Agents build shallow plans, miss patterns, and require constant context management.
Atomic forces depth before execution: deep parallel codebase research, architecture-aware specs, and fully extensible autonomous workflows that land complex changes correctly.
Atomic wraps Claude Code, GitHub Copilot CLI, and OpenCode behind a single interface. Switch backends with a flag. Every workflow, sub-agent, and skill works identically across all three.
Vanilla agents are one generalist in one context window. They work on demos. They break on large codebases and complex tasks. They introduce security risks. Atomic fixes the structural problems.
Traditional agent discovery and codebase exploration is too shallow for complex tasks. Atomic dispatches 9 specialized sub-agents in parallel each with its own session, tools, and scope to perform deep codebase research. Analysis, implementation, and review run as separate concerns.
Giving an agent one task at a time is slow and fragile. Atomic decomposes specs into dependency DAGs, dispatches parallel workers, handles retries and deadlock detection, and supports dynamic task mutation mid-execution. A real scheduler, not a chat loop.
Basic project-level memory isn't sufficient detail. Atomic persists research docs, specs, and progress logs on disk. Each research→spec→execute cycle compounds knowledge. Push to git and it becomes shared memory for your entire team.
Autonomous, long-running coding agents can become black boxes without proper points of intervention. Atomic's UX is custom-built for full visibility. See exactly how tasks are decomposed, monitor every sub-agent in real time, and intervene at critical stepping stones. Engineers stay in the loop, not locked out of it.
All the infrastructure top AI engineering teams build internally ready out of the box.
/research-codebase dispatches parallel sub-agents to analyze architecture, locate files, find patterns, and pull library docs.
/create-spec converts research into an implementation contract. You review, refine, and approve before any code is written.
/ralph decomposes the spec into a task DAG. Workers implement in parallel. Reviewer audits. Fix cycles re-run automatically.
Come back to a completed PRs. Each cycle enriches persistent project memory, making the next one faster and more accurate.
Atomic wraps these same agents with multi-agent orchestration: 9 specialized sub-agents running in parallel with independent sessions, a graph execution engine for structured workflows, persistent cross-session memory, and automated review cycles. It's the difference between relying on the agent or model's built in behavior and a coordinated team following software development lifecycle best practices.
Atomic is agent-agnostic. It's an orchestration layer, not a replacement model. When better agents or models ship, Atomic makes them more effective. Proprietary tools can lock you into one vendor's agents and/or models. Atomic lets you swap between Claude Code, Github Copilot CLI, and OpenCode with a flag, and the entire workflow system, including sub-agents, memory, review cycles, works across all of them.
Prompts and context files help, but they can't give you parallel multi-agent execution, DAG-based task decomposition, automated code review with structured findings, context window management across long-running sessions, or persistent memory that compounds across runs. These are systems-level problems. Atomic solves them at the infrastructure layer so you don't have to reinvent the orchestration yourself every time.
No. Atomic ships with 9 specialized sub-agents, 11 pre-built skills, file-system memory, the full research→spec→execute pipeline, review cycles, and session persistence all out of the box. Run atomic init and every agent, skill, and workflow is ready. These are the same patterns top AI engineering teams build internally, pre-built and battle-tested so you can just use them.
Claude Code (via the Claude Agent SDK), GitHub Copilot CLI (via the Github Copilot SDK), and OpenCode (via the OpenCode SDK). Switch between them with -a claude, -a copilot, or -a opencode. All workflows, sub-agents, and skills work identically across backends through a unified interface.
It's recommended to use Atomic with a terminal that supports true color for the best visual experience. Popular options include Ghostty, iTerm2 (macOS), and Windows Terminal (Windows). These terminals properly render Atomic's rich colors and styling.