AI Agent Cloud: Hosting & Coding Tasks

What is a cloud AI agent?

A cloud AI agent runs a task through a model, tools and an execution environment hosted in the cloud. An AI agent cloud provides the workspace for configuring and following that execution. Token4AI Cloud brings agent tasks and model API access together, while the selected harness determines which tools and workflows are available.

How much does a cloud AI agent cost?

Model requests consume tokens and account credit under the current model and billing-group prices. A repeating task can make multiple requests, so check the console pricing and balance before enabling a loop. Review request history for measured token usage and charges; a task count alone does not determine its cost.

Cloud hosting or a local coding agent?

A local coding agent works in the environment you provide on your computer. A cloud coding agent executes in a hosted environment, so first confirm that its harness can access the repository, files and tools your task needs. Cloud hosting does not automatically give an agent access to files on your laptop.

Choose a small task with a reviewable result, such as explaining a module or proposing a test change. Inspect the output and execution log before expanding its scope or scheduling it repeatedly. If you only need model responses in an existing application, start with API access; a hosted agent adds task execution and tool coordination.

Estimate a repeating task budget

A task scheduled every five minutes has 12 scheduled runs per hour, or 288 per day if it stays enabled for 24 hours. This is a schedule estimate, not a count of completed tasks or model requests: a run may fail or issue several requests.

Measure a representative run in request history, then estimate daily model spend as scheduled runs multiplied by average model cost per run. Recheck the estimate when changing the model, prompt length or tools. Check any separate service charges in the console; missing usage is not evidence of a free request.

What is an agent harness?

A model generates responses; an agent harness coordinates a task around the model, its tools and its execution context. The choice of harness affects how the agent works with tools and manages execution.

The agent creation flow includes Pi Agent, OpenClaw and Hermes harness options. Choose from the options currently enabled in your console and configure the model access required by that harness. Different harnesses and models can have different capabilities.

Start a task from a project Room

1. In Team Agents → Projects, create or select your private project.
2. Create an agent in that project and configure its permitted API key and model.
3. Open Agent Chat, create a session and mention the agent with @ followed by the task.
4. Track the project issue board through open, in progress, in review and closed, then inspect task events and results.

How do repeating tasks work?

In a Room, use @agent /loop 5m prompt to submit a repeating task. Replace the agent name, interval and prompt with the intended values; supported minute intervals include 1m, 5m and 30m.

Use a prompt with a clear scope and an expected result. Review the task configuration and stop a loop when it is no longer needed. Repeated model requests can consume additional tokens and account credit.

@agent /loop 5m Check the project status and summarize changes.

How can I understand a failed task?

Open the task log from its details or the latest task reply in the Room. The event timeline uses segment length to represent event duration, helping you locate slow parts of execution. Event cards provide context alongside the task outcome.

A failed task may involve credentials, model access, request limits or a tool failure. Read the reported event and retain the request ID when contacting support. A model response alone is not proof that a tool action completed successfully.