We're excited to introduce Atmos AI, an intelligent assistant built directly into Atmos CLI that understands your infrastructure-as-code like no other AI assistant can.

Unlike general-purpose AI coding assistants, Atmos AI has deep, native understanding of Atmos stacks, components, inheritance patterns, and infrastructure workflows. It's not just an AI that knows about code—it's an AI that truly understands your infrastructure.

With support for 7 AI providers (including local/offline Ollama), persistent sessions with full conversation memory, tool execution with granular permissions and persistent permission cache, specialized skills for specific tasks, and seamless IDE integration via MCP—Atmos AI brings the productivity patterns of industry-leading AI systems to infrastructure management.

The Problem

Infrastructure-as-code management is complex. Engineers lose hours searching documentation, debugging YAML configurations, understanding stack inheritance across dozens of files, and onboarding team members. The problem isn't lack of tools—it's the cognitive overhead of managing complex infrastructure.

The Solution: Atmos AI

Atmos AI solves this through infrastructure-aware intelligence. It's like having an expert Atmos engineer available 24/7, ready to analyze your stacks, validate configurations, answer questions, and help with best practices.

See It in Action

Ask a question about your infrastructure and Atmos AI automatically inspects your stacks, components, and configuration:

atmos ai ask (Claude)

$ atmos ai ask "what stacks and components do we have in the infra?"

👽 Thinking...

  I'll list all the available stacks and components in your infrastructure.

  Based on the infrastructure configuration, here are the stacks and components:

  ## Stacks (4 total)

  1. **ue1-network** - Network stack in us-east-1
  2. **ue1-prod** - Production stack in us-east-1
  3. **uw2-network** - Network stack in us-west-2
  4. **uw2-prod** - Production stack in us-west-2

  ## Components by Stack

  ### ue1-network

  • tgw/attachment - Transit Gateway attachment
  • tgw/hub - Transit Gateway hub
  • vpc - Virtual Private Cloud

  ### ue1-prod

  • tgw/attachment - Transit Gateway attachment
  • vpc - Virtual Private Cloud

  ### uw2-network

  • tgw/attachment - Transit Gateway attachment
  • tgw/cross-region-hub-connector - Cross-region Transit Gateway connector
  • vpc - Virtual Private Cloud

  ### uw2-prod

  • tgw/attachment - Transit Gateway attachment
  • vpc - Virtual Private Cloud

  ## Summary

  Your infrastructure is organized across two AWS regions (us-east-1 and us-west-2),
  with dedicated network and production stacks in each region. The network stacks
  contain Transit Gateway hubs for network connectivity, while the production stacks
  use Transit Gateway attachments to connect to the network infrastructure. All stacks
  include VPC components for network isolation.

What Makes Atmos AI Different?

• Deep Atmos Understanding — Knows stack structure, inheritance patterns, component relationships, and provides context-aware recommendations.
• Full Conversation Memory — Remembers entire chat history within sessions. Resume conversations days or weeks later with full context.
• Tool Execution — Analyzes infrastructure automatically via read-only operations, real-time YAML/Terraform validation, and a granular permission system.
• Multi-Provider Support — 7 providers including local/offline Ollama. Switch providers mid-conversation with Ctrl+P.
• Persistent Sessions — SQLite-backed storage with named sessions, auto-compact, and cross-platform support.
• Non-Interactive Execution — Run AI prompts programmatically for scripting and CI/CD with structured JSON output.

Key Features

1. Infrastructure-Aware Intelligence

Atmos AI has native tools to inspect your infrastructure:

You: What VPC CIDR does production use?

AI: Let me check your configuration...
    [Executes: atmos describe component vpc -s prod-use1-network]

    Your production VPC uses CIDR 10.2.0.0/16 with public subnets,
    private subnets, and NAT Gateways enabled in all AZs.

Available tools include atmos_describe_component, atmos_list_stacks, atmos_validate_stacks, validate_file_lsp, file operations, and web search. See Tool System documentation for the full list.

2. Real-Time Validation with LSP

Atmos AI integrates with Language Server Protocol to provide IDE-quality validation directly in the chat — catching typos, deprecated properties, and schema violations in YAML, Terraform, and HCL files.

3. Persistent Sessions with Full Memory

Unlike basic chatbots that forget context, Atmos AI remembers everything within a session. Start an architecture discussion on Monday, resume Tuesday with full context, and reference earlier decisions a week later — all with the same named session.

atmos ai chat --session vpc-migration

Sessions are stored in SQLite with visual session picker (Ctrl+L), provider awareness, and auto-compact for extended conversations. See Sessions documentation.

4. Specialized AI Skills

Atmos AI provides 21+ specialized skills you can install from the marketplace:

# Install all official skills with one command
atmos ai skill install cloudposse/atmos

Skills include atmos-terraform, atmos-stacks, atmos-validation, atmos-components, atmos-config, and many more — each with tailored prompts and tool access for its domain.

Switch skills with Ctrl+A during conversations! See AI Skills documentation.

5. Multi-Provider Support

Choose the right AI for your needs:

Provider	Best For	Privacy
Anthropic (Claude)	Complex reasoning, analysis	Cloud
OpenAI (GPT)	Code generation, refactoring	Cloud
Google (Gemini)	Large context windows	Cloud
xAI (Grok)	Real-time knowledge	Cloud
Ollama (Local)	Complete privacy, offline	100% Local
AWS Bedrock	Enterprise, AWS-native	AWS
Azure OpenAI	Enterprise, Azure-native	Azure

Ollama runs AI models entirely on your machine — zero API costs, complete privacy, offline capable, and compliance ready. Enterprise teams can use AWS Bedrock or Azure OpenAI for data residency, VPC isolation, and audit logging.

See AI Providers documentation for setup instructions.

6. Project Instructions (ATMOS.md)

Provide project-specific context to the AI across all sessions via an ATMOS.md file — human-readable Markdown that's version-controlled with your repo. Include your organization's naming conventions, common commands, stack patterns, and CIDR allocations.

See Project Instructions documentation.

7. Model Context Protocol (MCP) Integration

Use Atmos tools from any MCP-compatible client — Claude Desktop, VSCode/Cursor, or custom clients:

{
  "mcpServers": {
    "atmos": {
      "command": "atmos",
      "args": ["mcp", "start"]
    }
  }
}

Learn more: MCP Server documentation

8. Permission System

A three-tier security model protects your infrastructure:

• Allowed Tools — Execute without prompting (e.g., atmos_describe_component, atmos_list_stacks, read_file)
• Restricted Tools — Require confirmation (e.g., edit_file, write_stack_file, write_component_file)
• Blocked Tools — Never execute (e.g., execute_bash_command, execute_atmos_command)

Permission decisions persist across sessions in .atmos/ai.settings.local.json, reducing prompt fatigue by 80%+. Every tool execution is logged with timestamp, user, and context.

See Tool System documentation for configuration details.

9. Non-Interactive Execution and CI/CD Integration

Execute AI prompts programmatically with structured output:

# Simple execution
atmos ai exec "List all production stacks"

# JSON output for parsing
atmos ai exec "Analyze VPC configuration" --format json > analysis.json

# CI/CD integration
result=$(atmos ai exec "Check for security issues" --format json)
if echo "$result" | jq -e '.success == false'; then
  exit 1
fi

JSON Output Structure:

{
  "success": true,
  "response": "Analysis complete...",
  "tool_calls": [{"tool": "atmos_list_stacks", "success": true}],
  "tokens": {"prompt": 120, "completion": 80, "cached": 50},
  "metadata": {"model": "claude-sonnet-4-6", "provider": "anthropic"}
}

Supports multiple output formats (JSON, text, markdown), standard exit codes, stdin piping, and session context for multi-turn scripts. Learn more: atmos ai exec documentation

10. Token Caching for Cost Savings

Atmos AI supports prompt caching to dramatically reduce API costs — up to 90% savings by reusing frequently-sent content like system prompts and project instructions.

Provider	Caching Discount
Anthropic	90%
OpenAI / Azure	50%
Gemini	Free
Grok	75%
Bedrock	Up to 90%

Most providers cache automatically. For Anthropic, enable explicit cache markers in atmos.yaml:

ai:
  providers:
    anthropic:
      cache:
        enabled: true
        cache_system_prompt: true
        cache_project_instructions: true

Learn more: Token Caching documentation

Getting Started

1. Configure Atmos AI

Add to your atmos.yaml:

atmos.yaml

ai:
  enabled: true
  default_provider: "anthropic"
  providers:
    anthropic:
      model: "claude-sonnet-4-6"
      api_key: !env "ANTHROPIC_API_KEY"

2. Set Up Your Provider

# For Claude (Anthropic)
export ANTHROPIC_API_KEY="sk-ant-..."

# For Ollama (Local/Offline) - no API key needed
ollama pull llama4

See AI Providers for all provider setup instructions.

3. Start Using Atmos AI

# Interactive chat
atmos ai chat

# Named session
atmos ai chat --session infrastructure-review

# Quick question
atmos ai ask "What components are in production?"

# Non-interactive execution
atmos ai exec "List all production stacks" --format json

# MCP server for Claude Desktop
atmos mcp start

What's Next?

We're continuously improving Atmos AI. Here's what's shipped and what's coming:

Recently Completed:

• Non-Interactive Execution (atmos ai exec)
• Structured JSON Output with standard exit codes
• Token Caching (Prompt Caching) — up to 90% cost savings
• Conversation Checkpointing — export/import sessions
• Automatic Context Discovery with .gitignore support
• Skill Marketplace — install community skills from the Agent Skills registry

Coming Soon:

• Enhanced LSP (HCL, JSON Schema)
• Advanced Analytics — token usage tracking, cost analysis
• Multi-Skill Workflows — skill delegation and collaboration
• IDE Plugins — native VSCode/JetBrains integration
• Private Skill Registries and advanced security

Learn More

• Configuration Guide - Complete configuration reference
• AI Providers - All 7 providers with setup instructions
• Tool System - Tool execution and permissions
• AI Skills - Marketplace-installed skills
• Sessions - Session management and auto-compact
• Project Instructions - ATMOS.md documentation
• MCP Server - Claude Desktop integration
• Troubleshooting - Common issues and solutions
• Atmos Documentation

Get Involved:

• GitHub Issues
• Community Discussions

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Happy infrastructure engineering!