Architecting the Future: How AI Agentic Workflows are Redefining SaaS in 2026

import { ChatOpenAI } from "@langchain/openai";
import { StateGraph, Annotation } from "@langchain/langgraph";
import { ToolNode } from "@langchain/langgraph/prebuilt";
import { z } from "zod";

// Define the state of the agentic graph
const AgentState = Annotation.Root({
  messages: Annotation<any[]>({
    reducer: (x, y) => x.concat(y),
    default: () => [],
  }),
  codeQualityScore: Annotation<number>({
    reducer: (x, y) => y,
    default: () => 100,
  }),
  currentStep: Annotation<string>({
    reducer: (x, y) => y,
    default: () => "initialize",
  })
});

// Define tools the agent can use
const runCodeLinter = async (code: string) => {
  // Simulate linting logic
  const issues = code.includes("eval(") ? ["Security vulnerability: dynamic eval usage"] : [];
  return JSON.stringify({ issues, clean: issues.length === 0 });
};

// Orchestrate the graph
export async function runCodeAgent(sourceCode: string) {
  const model = new ChatOpenAI({ modelName: "gpt-4o-mini", temperature: 0.1 });
  
  const workflow = new StateGraph(AgentState)
    .addNode("agent", async (state) => {
      const response = await model.invoke([
        { role: "system", content: "You are an elite code quality auditor." },
        ...state.messages,
        { role: "user", content: `Audit this code: ${sourceCode}` }
      ]);
      return { messages: [response] };
    })
    .addNode("tools", new ToolNode([runCodeLinter]))
    .addEdge("__start__", "agent");
    
  const app = workflow.compile();
  const result = await app.invoke({
    messages: [{ role: "user", content: "Perform full quality inspection." }]
  });
  
  return result;
}