Guide
How to build a portable AI agent
Agentlas treats an agent as an asset you can inspect, keep, restore, and run again. Build and execute it in Agentlas Desktop, borrow specialists from the public Hub, and use private Agent Cloud when you want the same package on another supported computer.
What is an AI agent?
An AI agent combines instructions, tools, permissions, memory rules, and an execution entry point to do a repeatable job. In Agentlas, that definition lives in a portable package with an owner and content identity, not only inside one vendor's chat or one computer's settings.
Build an AI agent in 5 steps
- 1
Install the signed Agentlas Desktop release
Desktop is the visual build and run surface. Sign in with the same Agentlas account you use for Hub bookmarks and private Agent Cloud assets, then connect a supported model or BYOK provider locally.
- 2
Describe the job and choose an architecture
Explain the outcome in plain language, then choose a single agent or a coordinated team. Desktop turns that intent into an editable package instead of hiding it inside one chat session.
- 3
Review tools, permissions, memory, and handoffs
Inspect the package before running it. Keep credential values in your local keychain or approved vault; only value-free requirements belong in the portable package.
- 4
Run it on the host you control
Use Desktop's local runtime and inspect the real result, cancellation state, and run receipt. Agent Cloud stores packages; it does not execute a hosted model or remote VM for you.
- 5
Keep it private or publish deliberately
Save the full package to owner-private Agent Cloud so another supported computer can restore it, or publish a separate clean copy to the public Hub. Saving, publishing, bookmarking, and installing remain different states.
Choose one agent or a team
Use one agent for a focused job. Use a team when the work needs explicit specialist roles and handoffs. Desktop keeps that architecture editable and makes the runtime outcome visible; it does not claim that a generated diagram alone proves the team works.
Frequently asked questions
Can you build an AI agent without coding?
Yes. Agentlas Desktop provides a visual path for describing, reviewing, and running an agent without writing framework code. Advanced users can still inspect the files or use the existing Agentlas Terminal.
Does the Agentlas website build the agent?
No. Agentlas Web is the control plane for Hub discovery, private Agent Cloud, bookmarks, account state, and Desktop handoff. New agent creation and execution happen in Agentlas Desktop or the existing Agentlas Terminal.
How do I build a multi-agent system without writing Python?
In Desktop, choose a team architecture, describe the outcome, and review the coordinator, specialists, handoffs, tools, and memory boundaries before the first run. The result is a portable Agentlas package rather than a server-only Web draft.
How long does it take to build an AI agent?
A simple first package can be quick, but a production agent is ready only after its tools, permissions, representative task, failure path, and output have been tested on the target host.
Is it free to build an AI agent with Agentlas?
Plan limits and Hub call prices can change, so use the live pricing page and the quote shown before a call. Model execution uses the supported provider or model account you connect to the local runtime.
Where does the agent run after you build it?
On a supported host you choose, such as Agentlas Desktop, Agentlas Terminal, or a compatible LLM host. Agent Cloud is private package storage and restore, not a hosted execution service.
Build and run in Agentlas Desktop
Use Web to discover and manage assets; use the signed Desktop release for creation and execution.
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