The big gap in AI agents isn’t intelligence — it’s hands. An LLM can write a perfect playbook, but if it can’t hold a persistent interactive session, follow a live log, drive the SSH client through a password prompt, or watch the browser as it runs, it’s still stuck translating thought into brittle one-shot tool calls.
I went looking at what’s actually shipping right now to give agents a real computer. Six repos, three distinct layers: full sandboxes, interactive PTY servers, and browser control. Here’s how they stack up.
Most AI coding tools take the same approach: give one agent a prompt, let it run, hope it doesn’t forget the process halfway through. TAKT flips that — the workflow owns the process, not the agent.
takt (nrslib/takt) — 1,277★, TypeScript, MIT. Defines AI coding workflows as YAML pipelines: plan → implement → review → fix → re-review. Each step gets its own persona, context, permissions, and output contract. Agents execute the steps; the workflow decides what happens next.
The coding agent space is moving fast. Really fast. It feels like every week there’s a new agent claiming to be faster, cheaper, or smarter than the last. I spent some time digging through the latest entrants to see what’s actually novel and what’s just another Claude Code wrapper.
Here’s what I found.
Before diving into the agents themselves, there’s an important backdrop: the Agent Client Protocol (ACP). Think of it as MCP but for agents — a standard way for IDEs, CLIs, and other tools to discover, authenticate, and communicate with coding agents.
