Building Sequential AI Agents with Vercel AI SDK (Multi-Step LLM Workflows)

Most AI agents today are just a single prompt and a single response.

That approach works—until you need structure, reliability, or production-grade workflows.

In this post, we’ll explore sequential AI agents, how they differ from normal AI agents, and how you can build a multi-step AI workflow using the Vercel AI SDK.

What Is an AI Agent?

An AI agent is a system that:

1. Takes an input (user query, event, or data)

2. Uses an LLM to reason or generate output

3. Optionally calls tools, APIs, or functions

4. Returns a result or performs an action

In many applications, this entire process happens in one step.

Example:

User asks: “Write a product update email”
→ LLM generates the email in a single response

This works well for simple tasks—but it starts breaking down as complexity grows.

Normal AI Agent (Single-Step Agent)

A normal AI agent typically follows this flow:

Input → LLM → Output

Characteristics

• Single prompt

• Single LLM call

• Minimal or no intermediate state

• Fast and cheap

Example Use Cases

• Chatbots

• Text rewriting

• Summarization

• Simple content generation

Limitations

• Hard to enforce structure

• No explicit reasoning steps

• Poor control over multi-stage workflows

• Difficult to debug or extend

When tasks require planning, validation, transformation, or multiple roles, a single-step agent becomes fragile.

What Is a Sequential AI Agent?

A sequential AI agent breaks a task into multiple ordered steps, where:

• Each step has a clear responsibility

• Output of one step becomes input for the next

• Context accumulates across steps

Input
  ↓
Step 1 (Planner Agent)
  ↓
Step 2 (Executor Agent)
  ↓
Step 3 (Refiner / Validator Agent)
  ↓
Final Output

Instead of asking the model to do everything at once, we guide it through a pipeline.

Normal Agent vs Sequential Agent

Sequential agents trade simplicity for control and reliability.

When Are Sequential AI Agents Beneficial?

Sequential agents are ideal when:

1. Tasks Have Clear Phases

Example:

• Planning

• Writing

• Reviewing

• Formatting

2. Output Must Follow Strict Structure

• Emails

• Reports

• JSON schemas

• Code generation

3. Different “Roles” Are Needed

• Product marketer

• Engineer

• Editor

4. You Want Deterministic Pipelines

• SaaS features

• Automations

• Multi-tenant systems

This is why sequential agents work extremely well for:

• Product update emails

• CRM workflows

• Content pipelines

• Data extraction and transformation

Designing a Sequential Agent (Conceptually)

Let’s say we want to build a content generation agent.

Step 1: Planner Agent

Responsibility:

• Analyze the input

• Break it into structured sections

Output:

{
  "sections": ["Introduction", "Key Points", "Conclusion"]
}

Step 2: Writer Agent

Responsibility:

• Generate content for each section

Input:

• Original user input

• Planner output

Step 3: Refiner Agent

Responsibility:

• Improve tone

• Fix grammar

• Enforce constraints

Each step is predictable and replaceable.

Implementing a Sequential AI Agent with Vercel AI SDK

Step 1: Create the Planner Agent

import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

export async function plannerAgent(input: string) {
  const result = await generateText({
    model: openai("gpt-4.1"),
    prompt: `Analyze the input and create a structured plan.\n\nInput: ${input}`,
  });

  return result.text;
}

Step 2: Create the Writer Agent

export async function writerAgent(plan: string, input: string) {
  const result = await generateText({
    model: openai("gpt-4.1"),
    prompt: `Using the following plan, write detailed content.\n\nPlan:\n${plan}\n\nInput:\n${input}`,
  });

  return result.text;
}

Step 3: Create the Refiner Agent

export async function refinerAgent(content: string) {
  const result = await generateText({
    model: openai("gpt-4.1"),
    prompt: `Refine the following content for clarity and tone.\n\n${content}`,
  });

  return result.text;
}

Step 4: Orchestrate the Sequential Flow

export async function sequentialAgent(input: string) {
  const plan = await plannerAgent(input);
  const draft = await writerAgent(plan, input);
  const finalOutput = await refinerAgent(draft);

  return finalOutput;
}

This orchestration is the heart of a sequential agent.

Benefits of This Approach

• Clear separation of responsibilities

• Easier debugging (inspect each step)

• Reusable agents

• Better output consistency

• Safer production usage

This is especially useful when building AI-powered SaaS features, not demos.

Final Thoughts

Sequential AI agents represent a shift from “ask the model to do everything” to “designing AI workflows.”

With the Vercel AI SDK, building these workflows feels natural and maintainable.

If you’re building:

• AI-first products

• Content pipelines

• Internal tooling

…sequential agents will give you control, clarity, and confidence.

If you’re interested, the next step could be:

• Adding validation agents

• Parallel agents

• Streaming intermediate steps

• Persisting agent state

That’s where AI engineering starts to feel like real software engineering 🚀