The AI Developer Stack
The 6 layers of every AI coding workflow.
Terminal. Agent harness. Skills. Config. Cloud runtimes. Project management. If you're using AI coding agents, you're already building this stack — whether you've named the layers or not.
The 6 Layers
Each layer builds on the one below it. Most developers start at Layer 1 and add layers as their AI workflow matures.
Terminal & Session Management
The foundation layer. Developers running AI agents need terminal multiplexing to manage parallel sessions, monitor multiple agents, and keep context across workspaces.
The pattern
The multi-agent terminal pattern: running Claude Code, Codex, or other AI agents in parallel tmux panes, each working on different parts of a codebase simultaneously.
Agent Harness & Orchestration
The fastest-growing layer. Agent harnesses wrap around AI coding agents (Claude Code, Codex, Cursor) to add guardrails, task management, and orchestration capabilities.
The pattern
Instead of giving an agent free rein, harnesses break work into supervised steps, enforce coding standards, and coordinate multiple agents working on the same project.
Skills & Plugins
The extensibility layer. Agent skills are reusable modules that teach your AI coding agent new capabilities, from domain-specific knowledge to tool integrations.
The pattern
Skills let you configure what your agent knows and can do, without rewriting prompts every session. Share skills across projects and teams for consistent AI behavior.
Config & Dotfiles
The personalization layer. Configuration files like CLAUDE.md, .cursorrules, and agent-specific config files define how your AI agent behaves in each project.
The pattern
Dotfiles for AI: developers check agent configuration into version control alongside their code, so anyone who clones the repo gets the same AI-assisted experience.
Remote Access & Cloud Runtimes
The infrastructure layer. Running AI agents in the cloud gives you more compute, persistent sessions, and the ability to run agents 24/7 without tying up your local machine.
The pattern
SSH into your agent: developers set up cloud VMs or containers running AI coding agents, then connect remotely. This enables parallel agent execution at scale.
Project Management for Agent Workflows
The planning layer. Traditional project management tools were designed for human-only workflows. New tools are built specifically for AI-assisted development patterns.
The pattern
AI-native project planning: tools that understand the agent-driven development cycle, from backlog grooming to automated task decomposition to review workflows.
Compare Workflow Tools
Use the compare engine to evaluate tools side by side. See features, pricing, and community ratings across the stack.
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