# Guide: Human-in-the-loop approval with Vercel AI SDK

This guide shows you how to implement a human-in-the-loop (HITL) approval workflow for AI agent tool calls using the Vercel AI SDK and Ably. The agent requests human approval before executing sensitive operations, with role-based access control to verify approvers have sufficient permissions.

When the model calls a tool that requires human approval, the agent intercepts the tool call, publishes an `approval-request` message to an Ably channel, waits for an `approval-response` from a human approver, verifies the approver has the required role using [claims embedded in their JWT token](https://ably.com/docs/ai-transport/sessions-identity/identifying-users-and-agents.md#user-claims), and only then executes the action.

<Aside data-type="further-reading">
To learn more about human-in-the-loop patterns and verification strategies, see the [human-in-the-loop](https://ably.com/docs/ai-transport/messaging/human-in-the-loop.md) documentation.
</Aside>

## Prerequisites 

To follow this guide, you need:
- Node.js 20 or higher
- An API key for your preferred model provider (such as OpenAI or Anthropic)
- An Ably API key

Useful links:
- [Vercel AI SDK documentation](https://ai-sdk.dev/docs)
- [Vercel AI SDK tool calling](https://ai-sdk.dev/docs/ai-sdk-core/tools-and-tool-calling)
- [Ably JavaScript SDK getting started](https://ably.com/docs/getting-started/javascript.md)

Create a new Node project, which will contain the agent, client, and server code:

<Code>

### Shell

```
mkdir ably-vercel-hitl-example && cd ably-vercel-hitl-example
npm init -y
```
</Code>

Install the required packages using NPM:

<Code>

### Shell

```
npm install ai@^6 @ai-sdk/openai ably@^2 express jsonwebtoken zod@^4
```
</Code>

<Aside data-type="note">
This guide uses version 6.x of the AI SDK with Zod 4 and the OpenAI provider with the GPT-4o model. You can use any model from any [supported provider](https://ai-sdk.dev/providers/ai-sdk-providers) by installing the relevant provider package and updating the model initialization.
</Aside>

Export your API keys to the environment:

<Code>

### Shell

```
export OPENAI_API_KEY="your_openai_api_key_here"
export ABLY_API_KEY="your_ably_api_key_here"
```
</Code>

## Step 1: Initialize the agent 

Set up the agent that will use the Vercel AI SDK and request human approval for sensitive operations. This example uses a `publishBlogPost` tool that requires authorization before execution.

Initialize the Ably client and create a channel for communication between the agent and human approvers.

Add the following to a new file called `agent.mjs`:

<Code>

### Javascript

```
import { generateText, tool } from "ai";
import { openai } from "@ai-sdk/openai";
import { z } from "zod";
import Ably from "ably";

// Initialize Ably Realtime client
const realtime = new Ably.Realtime({
  key: process.env.ABLY_API_KEY,
  echoMessages: false,
});

// Wait for connection to be established
await realtime.connection.once("connected");

// Create a channel for HITL communication
const channel = realtime.channels.get("ai:your-channel-name");

// Track pending approval requests
const pendingApprovals = new Map();

// Function that executes the approved action
async function publishBlogPost(args) {
  const { title } = args;
  console.log(`Publishing blog post: ${title}`);
  // In production, this would call your CMS API
  return { published: true, title };
}
```
</Code>

<Aside data-type="note">
Set [`echoMessages`](https://ably.com/docs/api/realtime-sdk/types.md#client-options) to `false` on the agent's Ably client to prevent the agent from receiving its own messages, avoiding billing for [echoed messages](https://ably.com/docs/pub-sub/advanced.md#echo).
</Aside>

Tools that modify data, access sensitive resources, or perform actions with business impact are good candidates for HITL approval workflows.

## Step 2: Request human approval 

When the model returns a tool call, publish an approval request to the channel and wait for a human decision. The tool call ID is passed in the message headers to correlate requests with responses.

Add the approval request function to `agent.mjs`:

<Code>

### Javascript

```
async function requestHumanApproval(toolCall) {
  const approvalPromise = new Promise((resolve, reject) => {
    pendingApprovals.set(toolCall.toolCallId, { toolCall, resolve, reject });
  });

  await channel.publish({
    name: "approval-request",
    data: {
      tool: toolCall.toolName,
      arguments: toolCall.input,
    },
    extras: {
      headers: {
        toolCallId: toolCall.toolCallId,
      },
    },
  });

  console.log(`Approval request sent for: ${toolCall.toolName}`);
  return approvalPromise;
}
```
</Code>

The `toolCall.toolCallId` provided by the Vercel AI SDK correlates the approval request with the response, enabling the agent to handle multiple concurrent approval flows.

## Step 3: Subscribe to approval responses 

Set up a subscription to receive approval decisions from human users. When a response arrives, verify the approver has sufficient permissions using role-based access control before resolving the pending promise.

Add the subscription handler to `agent.mjs`:

<Code>

### Javascript

```
async function subscribeApprovalResponses() {
  // Define role hierarchy from lowest to highest privilege
  const roleHierarchy = ["editor", "publisher", "admin"];

  // Define minimum role required for each tool
  const approvalPolicies = {
    publishBlogPost: { minRole: "publisher" },
  };

  function canApprove(approverRole, requiredRole) {
    const approverLevel = roleHierarchy.indexOf(approverRole);
    const requiredLevel = roleHierarchy.indexOf(requiredRole);
    return approverLevel >= requiredLevel;
  }

  await channel.subscribe("approval-response", async (message) => {
    const { decision } = message.data;
    const toolCallId = message.extras?.headers?.toolCallId;
    const pending = pendingApprovals.get(toolCallId);

    if (!pending) {
      console.log(`No pending approval for tool call: ${toolCallId}`);
      return;
    }

    const policy = approvalPolicies[pending.toolCall.toolName];
    // Get the trusted role from the JWT user claim
    const approverRole = message.extras?.userClaim;

    // Verify the approver's role meets the minimum required
    if (!canApprove(approverRole, policy.minRole)) {
      console.log(`Insufficient role: ${approverRole} < ${policy.minRole}`);
      pending.reject(
        new Error(
          `Approver role '${approverRole}' insufficient for required '${policy.minRole}'`
        )
      );
      pendingApprovals.delete(toolCallId);
      return;
    }

    // Process the decision
    if (decision === "approved") {
      console.log(`Approved by ${approverRole}`);
      pending.resolve({ approved: true, approverRole });
    } else {
      console.log(`Rejected by ${approverRole}`);
      pending.reject(new Error(`Action rejected by ${approverRole}`));
    }
    pendingApprovals.delete(toolCallId);
  });
}
```
</Code>

The `message.extras.userClaim` contains the role embedded in the approver's JWT token, providing a trusted source for authorization decisions. See [user claims](https://ably.com/docs/ai-transport/sessions-identity/identifying-users-and-agents.md#user-claims) for details on embedding claims in tokens. This ensures only users with sufficient privileges can approve sensitive operations.

## Step 4: Process tool calls 

Create a function to process tool calls by requesting approval and executing the action if approved.

Add the tool processing function to `agent.mjs`:

<Code>

### Javascript

```
async function processToolCall(toolCall) {
  if (toolCall.toolName === "publishBlogPost") {
    // requestHumanApproval returns a promise that resolves when the human
    // approves the tool call, or rejects if the human explicitly rejects
    // the tool call or the approver's role is insufficient.
    await requestHumanApproval(toolCall);
    return await publishBlogPost(toolCall.input);
  }
  throw new Error(`Unknown tool: ${toolCall.toolName}`);
}
```
</Code>

The function awaits approval before executing. If the approver rejects or has insufficient permissions, the promise rejects and the tool is not executed.

## Step 5: Run the agent 

Create the main agent function that sends a prompt to the model and processes any tool calls that require approval. The `publishBlogPost` tool includes a passthrough `execute` function since execution is handled manually via `processToolCall` after human approval.

Add the agent runner to `agent.mjs`:

<Code>

### Javascript

```
async function runAgent(prompt) {
  await subscribeApprovalResponses();

  console.log(`User: ${prompt}`);

  const response = await generateText({
    model: openai.chat("gpt-4o"),
    messages: [{ role: "user", content: prompt }],
    tools: {
      publishBlogPost: tool({
        description: "Publish a blog post to the website. Requires human approval.",
        inputSchema: z.object({
          title: z.string().describe("Title of the blog post to publish"),
        }),
        execute: async (args) => {
          return args;
        },
      }),
    },
  });

  const toolCalls = response.toolCalls;

  for (const toolCall of toolCalls) {
    console.log(`Tool call: ${toolCall.toolName}`);
    try {
      const result = await processToolCall(toolCall);
      console.log("Result:", result);
    } catch (err) {
      console.error("Tool call failed:", err.message);
    }
  }

  realtime.close();
}

runAgent("Publish the blog post called 'Introducing our new API'");
```
</Code>

## Step 6: Create the authentication server 

The authentication server issues JWT tokens with embedded role claims. The role claim is trusted by Ably and included in messages, enabling secure role-based authorization.

Add the following to a new file called `server.mjs`:

<Code>

### Javascript

```
import express from "express";
import jwt from "jsonwebtoken";

const app = express();

// Mock authentication - replace with your actual auth logic
function authenticateUser(req, res, next) {
  // In production, verify the user's session/credentials
  req.user = { id: "user123", role: "publisher" };
  next();
}

// Return claims to embed in the JWT
function getJWTClaims(user) {
  return {
    "ably.channel.*": user.role,
  };
}

app.get("/api/auth/token", authenticateUser, (req, res) => {
  const [keyName, keySecret] = process.env.ABLY_API_KEY.split(":");

  const token = jwt.sign(getJWTClaims(req.user), keySecret, {
    algorithm: "HS256",
    keyid: keyName,
    expiresIn: "1h",
  });

  res.type("application/jwt").send(token);
});

app.listen(3001, () => {
  console.log("Auth server running on http://localhost:3001");
});
```
</Code>

The `ably.channel.*` claim embeds the user's role in the JWT. When the user publishes messages, this claim is available as `message.extras.userClaim`, providing a trusted source for authorization.

Run the server:

<Code>

### Shell

```
node server.mjs
```
</Code>

## Step 7: Create the approval client 

The approval client receives approval requests and allows humans to approve or reject them. It authenticates via the server to obtain a JWT with the user's role.

Add the following to a new file called `client.mjs`:

<Code>

### Javascript

```
import Ably from "ably";
import readline from "readline";

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
});

const realtime = new Ably.Realtime({
  authCallback: async (tokenParams, callback) => {
    try {
      const response = await fetch("http://localhost:3001/api/auth/token");
      const token = await response.text();
      callback(null, token);
    } catch (error) {
      callback(error, null);
    }
  },
});

realtime.connection.on("connected", () => {
  console.log("Connected to Ably");
  console.log("Waiting for approval requests...\n");
});

const channel = realtime.channels.get("ai:your-channel-name");

await channel.subscribe("approval-request", (message) => {
  const request = message.data;

  console.log("\n========================================");
  console.log("APPROVAL REQUEST");
  console.log("========================================");
  console.log(`Tool: ${request.tool}`);
  console.log(`Arguments: ${JSON.stringify(request.arguments, null, 2)}`);
  console.log("========================================");

  rl.question("Approve this action? (y/n): ", async (answer) => {
    const decision = answer.toLowerCase() === "y" ? "approved" : "rejected";

    await channel.publish({
      name: "approval-response",
      data: { decision },
      extras: {
        headers: {
          toolCallId: message.extras?.headers?.toolCallId,
        },
      },
    });

    console.log(`Decision sent: ${decision}\n`);
  });
});
```
</Code>

Run the client in a separate terminal:

<Code>

### Shell

```
node client.mjs
```
</Code>

With the server, client, and agent running, the workflow proceeds as follows:

1. The agent sends a prompt to the model that triggers a tool call
2. The agent publishes an approval request to the channel
3. The client displays the request and prompts the user
4. The user approves or rejects the request
5. The agent verifies the approver's role meets the minimum requirement
6. If approved and authorized, the agent executes the tool

## Next steps

- Learn more about [human-in-the-loop](https://ably.com/docs/ai-transport/messaging/human-in-the-loop.md) patterns and verification strategies
- Explore [identifying users and agents](https://ably.com/docs/ai-transport/sessions-identity/identifying-users-and-agents.md) for secure identity verification
- Understand [sessions and identity](https://ably.com/docs/ai-transport/sessions-identity.md) in AI-enabled applications
- Learn about [tool calls](https://ably.com/docs/ai-transport/messaging/tool-calls.md) for agent-to-agent communication

## Related Topics

- [Message per response](https://ably.com/docs/ai-transport/guides/vercel-ai-sdk/vercel-message-per-response.md): Stream tokens from the Vercel AI SDK over Ably in realtime using message appends.
- [Message per token](https://ably.com/docs/ai-transport/guides/vercel-ai-sdk/vercel-message-per-token.md): Stream tokens from the Vercel AI SDK over Ably in realtime.

## Documentation Index

To discover additional Ably documentation:

1. Fetch [llms.txt](https://ably.com/llms.txt) for the canonical list of available pages.
2. Identify relevant URLs from that index.
3. Fetch target pages as needed.

Avoid using assumed or outdated documentation paths.