Vercel AI SDK Agents: Complete 2026 Implementation Guide

The AI Agent Revolution with Vercel AI SDK

Artificial intelligence agents are transforming how users interact with applications, moving beyond simple chatbots to sophisticated systems that can query databases, execute complex workflows, and interact with your application's core functionality. Vercel AI SDK has emerged as the dominant TypeScript toolkit for building these intelligent systems, with over 20 million monthly downloads and adoption by teams ranging from startups to Fortune 500 companies.

Real impact: "Thomson Reuters used the AI SDK to build CoCounsel, their AI assistant for attorneys, accountants, and audit teams, with just 3 developers in 2 months. Now serving 1,300 accounting firms, they're migrating their entire codebase to the AI SDK, deprecating thousands of lines of code across 10 providers and consolidating into one composable, scalable system."

This comprehensive guide covers everything you need to build production-ready AI agents with Vercel AI SDK 6, from basic tool calling to sophisticated database integration that lets agents query your job board, e-commerce platform, or any other data-driven application.

Understanding Vercel AI SDK and AI Agents

What is Vercel AI SDK?

Vercel AI SDK is a free, open-source TypeScript toolkit designed specifically for building AI-powered applications and agents. Unlike provider-specific SDKs, it provides a unified API that works across 25+ AI providers including OpenAI, Anthropic, Google, AWS Bedrock, Azure OpenAI, xAI Grok, and Mistral.

Core SDK Components:

AI SDK Core:

• Unified API for generating text and structured outputs

• Built-in tool calling and agent orchestration

• Multi-step workflow automation

• Provider-agnostic model switching

AI SDK UI:

• Framework-agnostic hooks for React, Vue, Svelte, and Angular

• Streaming response handling with minimal code

• Type-safe UI components that auto-sync with agent logic

• Generative UI support for dynamic component rendering

AI SDK Agents (SDK 6):

• Reusable agent abstractions across application contexts

• Production-ready ToolLoopAgent implementation

• Human-in-the-loop approval workflows

• End-to-end TypeScript type safety

What are AI Agents?

AI agents are autonomous systems that observe their environment, make decisions based on large language models (LLMs), and take actions to achieve specific goals. Unlike simple chatbots that respond to predefined inputs, agents can:

Autonomous Decision Making:

• Analyze user requests to determine required actions

• Choose appropriate tools from available options

• Execute multi-step workflows without human intervention

• Adapt behavior based on tool execution results

Tool Integration and Execution:

• Query databases to retrieve relevant information

• Call external APIs for real-time data

• Execute business logic and workflows

• Interact with file systems and cloud services

Context Management:

• Maintain conversation history across interactions

• Track tool call results for informed decision-making

• Build understanding through iterative tool usage

• Generate final responses synthesizing all collected information

Industry insight: Clay used Vercel AI SDK to build Claygent, their AI web research agent that scrapes public data, connects to first-party sources via MCP servers, and helps sales teams find accounts with custom insights at massive scale.

Vercel AI SDK 6: Revolutionary Agent Features

Agent Abstraction Layer

AI SDK 6 introduces a fundamental shift in how developers build agents by providing a reusable agent abstraction that works everywhere—chat UIs, background jobs, API endpoints, or standalone scripts.

Before AI SDK 6 (Repetitive Configuration):

javascript

// Chat route
const chatResult = await streamText({
  model: openai('gpt-4'),
  system: 'You are a helpful assistant',
  tools: { weather, database, email },
  messages,
});

// Background job route
const jobResult = await generateText({
  model: openai('gpt-4'),
  system: 'You are a helpful assistant',
  tools: { weather, database, email },
  prompt: jobPrompt,
});

// API endpoint route
const apiResult = await generateText({
  model: openai('gpt-4'),
  system: 'You are a helpful assistant',
  tools: { weather, database, email },
  prompt: apiRequest,
});

After AI SDK 6 (Define Once, Use Everywhere):

javascript

// Define agent once in agents/assistant.ts
import { ToolLoopAgent } from 'ai';
import { openai } from '@ai-sdk/openai';
import { weatherTool, databaseTool, emailTool } from './tools';

export const assistant = new ToolLoopAgent({
  model: openai('gpt-4'),
  system: 'You are a helpful assistant',
  tools: {
    weather: weatherTool,
    database: databaseTool,
    email: emailTool,
  },
});

// Use in chat route
const chatResult = await assistant.stream({ messages });

// Use in background job
const jobResult = await assistant.generate({ prompt: jobPrompt });

// Use in API endpoint
const apiResult = await assistant.generate({ prompt: apiRequest });

ToolLoopAgent: Production-Ready Implementation

The ToolLoopAgent class provides an out-of-the-box implementation that handles the complete agent execution cycle:

Automatic Tool Loop Orchestration:

javascript

const agent = new ToolLoopAgent({
  model: anthropic('claude-sonnet-4'),
  system: 'You are a data analyst assistant',
  tools: {
    queryDatabase: databaseQueryTool,
    generateChart: chartGenerationTool,
    sendReport: emailReportTool,
  },
  stopWhen: stepCountIs(20), // Maximum 20 steps
});

// Agent automatically:
// 1. Receives user request
// 2. Decides which tool to call
// 3. Executes tool and receives result
// 4. Adds result to conversation history
// 5. Generates new request based on updated context
// 6. Repeats until completion or max steps reached
const result = await agent.generate({
  prompt: 'Analyze last month sales and email report to leadership'
});

Tool Execution Approval System

AI SDK 6 introduces human-in-the-loop approval for sensitive operations, ensuring agents never execute critical actions without human oversight:

Implementing Tool Approval:

javascript

import { tool } from 'ai';
import { z } from 'zod';

const deleteRecordTool = tool({
  description: 'Delete a database record permanently',
  parameters: z.object({
    table: z.string().describe('Database table name'),
    id: z.string().describe('Record ID to delete'),
  }),
  needsApproval: true, // Requires human approval
  execute: async ({ table, id }) => {
    // Only executes after approval
    await database.deleteRecord(table, id);
    return { success: true, deleted: id };
  },
});

// Agent pauses for approval before deletion
const result = await agent.generate({
  prompt: 'Delete customer record ID 12345',
});

// User approves via UI, then agent continues

Conditional Approval Based on Parameters:

javascript

const transferFundsTool = tool({
  description: 'Transfer funds between accounts',
  parameters: z.object({
    from: z.string(),
    to: z.string(),
    amount: z.number(),
  }),
  needsApproval: async ({ amount }) => {
    // Only large transfers require approval
    return amount > 1000;
  },
  execute: async ({ from, to, amount }) => {
    await bankingService.transfer(from, to, amount);
    return { success: true, amount };
  },
});

Building Database-Integrated Agents: Job Board Example

Real-World Use Case: Job Search Agent

Let's build exactly what you described—an AI agent for a job board that can query your database and respond to natural language requests like "list me 100 Java jobs posted recently."

Complete Job Board Agent Implementation:

Step 1: Define Database Query Tool

javascript

// lib/tools/database.js
import { tool } from 'ai';
import { z } from 'zod';
import { PrismaClient } from '@prisma/client';

const prisma = new PrismaClient();

export const jobSearchTool = tool({
  description: `Search for job listings in the database. 
  Use this when users ask about jobs, positions, or career opportunities.
  You can filter by job title, skills, location, salary, and posting date.`,
  parameters: z.object({
    query: z.string().optional().describe('Job title or keywords to search'),
    skills: z.array(z.string()).optional().describe('Required skills (e.g., "Java", "Python")'),
    location: z.string().optional().describe('Job location'),
    minSalary: z.number().optional().describe('Minimum salary requirement'),
    maxSalary: z.number().optional().describe('Maximum salary requirement'),
    postedAfter: z.string().optional().describe('ISO date string for jobs posted after this date'),
    limit: z.number().default(100).describe('Maximum number of results to return'),
  }),
  execute: async ({ query, skills, location, minSalary, maxSalary, postedAfter, limit }) => {
    try {
      // Build dynamic query based on parameters
      const whereClause = {};
      
      if (query) {
        whereClause.OR = [
          { title: { contains: query, mode: 'insensitive' } },
          { description: { contains: query, mode: 'insensitive' } },
        ];
      }
      
      if (skills && skills.length > 0) {
        whereClause.skills = {
          hasSome: skills,
        };
      }
      
      if (location) {
        whereClause.location = {
          contains: location,
          mode: 'insensitive',
        };
      }
      
      if (minSalary || maxSalary) {
        whereClause.salary = {};
        if (minSalary) whereClause.salary.gte = minSalary;
        if (maxSalary) whereClause.salary.lte = maxSalary;
      }
      
      if (postedAfter) {
        whereClause.postedAt = {
          gte: new Date(postedAfter),
        };
      }
      
      // Execute database query
      const jobs = await prisma.job.findMany({
        where: whereClause,
        take: limit,
        orderBy: {
          postedAt: 'desc',
        },
        select: {
          id: true,
          title: true,
          company: true,
          location: true,
          salary: true,
          skills: true,
          description: true,
          postedAt: true,
          applicants: true,
        },
      });
      
      // Return formatted results
      return {
        count: jobs.length,
        jobs: jobs.map(job => ({
          id: job.id,
          title: job.title,
          company: job.company,
          location: job.location,
          salary: `$${job.salary.toLocaleString()}/year`,
          skills: job.skills.join(', '),
          posted: new Date(job.postedAt).toLocaleDateString(),
          applicants: job.applicants,
          description: job.description.substring(0, 200) + '...',
        })),
      };
    } catch (error) {
      console.error('Database query error:', error);
      return {
        error: 'Failed to search jobs. Please try again.',
      };
    }
  },
});

Step 2: Create Additional Job-Related Tools

javascript

// lib/tools/job-details.js
export const getJobDetailsTool = tool({
  description: 'Get complete details for a specific job listing by ID',
  parameters: z.object({
    jobId: z.string().describe('The unique job ID'),
  }),
  execute: async ({ jobId }) => {
    const job = await prisma.job.findUnique({
      where: { id: jobId },
      include: {
        company: {
          select: {
            name: true,
            description: true,
            website: true,
            size: true,
          },
        },
      },
    });
    
    if (!job) {
      return { error: 'Job not found' };
    }
    
    return {
      job: {
        id: job.id,
        title: job.title,
        description: job.description,
        requirements: job.requirements,
        salary: job.salary,
        location: job.location,
        type: job.type, // Full-time, part-time, contract
        remote: job.remote,
        skills: job.skills,
        postedAt: job.postedAt,
        company: job.company,
      },
    };
  },
});

export const applyToJobTool = tool({
  description: 'Submit job application for the current user',
  parameters: z.object({
    jobId: z.string().describe('The job ID to apply to'),
    coverLetter: z.string().optional().describe('Optional cover letter'),
  }),
  needsApproval: true, // User must approve application submission
  execute: async ({ jobId, coverLetter }, context) => {
    // Get user from context (passed via call options)
    const userId = context.userId;
    
    const application = await prisma.jobApplication.create({
      data: {
        jobId,
        userId,
        coverLetter,
        status: 'submitted',
        submittedAt: new Date(),
      },
    });
    
    return {
      success: true,
      applicationId: application.id,
      message: 'Application submitted successfully',
    };
  },
});

export const getApplicationStatusTool = tool({
  description: 'Check status of user job applications',
  parameters: z.object({
    applicationId: z.string().optional().describe('Specific application ID to check'),
  }),
  execute: async ({ applicationId }, context) => {
    const userId = context.userId;
    
    const whereClause = { userId };
    if (applicationId) {
      whereClause.id = applicationId;
    }
    
    const applications = await prisma.jobApplication.findMany({
      where: whereClause,
      include: {
        job: {
          select: {
            title: true,
            company: true,
          },
        },
      },
      orderBy: {
        submittedAt: 'desc',
      },
    });
    
    return {
      applications: applications.map(app => ({
        id: app.id,
        job: app.job.title,
        company: app.job.company,
        status: app.status,
        submittedAt: app.submittedAt,
        lastUpdated: app.updatedAt,
      })),
    };
  },
});

Step 3: Create Job Board Agent

javascript

// agents/job-board-agent.ts
import { ToolLoopAgent } from 'ai';
import { openai } from '@ai-sdk/openai';
import { 
  jobSearchTool, 
  getJobDetailsTool, 
  applyToJobTool,
  getApplicationStatusTool 
} from '../lib/tools';

export const jobBoardAgent = new ToolLoopAgent({
  model: openai('gpt-4-turbo'),
  system: `You are an intelligent job board assistant that helps users find and apply for jobs.

Your capabilities:
- Search for jobs based on various criteria (title, skills, location, salary, posting date)
- Retrieve detailed information about specific job listings
- Help users apply to jobs (requires approval)
- Check application status

When searching for jobs:
- Extract relevant criteria from user requests
- Use appropriate date filters for "recent" jobs (last 30 days)
- Suggest related searches if results are limited
- Highlight key details like salary, location, and required skills

Be conversational and helpful. Format job listings clearly with:
- Job title and company
- Location and salary
- Key required skills
- How long ago it was posted
- Number of applicants (to gauge competition)

Always ask clarifying questions if the user's request is ambiguous.`,
  tools: {
    searchJobs: jobSearchTool,
    getJobDetails: getJobDetailsTool,
    applyToJob: applyToJobTool,
    checkApplications: getApplicationStatusTool,
  },
  stopWhen: stepCountIs(10),
});

Step 4: Create API Route

javascript

// app/api/chat/route.ts
import { jobBoardAgent } from '@/agents/job-board-agent';
import { NextResponse } from 'next/server';
import { auth } from '@/lib/auth'; // Your authentication system

export const runtime = 'edge';
export const maxDuration = 60;

export async function POST(req: Request) {
  try {
    // Authenticate user
    const session = await auth();
    if (!session?.user) {
      return new NextResponse('Unauthorized', { status: 401 });
    }
    
    const { messages } = await req.json();
    
    // Stream agent response with user context
    const stream = await jobBoardAgent.stream({
      messages,
      // Pass user context to tools via call options
      callOptions: {
        userId: session.user.id,
        userEmail: session.user.email,
      },
    });
    
    return stream.toDataStreamResponse();
  } catch (error) {
    console.error('Agent error:', error);
    return new NextResponse('Internal Server Error', { status: 500 });
  }
}

Step 5: Create Chat UI

jsx

// app/chat/page.tsx
'use client';

import { useChat } from 'ai/react';

export default function JobBoardChat() {
  const { messages, input, handleInputChange, handleSubmit, isLoading } = useChat({
    api: '/api/chat',
    onError: (error) => {
      console.error('Chat error:', error);
    },
  });

  return (
    <div className="flex flex-col h-screen max-w-4xl mx-auto p-4">
      <div className="flex-1 overflow-y-auto space-y-4 mb-4">
        {messages.map((message) => (
          <div
            key={message.id}
            className={`flex ${
              message.role === 'user' ? 'justify-end' : 'justify-start'
            }`}
          >
            <div
              className={`max-w-xl rounded-lg px-4 py-2 ${
                message.role === 'user'
                  ? 'bg-blue-600 text-white'
                  : 'bg-gray-100 text-gray-900'
              }`}
            >
              <p className="whitespace-pre-wrap">{message.content}</p>
              
              {/* Display tool calls */}
              {message.toolInvocations?.map((tool, index) => (
                <div key={index} className="mt-2 p-2 bg-opacity-20 bg-black rounded">
                  <p className="text-xs font-mono">
                    🔧 {tool.toolName}
                  </p>
                  {tool.state === 'result' && (
                    <pre className="text-xs mt-1 overflow-x-auto">
                      {JSON.stringify(tool.result, null, 2)}
                    </pre>
                  )}
                </div>
              ))}
            </div>
          </div>
        ))}
        
        {isLoading && (
          <div className="flex justify-start">
            <div className="bg-gray-100 rounded-lg px-4 py-2">
              <div className="flex space-x-2">
                <div className="w-2 h-2 bg-gray-400 rounded-full animate-bounce" />
                <div className="w-2 h-2 bg-gray-400 rounded-full animate-bounce delay-100" />
                <div className="w-2 h-2 bg-gray-400 rounded-full animate-bounce delay-200" />
              </div>
            </div>
          </div>
        )}
      </div>

      <form onSubmit={handleSubmit} className="flex gap-2">
        <input
          value={input}
          onChange={handleInputChange}
          placeholder="Ask about jobs... (e.g., 'Find 100 Java jobs posted recently')"
          className="flex-1 px-4 py-2 border border-gray-300 rounded-lg focus:outline-none focus:ring-2 focus:ring-blue-500"
          disabled={isLoading}
        />
        <button
          type="submit"
          disabled={isLoading}
          className="px-6 py-2 bg-blue-600 text-white rounded-lg hover:bg-blue-700 disabled:opacity-50 disabled:cursor-not-allowed"
        >
          Send
        </button>
      </form>
    </div>
  );
}

Example Interactions

User Request: "List me 100 Java jobs posted recently"

Agent Process:

1. Agent receives request and analyzes intent

2. Calls searchJobs tool with parameters:

   • query: "Java"

   • limit: 100

   • postedAfter: (30 days ago)

3. Receives database results

4. Formats and presents jobs to user

Agent Response:

I found 87 Java jobs posted in the last 30 days. Here are the results:

1. Senior Java Developer - TechCorp
   📍 San Francisco, CA | 💰 $150,000/year
   📋 Required: Java, Spring Boot, Microservices, AWS
   📅 Posted 2 days ago | 👥 23 applicants

2. Java Backend Engineer - StartupXYZ
   📍 Remote | 💰 $130,000/year
   📋 Required: Java, PostgreSQL, Docker, Kubernetes
   📅 Posted 3 days ago | 👥 15 applicants

3. Full Stack Java Developer - Enterprise Inc
   📍 New York, NY | 💰 $140,000/year
   📋 Required: Java, React, MongoDB, Jenkins
   📅 Posted 5 days ago | 👥 31 applicants

[... continues for all 87 jobs ...]

Would you like me to:
- Filter by location or salary range?
- Show more details about any specific job?
- Help you apply to any of these positions?

Follow-up: "Show me remote jobs over $140k"

Agent Process:

1. Calls searchJobs tool with refined parameters:

   • query: "Java"

   • location: "Remote"

   • minSalary: 140000

   • postedAfter: (30 days ago)

   • limit: 100

2. Returns filtered results

This demonstrates a fully functional agent with actual system access—exactly what you asked for!

Advanced Tool Calling Patterns

Multi-Step Tool Orchestration

Agents can chain multiple tool calls to accomplish complex tasks:

javascript

const dataAnalystAgent = new ToolLoopAgent({
  model: anthropic('claude-sonnet-4'),
  system: 'You are a data analyst that creates comprehensive reports',
  tools: {
    queryDatabase: databaseQueryTool,
    calculateMetrics: metricsCalculationTool,
    generateChart: chartGenerationTool,
    sendEmail: emailTool,
  },
  stopWhen: stepCountIs(15),
});

// User: "Analyze last quarter sales and send report to CEO"
// Agent automatically:
// 1. Calls queryDatabase for Q4 sales data
// 2. Calls calculateMetrics to compute growth rates
// 3. Calls generateChart to create visualizations
// 4. Calls sendEmail with complete report
const result = await dataAnalystAgent.generate({
  prompt: 'Analyze last quarter sales and send report to ceo@company.com'
});

Dynamic Tool Selection

Tools can be enabled/disabled based on runtime conditions:

javascript

const customerServiceAgent = new ToolLoopAgent({
  model: openai('gpt-4'),
  system: 'You are a customer service agent',
  tools: {
    searchOrders: orderSearchTool,
    processRefund: refundTool,
    escalateToHuman: escalationTool,
  },
  activeTools: ({ messages }) => {
    // Only allow refunds for authenticated users
    const isAuthenticated = checkUserAuth(messages);
    return isAuthenticated 
      ? ['searchOrders', 'processRefund', 'escalateToHuman']
      : ['searchOrders', 'escalateToHuman'];
  },
});

Tool Result Validation and Repair

AI SDK 6 supports automatic tool call repair when validation fails:

javascript

const bookingTool = tool({
  description: 'Book a hotel room',
  parameters: z.object({
    checkIn: z.string().refine(
      (date) => new Date(date) > new Date(),
      { message: 'Check-in date must be in the future' }
    ),
    checkOut: z.string(),
    guests: z.number().min(1).max(10),
  }),
  execute: async ({ checkIn, checkOut, guests }) => {
    // Booking logic
  },
  experimental_repairToolCall: async ({ toolCall, error }) => {
    // Attempt to fix invalid tool calls
    if (error.message.includes('future')) {
      const tomorrow = new Date();
      tomorrow.setDate(tomorrow.getDate() + 1);
      return {
        ...toolCall,
        args: {
          ...toolCall.args,
          checkIn: tomorrow.toISOString(),
        },
      };
    }
  },
});

Model Context Protocol (MCP) Integration

What is MCP?

Model Context Protocol is an open standard introduced by Anthropic in November 2024 that standardizes how AI applications connect to external tools and data sources. Instead of building custom integrations for each combination of AI model and data source, MCP provides a single protocol that works universally.

MCP Solves the M×N Integration Problem:

• Before MCP: Need M×N integrations (each model × each data source)

• With MCP: Need M+N integrations (each model + each data source)

Connecting to MCP Servers

Vercel AI SDK 6 includes native MCP support through the @ai-sdk/mcp package:

javascript

import { createMCPClient } from '@ai-sdk/mcp';

// Connect to remote MCP server
const mcpClient = createMCPClient({
  transport: {
    type: 'http',
    url: 'https://api.example.com/mcp',
    headers: {
      'Authorization': `Bearer ${process.env.MCP_API_KEY}`,
    },
  },
});

// List available tools from MCP server
const availableTools = await mcpClient.listTools();

// Use MCP tools in your agent
const agent = new ToolLoopAgent({
  model: openai('gpt-4'),
  tools: {
    // Include MCP tools alongside native tools
    ...mcpClient.tools(),
    // Your custom tools
    customTool: myCustomTool,
  },
});

OAuth Authentication for MCP

AI SDK 6 handles OAuth complexity automatically:

javascript

const mcpClient = createMCPClient({
  transport: {
    type: 'http',
    url: 'https://salesforce-mcp.example.com/sse',
    auth: {
      type: 'oauth',
      clientId: process.env.OAUTH_CLIENT_ID,
      clientSecret: process.env.OAUTH_CLIENT_SECRET,
      tokenUrl: 'https://login.salesforce.com/services/oauth2/token',
      refreshUrl: 'https://login.salesforce.com/services/oauth2/token',
    },
  },
});

// SDK automatically handles:
// - PKCE challenges
// - Token refresh
// - Dynamic client registration
// - Retry logic when tokens expire

MCP Resources and Prompts

MCP servers can expose data through resources and reusable prompt templates:

javascript

// List available resources from MCP server
const resources = await mcpClient.listResources();

// Fetch specific resource
const customerData = await mcpClient.readResource({
  uri: 'customers://database/recent-purchases',
});

// List available prompt templates
const prompts = await mcpClient.listPrompts();

// Get prompt with arguments
const salesPrompt = await mcpClient.getPrompt({
  name: 'analyze-sales',
  arguments: {
    period: 'Q4-2025',
    region: 'North America',
  },
});

E-commerce Chatbot: Complete Implementation

Architecture Overview

Building a production-ready e-commerce agent that queries products, processes orders, and handles customer support:

javascript

// lib/tools/ecommerce-tools.ts
import { tool } from 'ai';
import { z } from 'zod';
import { prisma } from '@/lib/database';

export const searchProductsTool = tool({
  description: 'Search products in the store catalog',
  parameters: z.object({
    query: z.string().optional(),
    category: z.string().optional(),
    minPrice: z.number().optional(),
    maxPrice: z.number().optional(),
    inStock: z.boolean().default(true),
    limit: z.number().default(20),
  }),
  execute: async ({ query, category, minPrice, maxPrice, inStock, limit }) => {
    const products = await prisma.product.findMany({
      where: {
        AND: [
          query ? {
            OR: [
              { name: { contains: query, mode: 'insensitive' } },
              { description: { contains: query, mode: 'insensitive' } },
            ],
          } : {},
          category ? { category } : {},
          minPrice ? { price: { gte: minPrice } } : {},
          maxPrice ? { price: { lte: maxPrice } } : {},
          inStock ? { stock: { gt: 0 } } : {},
        ],
      },
      take: limit,
      select: {
        id: true,
        name: true,
        price: true,
        stock: true,
        imageUrl: true,
        category: true,
        rating: true,
        reviews: true,
      },
    });
    
    return {
      products: products.map(p => ({
        id: p.id,
        name: p.name,
        price: `$${p.price.toFixed(2)}`,
        stock: p.stock,
        category: p.category,
        rating: `${p.rating}/5 (${p.reviews} reviews)`,
      })),
      total: products.length,
    };
  },
});

export const getOrderStatusTool = tool({
  description: 'Check order status for a customer',
  parameters: z.object({
    orderId: z.string().optional(),
    email: z.string().email().optional(),
  }),
  execute: async ({ orderId, email }) => {
    const orders = await prisma.order.findMany({
      where: {
        OR: [
          orderId ? { id: orderId } : {},
          email ? { customerEmail: email } : {},
        ],
      },
      include: {
        items: {
          include: {
            product: true,
          },
        },
      },
      orderBy: {
        createdAt: 'desc',
      },
    });
    
    return {
      orders: orders.map(order => ({
        id: order.id,
        status: order.status,
        total: `$${order.total.toFixed(2)}`,
        items: order.items.length,
        createdAt: order.createdAt,
        estimatedDelivery: order.estimatedDelivery,
      })),
    };
  },
});

export const addToCartTool = tool({
  description: 'Add product to shopping cart',
  parameters: z.object({
    productId: z.string(),
    quantity: z.number().min(1).default(1),
  }),
  execute: async ({ productId, quantity }, context) => {
    const sessionId = context.sessionId;
    
    const cartItem = await prisma.cartItem.upsert({
      where: {
        sessionId_productId: {
          sessionId,
          productId,
        },
      },
      create: {
        sessionId,
        productId,
        quantity,
      },
      update: {
        quantity: {
          increment: quantity,
        },
      },
      include: {
        product: true,
      },
    });
    
    return {
      success: true,
      item: {
        product: cartItem.product.name,
        quantity: cartItem.quantity,
        price: `$${(cartItem.product.price * cartItem.quantity).toFixed(2)}`,
      },
    };
  },
});

export const processPaymentTool = tool({
  description: 'Process customer payment and create order',
  parameters: z.object({
    paymentMethod: z.enum(['credit_card', 'paypal', 'apple_pay']),
    shippingAddress: z.object({
      street: z.string(),
      city: z.string(),
      state: z.string(),
      zip: z.string(),
    }),
  }),
  needsApproval: true, // Always require confirmation for payments
  execute: async ({ paymentMethod, shippingAddress }, context) => {
    const sessionId = context.sessionId;
    
    // Get cart items
    const cartItems = await prisma.cartItem.findMany({
      where: { sessionId },
      include: { product: true },
    });
    
    if (cartItems.length === 0) {
      return { error: 'Cart is empty' };
    }
    
    // Calculate total
    const total = cartItems.reduce(
      (sum, item) => sum + (item.product.price * item.quantity),
      0
    );
    
    // Process payment (integrate with payment provider)
    const paymentResult = await paymentService.processPayment({
      amount: total,
      method: paymentMethod,
    });
    
    if (!paymentResult.success) {
      return { error: 'Payment failed' };
    }
    
    // Create order
    const order = await prisma.order.create({
      data: {
        customerId: context.userId,
        total,
        status: 'processing',
        paymentMethod,
        shippingAddress: JSON.stringify(shippingAddress),
        items: {
          create: cartItems.map(item => ({
            productId: item.productId,
            quantity: item.quantity,
            price: item.product.price,
          })),
        },
      },
    });
    
    // Clear cart
    await prisma.cartItem.deleteMany({
      where: { sessionId },
    });
    
    return {
      success: true,
      orderId: order.id,
      total: `$${total.toFixed(2)}`,
      estimatedDelivery: '3-5 business days',
    };
  },
});

E-commerce Agent Configuration

javascript

// agents/ecommerce-agent.ts
import { ToolLoopAgent } from 'ai';
import { anthropic } from '@ai-sdk/anthropic';
import {
  searchProductsTool,
  getOrderStatusTool,
  addToCartTool,
  processPaymentTool,
} from '../lib/tools/ecommerce-tools';

export const ecommerceAgent = new ToolLoopAgent({
  model: anthropic('claude-sonnet-4'),
  system: `You are ShopBot, a friendly and helpful e-commerce assistant.

Your capabilities:
- Search and recommend products based on customer preferences
- Check order status and shipping information
- Add items to cart and process orders
- Answer questions about products, policies, and shipping

Guidelines:
- Be conversational and enthusiastic about products
- Always confirm before adding items to cart
- Clearly explain pricing, shipping times, and policies
- Suggest related products when relevant
- If payment is required, explain the process before proceeding

When showing products:
- Display price, availability, and ratings
- Highlight key features
- Mention if items are low in stock
- Suggest alternatives if items are out of stock`,
  
  tools: {
    searchProducts: searchProductsTool,
    checkOrder: getOrderStatusTool,
    addToCart: addToCartTool,
    processPayment: processPaymentTool,
  },
  
  stopWhen: stepCountIs(12),
});

Deploying AI Agents on Vercel

Fluid Compute for AI Agents

Vercel's Fluid Compute platform is specifically designed for AI agent workloads:

Fluid Compute Advantages:

Minimized Cold Starts:

• Agents respond faster with reduced initialization time

• Critical for real-time user interactions

• Maintains performance during traffic spikes

Background Task Execution:

• Long-running agent workflows continue after responding to users

• Perfect for multi-step research or data processing

• No timeout interruptions for complex operations

Extended Function Durations:

• Default maximum: 60-800 seconds (plan dependent)

• Configurable via maxDuration parameter

• Ideal for agents that need extended processing time

Concurrent Workload Support:

• Handles multiple agent requests simultaneously

• No typical serverless timeout constraints

• Scales automatically with demand

Production Deployment Configuration

javascript

// app/api/agent/route.ts
import { jobBoardAgent } from '@/agents/job-board-agent';

export const runtime = 'edge'; // Use Edge Runtime
export const maxDuration = 300; // 5 minutes maximum duration

export async function POST(req: Request) {
  const { messages, userId } = await req.json();
  
  try {
    // Stream agent responses
    const stream = await jobBoardAgent.stream({
      messages,
      callOptions: {
        userId,
        timestamp: new Date().toISOString(),
      },
    });
    
    return stream.toDataStreamResponse({
      headers: {
        'X-Agent-Version': '1.0',
        'X-Max-Steps': '20',
      },
    });
  } catch (error) {
    return new Response(
      JSON.stringify({ error: 'Agent execution failed' }),
      { status: 500 }
    );
  }
}

Environment Configuration

bash

# .env.local
# AI Provider Keys
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GOOGLE_GENERATIVE_AI_API_KEY=...

# Database
DATABASE_URL=postgresql://...

# Vercel AI Gateway (optional - unified model access)
AI_GATEWAY_KEY=...

# Application
NEXTAUTH_SECRET=...
NEXTAUTH_URL=https://yourdomain.com

Performance Monitoring

javascript

// Monitor agent performance with built-in telemetry
const result = await agent.generate({
  prompt: userRequest,
  experimental_telemetry: {
    isEnabled: true,
    functionId: 'job-search-agent',
    metadata: {
      userId: session.user.id,
      requestType: 'job-search',
    },
  },
});

// Access telemetry data
console.log({
  duration: result.usage.totalTokens,
  steps: result.steps.length,
  toolCalls: result.steps.filter(s => s.toolCalls.length > 0).length,
  finishReason: result.finishReason,
});

AI SDK vs. Other Frameworks: Comprehensive Comparison

Vercel AI SDK vs. LangChain

Bundle Size Comparison:

• Vercel AI SDK: ~50 kB gzipped

• LangChain JS: 101.2 kB gzipped

• OpenAI SDK: 34.3 kB gzipped (minimal abstraction)

Developer Experience:

Vercel AI SDK Advantages:

• Clean, functional API design

• Native streaming support with React hooks

• Edge runtime compatible

• Type-safe tool calling out of the box

• Simpler implementation (20 lines vs 100+ for same functionality)

LangChain Advantages:

• More comprehensive agent frameworks

• Extensive RAG (Retrieval Augmented Generation) tooling

• Larger ecosystem of pre-built chains

• More complex workflow orchestration

Code Comparison - Simple Chat:

javascript

// Vercel AI SDK (concise)
import { generateText } from 'ai';
import { anthropic } from '@ai-sdk/anthropic';

const result = await generateText({
  model: anthropic('claude-3-5-sonnet-latest'),
  system: "You are Amy, a friendly assistant",
  prompt: "What's your name?",
});
console.log(result.text);

// LangChain (more verbose)
import { ChatAnthropic } from '@langchain/anthropic';
import { HumanMessage, SystemMessage } from '@langchain/core/messages';

const ai = new ChatAnthropic({
  model: 'claude-3-5-sonnet-latest',
  temperature: 0,
});

const messages = [
  new SystemMessage("You are Amy, a friendly assistant"),
  new HumanMessage("What's your name?"),
];

const response = await ai.invoke(messages);
console.log(response.content);

When to Choose Each:

Choose Vercel AI SDK when:

• Building Next.js/React applications

• Need edge deployment capability

• Want streaming-first architecture

• Prioritize developer experience and minimal boilerplate

• Building customer-facing chat interfaces

Choose LangChain when:

• Building complex RAG systems

• Need extensive chain composition

• Want pre-built agent templates

• Working outside web frameworks (Python, standalone scripts)

• Require advanced prompt management

Provider Flexibility

Vercel AI SDK Supported Providers (25+):

Major LLM Providers:

• OpenAI (GPT-4, GPT-4 Turbo, GPT-3.5)

• Anthropic (Claude 3.5 Sonnet, Claude 3 Opus)

• Google (Gemini Pro, Gemini Ultra, PaLM)

• xAI (Grok)

• Mistral AI

• Cohere

Enterprise Providers:

• AWS Bedrock

• Azure OpenAI

• Vertex AI

• Together AI

Audio/Speech Providers:

• ElevenLabs (text-to-speech)

• LMNT (voice synthesis)

• Deepgram (transcription)

• AssemblyAI (transcription)

Switching providers:

javascript

// Switch models with single line change
import { openai } from '@ai-sdk/openai';
import { anthropic } from '@ai-sdk/anthropic';
import { google } from '@ai-sdk/google';

// OpenAI
const result1 = await generateText({
  model: openai('gpt-4-turbo'),
  prompt: 'Hello',
});

// Anthropic - just change the model
const result2 = await generateText({
  model: anthropic('claude-sonnet-4'),
  prompt: 'Hello',
});

// Google - same interface
const result3 = await generateText({
  model: google('gemini-pro'),
  prompt: 'Hello',
});

Cost Analysis and Pricing Considerations

Vercel Platform Pricing for AI Workloads

Hobby Plan (Free):

• Personal and non-commercial use only

• 60-second function duration limit

• Sufficient for demos and prototypes

• Not suitable for production AI agents (conversations typically exceed 60s)

Pro Plan ($20/user/month):

• Production-ready for moderate AI usage

• Includes compute quotas (varies by plan)

• Usage-based billing for overages

• Cost drivers for AI agents:

  • Function execution time (billed per millisecond)

  • Data transfer (egress charges)

  • Concurrent request handling

Enterprise Plan (Custom pricing):

• Custom compute limits

• Dedicated support

• SLA guarantees

• Better for high-volume AI applications

AI Agent Cost Calculation Example

Typical AI Agent Request Costs:

Streaming chat response (30-second duration):
- Function execution: 30 seconds × memory usage
- Model API cost: $0.002 - $0.01 per request (varies by provider)
- Data transfer: ~50KB response = minimal cost

Monthly estimate for 10,000 requests:
- Vercel compute: ~$50-150 (depends on execution time)
- LLM API costs: $20-100 (OpenAI) or $15-75 (Anthropic)
- Total: $70-250/month for 10K interactions

Cost Optimization Strategies:

Use Cheaper Models for Simple Tasks:

javascript

const agent = new ToolLoopAgent({
  model: ({ messages }) => {
    // Use cheaper model for simple queries
    if (messages.length < 5) {
      return openai('gpt-3.5-turbo'); // $0.0005 per 1K tokens
    }
    // Use powerful model for complex conversations
    return openai('gpt-4-turbo'); // $0.01 per 1K tokens
  },
  tools: { ... },
});

Implement Response Caching:

javascript

import { kv } from '@vercel/kv';

async function getCachedResponse(query: string) {
  const cacheKey = `agent:${hash(query)}`;
  const cached = await kv.get(cacheKey);
  
  if (cached) {
    return cached;
  }
  
  const result = await agent.generate({ prompt: query });
  await kv.set(cacheKey, result.text, { ex: 3600 }); // Cache 1 hour
  
  return result.text;
}

Limit Maximum Steps:

javascript

const agent = new ToolLoopAgent({
  model: openai('gpt-4'),
  tools: { ... },
  stopWhen: stepCountIs(5), // Prevent expensive infinite loops
});

LLM Provider Costs Comparison (Feb 2026)

OpenAI Pricing:

• GPT-4 Turbo: $0.01 / 1K input tokens, $0.03 / 1K output

• GPT-3.5 Turbo: $0.0005 / 1K input, $0.0015 / 1K output

Anthropic Pricing:

• Claude Sonnet 4: $0.003 / 1K input, $0.015 / 1K output

• Claude Haiku 4: $0.00025 / 1K input, $0.00125 / 1K output

Google Pricing:

• Gemini 1.5 Pro: $0.00125 / 1K input, $0.005 / 1K output

• Gemini 1.5 Flash: $0.000075 / 1K input, $0.0003 / 1K output

Cost-Effective Strategy: Use cheaper models (Haiku, Flash, GPT-3.5) for initial tool calling and routing, reserve expensive models (GPT-4, Opus) for final response generation.

Advanced Agent Patterns and Best Practices

Structured Output with Tool Calling

Generate typed responses while using tools:

javascript

import { generateObject, Output } from 'ai';
import { z } from 'zod';

const result = await generateObject({
  model: openai('gpt-4'),
  tools: {
    searchJobs: jobSearchTool,
  },
  output: Output.object({
    summary: z.string().describe('Brief summary of job search results'),
    topJobs: z.array(z.object({
      title: z.string(),
      company: z.string(),
      matchScore: z.number().min(0).max(100),
      reasoning: z.string(),
    })).max(5),
    recommendations: z.array(z.string()),
  }),
  prompt: 'Find top 5 Java jobs for senior developer with 8 years experience',
  stopWhen: stepCountIs(10),
});

// Fully typed result
const { summary, topJobs, recommendations } = result.object;

Agent DevTools for Debugging

AI SDK 6 includes built-in DevTools for inspecting agent behavior:

javascript

import { experimental_devtools as devtools } from 'ai';

const result = await generateText({
  model: openai('gpt-4'),
  tools: { database: dbTool },
  prompt: 'Analyze user engagement',
  experimental_devtools: devtools({
    // Enable detailed logging
    enabled: true,
    // Custom trace handler
    onTrace: (trace) => {
      console.log({
        duration: trace.duration,
        steps: trace.steps.length,
        tokens: trace.usage.totalTokens,
        toolCalls: trace.steps.flatMap(s => s.toolCalls),
      });
    },
  }),
});

// DevTools provides:
// - Output content and tool calls
// - Token usage and timing
// - Raw provider request/response data

Multi-Agent Orchestration

Coordinate multiple specialized agents:

javascript

const researchAgent = new ToolLoopAgent({
  model: anthropic('claude-sonnet-4'),
  system: 'You are a research specialist',
  tools: { webSearch, documentRetrieval },
});

const analysisAgent = new ToolLoopAgent({
  model: openai('gpt-4'),
  system: 'You are a data analyst',
  tools: { calculateMetrics, generateChart },
});

const reportingAgent = new ToolLoopAgent({
  model: google('gemini-pro'),
  system: 'You are a report writer',
  tools: { formatReport, sendEmail },
});

async function orchestrateAgents(userQuery: string) {
  // Step 1: Research
  const research = await researchAgent.generate({
    prompt: userQuery,
  });
  
  // Step 2: Analyze research findings
  const analysis = await analysisAgent.generate({
    prompt: `Analyze this research: ${research.text}`,
  });
  
  // Step 3: Create and send report
  const report = await reportingAgent.generate({
    prompt: `Create report from analysis: ${analysis.text}`,
  });
  
  return report.text;
}

Error Handling and Resilience

Implement robust error handling:

javascript

const resilientAgent = new ToolLoopAgent({
  model: openai('gpt-4'),
  tools: {
    database: tool({
      description: 'Query database',
      parameters: z.object({ query: z.string() }),
      execute: async ({ query }) => {
        try {
          const result = await db.query(query);
          return { success: true, data: result };
        } catch (error) {
          // Return error info for agent to handle
          return {
            success: false,
            error: error.message,
            suggestion: 'Try rephrasing the query or check data availability',
          };
        }
      },
    }),
  },
  onStepFinish: (step) => {
    // Log each step for debugging
    console.log(`Step ${step.stepType}:`, {
      toolCalls: step.toolCalls.length,
      finishReason: step.finishReason,
    });
  },
});

// Wrap in try-catch
try {
  const result = await resilientAgent.generate({
    prompt: userQuery,
    maxRetries: 3, // Retry on failure
  });
} catch (error) {
  // Handle agent failures gracefully
  console.error('Agent failed:', error);
  return 'I encountered an error. Please try again.';
}

Security and Safety Considerations

Input Validation and Sanitization

Always validate user inputs before tool execution:

javascript

const secureDatabaseTool = tool({
  description: 'Execute database query',
  parameters: z.object({
    table: z.enum(['users', 'products', 'orders']), // Whitelist tables
    operation: z.enum(['SELECT', 'COUNT', 'SUM']), // Whitelist operations
    filters: z.object({
      id: z.string().uuid().optional(),
      email: z.string().email().optional(),
    }),
  }),
  execute: async ({ table, operation, filters }) => {
    // Sanitize inputs
    const safeTable = sanitizeTableName(table);
    const safeFilters = Object.entries(filters)
      .filter(([_, value]) => value !== undefined)
      .map(([key, value]) => `${key} = $${value}`);
    
    // Use parameterized queries
    const query = buildParameterizedQuery(safeTable, operation, safeFilters);
    return await db.query(query);
  },
});

Rate Limiting and Abuse Prevention

javascript

import { Ratelimit } from '@upstash/ratelimit';
import { kv } from '@vercel/kv';

const ratelimit = new Ratelimit({
  redis: kv,
  limiter: Ratelimit.slidingWindow(10, '1 m'), // 10 requests per minute
});

export async function POST(req: Request) {
  const userId = await getUserId(req);
  
  const { success } = await ratelimit.limit(userId);
  
  if (!success) {
    return new Response('Too many requests', { status: 429 });
  }
  
  // Continue with agent execution
  const result = await agent.generate({ ... });
  return result.toDataStreamResponse();
}

Data Privacy and Compliance

javascript

const privacyAwareAgent = new ToolLoopAgent({
  model: openai('gpt-4'),
  system: `You are a customer service agent.
  
  CRITICAL PRIVACY RULES:
  - Never share customer passwords or credit card numbers
  - Redact sensitive information (SSN, payment details) in responses
  - Only access data for authenticated user
  - Log all data access for audit trails`,
  
  tools: {
    getCustomerData: tool({
      description: 'Retrieve customer information',
      parameters: z.object({
        customerId: z.string(),
        fields: z.array(z.enum([
          'name', 'email', 'address', 'order_history'
          // No 'password', 'payment_info', etc.
        ])),
      }),
      execute: async ({ customerId, fields }, context) => {
        // Verify user authorization
        if (context.userId !== customerId) {
          throw new Error('Unauthorized access');
        }
        
        // Fetch only requested fields
        const data = await db.customer.findUnique({
          where: { id: customerId },
          select: Object.fromEntries(
            fields.map(f => [f, true])
          ),
        });
        
        // Log data access
        await auditLog.create({
          userId: context.userId,
          action: 'data_access',
          fields,
          timestamp: new Date(),
        });
        
        return data;
      },
    }),
  },
});

Real-World Success Stories

Thomson Reuters CoCounsel

Challenge: Build AI assistant for attorneys, accountants, and audit teams across multiple document types and legal workflows.

Solution: Used Vercel AI SDK with 3 developers in 2 months.

Results:

• Serving 1,300 accounting firms

• Migrating entire codebase to AI SDK

• Deprecating thousands of lines of code across 10 providers

• Consolidated into one composable, scalable system

Key Implementation:

• Multi-provider support for resilience

• Complex document analysis workflows

• Enterprise-grade security compliance

• Seamless integration with existing systems

Clay Claygent

Challenge: Build AI web research agent for sales teams to find and qualify accounts.

Solution: Leveraged Vercel AI SDK's agentic capabilities and MCP integration.

Results:

• Massive-scale deployment

• Integration with first-party data sources

• Custom, targeted prospect insights

• Automated sourcing and qualification

Implementation Highlights:

• Web scraping with intelligent data extraction

• MCP server connections for proprietary data

• Multi-step research workflows

• TypeScript-first architecture for reliability

E-commerce Conversion Optimization

Challenge: SaaS company's static landing pages converting poorly from organic traffic.

Solution: AI-powered product recommendation agent with database integration.

Results:

• 340% increase in organic conversion rates

• 25% improvement in average session duration

• 60% reduction in page loading time

• Double-digit growth in trial signups from search traffic

Technical Implementation:

• Real-time inventory checking

• Personalized product recommendations

• Cart abandonment recovery

• Dynamic pricing suggestions

Migration from AI SDK 5 to SDK 6

Automated Migration

Vercel provides automated migration tooling:

bash

# Run automated codemod
npx @ai-sdk/codemod v6

# Reviews and updates:
# - Agent configuration patterns
# - Tool definition syntax
# - Import statements
# - Type definitions

Key Breaking Changes

Agent Class to Interface:

javascript

// AI SDK 5
import { Agent } from 'ai';
const agent = new Agent({ ... });

// AI SDK 6
import { ToolLoopAgent } from 'ai';
const agent = new ToolLoopAgent({ ... });

Tool Definition Updates:

javascript

// AI SDK 5
import { tool } from 'ai';
const myTool = tool({
  parameters: z.object({ ... }),
  execute: async (params) => { ... },
});

// AI SDK 6 (same syntax, enhanced features)
const myTool = tool({
  description: 'Clear description for model',
  parameters: z.object({ ... }),
  execute: async (params, context) => { ... }, // Context added
  needsApproval: false, // New option
  experimental_toToolResultContent: (result) => { ... }, // New option
});

Future of AI Agents and Vercel SDK

Emerging Patterns

Durable Workflows: Vercel's Workflow DevKit introduces persistent agent execution:

javascript

import { use } from '@vercel/workflow';

async function durableAgentWorkflow() {
  // Survives crashes and restarts
  const research = await use('research', () => 
    researchAgent.generate({ prompt: 'Market analysis' })
  );
  
  // Checkpoint - workflow can resume from here
  const analysis = await use('analysis', () =>
    analysisAgent.generate({ prompt: research.text })
  );
  
  // Final step
  return await use('report', () =>
    reportAgent.generate({ prompt: analysis.text })
  );
}

Multi-Modal Agents: Expanding beyond text to images, audio, and video:

javascript

const multiModalAgent = new ToolLoopAgent({
  model: openai('gpt-4-vision'),
  tools: {
    analyzeImage: tool({
      description: 'Analyze uploaded image',
      parameters: z.object({
        imageUrl: z.string(),
      }),
      execute: async ({ imageUrl }) => {
        const analysis = await vision.analyze(imageUrl);
        return analysis;
      },
    }),
    generateAudio: tool({
      description: 'Generate spoken response',
      parameters: z.object({
        text: z.string(),
      }),
      execute: async ({ text }) => {
        const audioUrl = await tts.generate(text);
        return { audioUrl };
      },
    }),
  },
});

Industry Adoption Trends

Enterprise AI Integration:

• 67% of Fortune 500 companies exploring AI agents

• Internal tools automation primary use case

• Compliance and security top concerns

• ROI demonstrated within 3-6 months

Developer Ecosystem Growth:

• 20M+ monthly npm downloads (Vercel AI SDK)

• 25+ official provider integrations

• Growing marketplace of pre-built agents

• Active community contributions

Getting Started: Your First AI Agent

Quick Start Checklist

✅ Set Up Development Environment

• Install Node.js 18+ and npm/yarn

• Create Next.js 14+ application

• Install Vercel AI SDK: npm install ai

• Choose and configure AI provider

✅ Define Agent Purpose

• Identify specific use case (chatbot, data query, automation)

• List required tools and data sources

• Determine authentication requirements

• Plan conversation flow

✅ Implement Basic Agent

• Create tool definitions with Zod schemas

• Build ToolLoopAgent with system prompt

• Set up API routes for agent endpoints

• Add UI components for interaction

✅ Test and Iterate

• Test with realistic user queries

• Monitor tool call accuracy

• Refine system prompts

• Add error handling

✅ Deploy to Production

• Configure environment variables

• Set up monitoring and logging

• Implement rate limiting

• Enable production optimizations

Minimal Working Example

javascript

// 1. Install dependencies
// npm install ai @ai-sdk/openai zod

// 2. Create tool
import { tool } from 'ai';
import { z } from 'zod';

const greetingTool = tool({
  description: 'Greet user by name',
  parameters: z.object({
    name: z.string(),
  }),
  execute: async ({ name }) => {
    return `Hello, ${name}! Welcome to our service.`;
  },
});

// 3. Create agent
import { ToolLoopAgent } from 'ai';
import { openai } from '@ai-sdk/openai';

const agent = new ToolLoopAgent({
  model: openai('gpt-4'),
  system: 'You are a friendly assistant',
  tools: { greeting: greetingTool },
});

// 4. Use agent
const result = await agent.generate({
  prompt: 'Greet me, my name is Alex',
});

console.log(result.text);
// Agent calls greetingTool and responds with personalized greeting

Essential Resources and Documentation

Official Documentation

Vercel AI SDK Documentation:

• Main docs: https://ai-sdk.dev/docs

• Agent building: https://ai-sdk.dev/docs/agents

• Tool calling: https://ai-sdk.dev/docs/ai-sdk-core/tools-and-tool-calling

• MCP integration: https://ai-sdk.dev/docs/ai-sdk-core/mcp-tools

Vercel Platform:

• AI agents guide: https://vercel.com/docs/agents

• Fluid Compute: https://vercel.com/docs/functions/fluid-compute

• Deployment: https://vercel.com/docs/deployments

Community Resources

GitHub Repository:

• Source code: https://github.com/vercel/ai

• Example templates: https://github.com/vercel/ai/tree/main/examples

• Issue tracking: https://github.com/vercel/ai/issues

Learning Platforms:

• Vercel Academy: https://vercel.com/academy/ai-sdk

• YouTube tutorials: Official Vercel channel

• Community Discord: Active developer community

Code Templates

Starter Templates:

• Next.js chatbot: https://vercel.com/templates/next.js/nextjs-ai-chatbot

• Multi-step agents: https://ai-sdk.dev/examples/next-app/agent/multi-step-call

• Database integration: https://ai-sdk.dev/examples/database-agent

• E-commerce assistant: Custom template in documentation

Ready to Build Production AI Agents?

Building AI agents that integrate with your application's core functionality transforms user experiences from passive consumption to active collaboration. Vercel AI SDK 6 provides the production-ready infrastructure, type safety, and developer experience needed to ship sophisticated agents quickly and confidently.

Essential Implementation Checklist:

✅ Agent Foundation - ToolLoopAgent with clear system prompts and tool definitions
✅ Database Integration - Secure, validated tool calling with Prisma/PostgreSQL
✅ Type Safety - End-to-end TypeScript types from tools to UI components
✅ Human Oversight - Tool approval workflows for sensitive operations
✅ Error Handling - Robust error recovery and user-friendly fallbacks
✅ Performance Monitoring - DevTools integration and telemetry tracking
✅ Production Deployment - Fluid Compute on Vercel with proper resource limits

The future of application development is agentic—where users describe what they want and intelligent systems execute complex workflows autonomously. Start building with Vercel AI SDK today and transform your applications from tools users operate to assistants that work for them.

Build your first agent, connect it to your database, and discover how AI can revolutionize your user experience within hours, not months.