The Right Way to Add AI to Your Next.js App: Agents vs Workflows Decision Framework
Most Next.js apps don't need AI agents. They need workflows. Here's how to know which one you're actually building and the architecture patterns that work.
December 24, 2025 7 min read
You want to add AI to your Next.js app. You've read about AI agents. You're wondering if you need one.
Probably not.
Most teams building AI features think they're building agents. They're actually building workflows. The distinction matters because the architecture is completely different.
Agents make autonomous decisions. Workflows follow predefined paths. Agents cost 10x more to build right. Workflows ship in a week.
Here's how to know which one you need, and how to build it in Next.js without over-engineering.
The Agent vs Workflow Confusion
The AI hype cycle made "AI agent" synonymous with "AI feature." Companies sell chatbots as "intelligent agents." Developers call simple LLM calls "agentic workflows."
This creates confusion when you're architecting real features.
An AI agent:
Decides what to do based on context
Chooses which tools to use
Determines when to stop
Handles unexpected situations
Requires error recovery and state management
An AI workflow:
Follows a predefined sequence
Uses tools you specify upfront
Stops when the sequence ends
Handles expected situations
Requires basic error handling
Most AI features are workflows dressed up as agents.
Agents cost 5x more and take 7x longer. Only pay that premium when necessary.
The Decision Framework
Stop guessing. Use this framework.
Question 1: Does the AI need to decide what to do?
No: Build a workflow
Yes: Continue to question 2
Question 2: Can you predefine all possible paths?
Yes: Build a workflow with conditional logic
No: Continue to question 3
Question 3: Is iteration required based on intermediate results?
No: Build a multi-step workflow
Yes: Continue to question 4
Question 4: Can you afford 5-10x higher costs and longer response times?
No: Simplify to a workflow or rethink the feature
Yes: Build an agent
Most features fail this framework at question 1 or 2. That's good. Workflows are easier. Use our MVP calculator to estimate costs for both approaches before committing.
Common Next.js AI Patterns
Here are the actual patterns we ship in Next.js apps.
Pattern 1: Simple generation (workflow)
Pattern 2: RAG Q&A (workflow)
Pattern 3: Multi-step workflow
Pattern 4: Agent with tool calling
90% of projects use patterns 1-3. 10% actually need pattern 4.
Testing and Validation Strategies
Workflows are easy to test. Agents are hard.
Testing workflows:
Testing agents:
Agents require different validation. You test behavior, not exact outputs. Workflows let you test outputs directly.
This is another reason to prefer workflows when possible. Simpler testing means faster iteration.
Migration Path: Workflow to Agent
Start with workflows. Upgrade to agents only when workflows break. This phased approach is especially valuable for startup MVPs where speed to market matters.
Indicators you need to upgrade:
Users asking for capabilities beyond current workflow
Workflow becomes a giant conditional tree
You're building custom orchestration logic
Error handling gets complex because paths diverge
You need iteration based on intermediate results
When that happens, extract the complex part into an agent. Keep simple parts as workflows.
Hybrid architecture:
You don't need all agents or all workflows. Use the right tool for each feature.
Ready to Build AI Features the Right Way?
Most teams over-engineer AI features. They build agents when workflows suffice. They add complexity they don't need.
NextBuild helps startups ship AI features that match their actual requirements. Sometimes that's a simple workflow. Sometimes it's a full agent. We build what you need, not what's trendy.
We'll help you ship the right architecture, not the most complex one.
Chatbots are stateless. Agents accumulate state, make decisions, and run for minutes. Here are the 7 backend requirements that make or break production agents.
// lib/agents/researcher.tsimport { Mastra } from "@mastra/core";import { openai } from "@mastra/openai";import { z } from "zod";import { searchTool, analyzeTool } from "./tools";const mastra = new Mastra();export const researchAgent = mastra.agent({ name: "researcher", model: openai("gpt-4"), instructions: `You are a research assistant. Use available tools to gather and analyze information.`, tools: [searchTool, analyzeTool], schema: z.object({ findings: z.array(z.string()), analysis: z.string(), confidence: z.number(), }),});
typescript
// app/api/research/route.tsimport { researchAgent } from "@/lib/agents/researcher";export async function POST(req: Request) { const { query } = await req.json(); // Agent decides which tools to use and when const result = await researchAgent.generate(query); return Response.json(result);}
// No state needed - each request is independentexport async function POST(req: Request) { const { input } = await req.json(); const result = await processWithLLM(input); return Response.json(result);}
typescript
// Need to persist conversation and tool call historyimport { db } from "@/lib/db";export async function POST(req: Request) { const { sessionId, message } = await req.json(); // Load conversation history const history = await db.conversation.findUnique({ where: { sessionId }, }); // Agent uses history to make decisions const result = await agent.generate({ messages: history.messages, newMessage: message, }); // Save updated history await db.conversation.update({ where: { sessionId }, data: { messages: [...history.messages, result] }, }); return Response.json(result);}
