Amp wants AI agents to handle more than program code. An OpenAI article published on October 14 describes how the company also uses them for documentation, design, and marketing. The interesting part is less a single new model than the combination of a working environment, specialized assistants, and an interface that keeps multiple assignments organized.
Key takeaways
- Amp combines different AI models with tools and dedicated working environments for agents.
- The Puck assistant can launch tasks, find ongoing work, and pass along further instructions.
- Cloud machines called orbs keep working with your laptop closed; alternatively, the command-line version runs on your own computer.
- A connected ChatGPT subscription and the cost of a cloud working environment need to be considered separately.
Why the working environment matters more
A chat can provide instructions. An agent with tools can also read files, execute commands, and modify the result. It needs somewhere to bring programs, data, and intermediate results together. Amp calls its remote working machines orbs. Each corresponding conversation gets its own environment with project code and tools. Work continues there when your personal computer is closed.
That changes the practical question. Instead of simply requesting text, users can ask for a verifiable deliverable: a documentation page with working examples, a visualization of existing project data, or a draft with a preview. Success depends on more than the language model. Files, installed programs, and a clearly defined assignment also determine what an agent can actually accomplish.
The development resembles the agent approach in Higgsfield’s video production: several work steps combine into a larger product. With Amp, that product remains closely connected to software projects. This does not establish that arbitrary office work can already be completed independently and reliably.
Puck coordinates while other agents do the work
Puck is the personal point of contact inside Amp. According to the documentation, the assistant can start new agents in projects, search previous conversations, and send further instructions to active tasks. It can also distribute an assignment among several agents and gather their reports. For users, tracking the work matters alongside the answer: which assignment is running where, and which result belongs to it?
There is a technical limit to this separation. Puck has no orb of its own. It can read instructions from skills, which are reusable descriptions of how to perform work, but cannot execute their scripts itself. That requires starting an agent in a suitable environment. Someone asking Puck for a report should therefore distinguish between summarizing existing information and actually carrying out new steps.
Different models also play different parts. The current model overview lists GPT-6 Astra for the main agent in High mode. It assigns GPT-5.6 Sol to Puck and GPT Live 1 to real-time conversations. These names are not interchangeable labels: a coordinator, a voice conversation, and an agent executing work serve different roles. The provider can change those assignments; for a specific task, the current overview is more useful than general model marketing.
How to give a first assignment useful boundaries
According to Amp’s documentation, getting started begins with an account and a project. Users can add a Git repository, a version-controlled directory of project files, or start without an existing repository. The web interface needs no installation; command-line use is available separately. This choice concerns where the work happens: running a local program does not necessarily mean the AI computation is local too.
A sensible first attempt might be to turn existing, nonconfidential project documents into a short introduction page, flag unknown information, and show a preview. This is an editorial example, not a test we performed. The assignment should specify the audience, input files, and desired output format. If the initial goal is only a plan, Amp explicitly recommends telling the agent not to edit files.
A visible criterion helps evaluate the result: do the claims match the inputs, do links work, and can the preview be opened? Amp recommends pointing the agent to specific verification commands or pages to inspect. That turns a plausible success message into an assignment with an inspectable result. Whether another team of agents helps remains a tradeoff; more agents can mean additional coordination and costs.
What to clarify before making it a regular tool
The documentation offers the optional connection of a ChatGPT Plus or Pro subscription. That should not be read as a promise that the entire workflow is free. Amp distinguishes model usage from orbs; its pricing page lists usage-based cloud machines for the free Hobby tier, with users’ own runners as an alternative. An existing subscription therefore does not settle what additional machine costs a chosen workflow might incur.
The data path matters just as much. The security documentation describes a cloud service and external model providers. Conversation data can include messages, tool results, attachments, and code snippets used as context. With orbs, a copy of the project also resides on the remote machine. Starting an Amp client locally therefore does not establish offline operation or exclusively local data processing.
The useful next step is a small, repeatable assignment whose result is easy to evaluate. If Amp reliably brings the inputs, work, and preview together, the main saving is in handoffs between tools. That is the interesting prospect: managing fewer individual chat answers and receiving a finished, inspectable piece of work more often.