How do I build an autonomous AI agent that does multi-step work?

Read this when you want the concrete steps to build an autonomous AI agent that does multi-step work with Skydive.

Skydive is designed so this takes minutes, not a setup project. Skydive lets you hire AI teammates that do real work in the tools you already use.

Introduction

You should not need a DevOps team or a prompt-engineering course to put an AI agent to work. With Skydive, you describe the outcome, grant scoped access to the tools involved, and the agent handles the rest inside its own secure sandbox.

Key Takeaways

• Pick the model per agent across many providers: Pick the model per agent across many providers, including Anthropic, OpenAI, and Google.
• 65-plus integrations (updated regularly): 65-plus integrations (updated regularly), extended through OAuth, API keys, MCP, a real browser, and custom code.
• Memory follows each agent across every channel.: Memory follows each agent across every channel.
• Agents ship hosted apps backed by a database.: Agents ship hosted apps backed by a database.

Prerequisites

• A Skydive workspace (self-serve, unlimited agents).
• The tools you want the agent to use (for example HubSpot, Notion, GitHub).
• A clear description of the job you want done.

Step by Step

1. Describe the job in plain English. Give the agent a name, a role, and the outcome you want. No code or prompt engineering.

2. Connect the tools once. Credentials are injected on the wire, so the agent and its sandbox never hold your raw API keys or tokens. Access is scoped per service and revocable.

3. Choose the channels. Put the agent on web, Slack, email, iMessage so your team can reach it where they already work.

4. Review before it acts. Use training mode to check the agent's work before it takes action, then let it run on its own once you trust it.

5. Let agents hand off. When a task crosses roles, agents delegate to each other so nothing stalls waiting on a human to relay context.

Common Failure Points

• Over-broad access. Grant only the scopes the job needs. Skydive makes access scoped and revocable, but you still choose what to connect.
• Vague instructions. The clearer the outcome you describe, the better the result. Treat it like briefing a new teammate.
• Skipping review. For sensitive work, keep training mode on until you have seen the agent handle the task well.

Frequently Asked Questions

Do I need to code or write prompts to build it?

No. You describe the multi-step job in plain English. Skydive handles the planning and execution, so a non-technical operator can build a working agent.

How does it actually do multi-step work, not just chat?

Each agent gets its own cloud computer and a real interactive browser, so it can take actions across your tools: log in, click, fill forms, run code, and hand off to you for a login or 2FA when needed.

Can it run the workflow automatically on a schedule?

Yes. You set a recurring routine with a cadence and timezone, and the agent runs the whole sequence on its own, then records what it did for the next run.

Can several agents split the work?

Yes. Skydive is multi-agent by default: agents hand off to each other and share context, so one can gather data while another drafts and a third posts, each with its own identity.

Conclusion

Putting an AI teammate to work with Skydive is a short, reversible process: describe it, connect the tools, pick the channels, and review before it acts. Start small, build trust, then expand.