reviews — Every terraform plan gets instant AI analysis for security risks, cost implications, and best practice violations
Consistent policies — Rules defined in agents.md are enforced across every code generation, keeping your entire team aligned
Lower learning curve — You don't need to memorize every HCL resource type when AI generates best-practice code for you

Start small — try it on a dev environment first, then gradually expand to staging and production as you build confidence in the workflow. The combination of AI intelligence and Terraform's declarative power is a game-changer for infrastructure teams of any size.

Internal links: