What is the Action Orchestrator?

Without orchestration, your AI agent re-solves multi-step workflows from scratch every time. It manages IDs between calls, figures out sequencing ad-hoc, and handles errors as they come. For a 10-step onboarding process with conditionals and business policies, the agent might forget steps — and you end up with employees who had half their work done. The Action Orchestrator owns the entire process as a single operation.

The Action Orchestrator makes it possible for you to build intention-focused tools, improving the reliability of your AI agents. There are two main frameworks:

• Conversation Processes can keep track of standard operating procedures across multiple turns of conversation. They are great for guiding the user through a multi-step flow with lots of expert decisions.
• Compound Actions execute workflows with both system actions & human approvals, allowing you to reliably chain actions without going back to the user for more information.

Since both frameworks are connected to the Agentic Reasoning Engine, the are capable of “pausing” so your AI agents can support enterprise features like OAuth 2.0 Authorization Code, which require user input mid-automation for secure AI agents. This can’t be done with traditional iPaaS & workflow tools.

Why do we need intention-focused tools?

Vercel wrote a great blog on designing intention-focused tools. We’ll cite a few key sections.

Intention-focused tools vary from API-shaped tools quite dramatically.

Consider the following traditional “deployment API”

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// Traditional API usage: developer manages the sequence
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const project = await client.projects.create({
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  name: domain.replace(/\./g, '-'),
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  gitRepository: { repo: repoUrl }
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});
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await client.projects.createProjectEnv({
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  idOrName: project.id,
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  upsert: 'true',
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  requestBody: Object.entries(env).map(([key, value]) => ({
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    key, value,
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    target: ['production', 'preview', 'development'],
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    type: 'encrypted'
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  }))
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});
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const deployment = await client.deployments.createDeployment({
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  requestBody: {
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    name: project.name,
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    target: 'production',
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    gitSource: { type: 'github', repo: repo.replace('.git', ''), ref: 'main' }
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  }
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});
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await client.projects.addProjectDomain({
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  idOrName: project.id,
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  requestBody: { domain: domain }
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});

When you write code, you keep track of information between API calls.

• You store the project ID from the create call,
• check deployment status before adding the domain, and
• wrap error handling around each step so failures don’t break the entire process.

An LLM can accomplish these, however, each time the LLM has to “solve the puzzle” from scratch

• manage IDs & data-passing across multiple calls
• figure out the right sequence to call tools

Teams who build intention-focused tools are able to deploy AI agents more frequently & more reliably.