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Software Development

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Business

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AI/ML

AI in ERP software refers to the use of machine learning, NLP, and AI tools to improve forecasting, reporting, and decision-making inside ERP systems.

AI in ERP Software: Use Cases, Benefits, and Challenges

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How Should Businesses Approach AI in ERP Software Today?

AI assistants need a standard way to call into your app to run a report, fetch status, create a ticket. Building a custom integration per vendor doesn't scale. The <a href="https://reliasoftware.com/blog/what-is-model-context-protocol" rel="dofollow">Model Context Protocol (MCP)</a> gives you one contract: tools, resources, and prompts that any MCP client can discover and call.&nbsp;
This guide is practical: you'll build a minimal MCP server and a React client that talks to it, using the stack you already use (Next.js, Vite + Express, or browser-only). By the end you'll have a runnable server, a React UI that invokes tools, and a clear path to production.
A small note that just keep using <a href="https://reliasoftware.com/blog/react-server-actions" rel="nofollow">React Server Actions</a> or your framework's data layer for normal user-facing data. MCP is for the AI-facing surface.
&gt;&gt;&nbsp;Read more: How to <a href="https://reliasoftware.com/blog/react-chatbot-ui" rel="dofollow">Build a React Chatbot UI with Vite, Tailwind, Shadcn</a>?
<h2>What You'll Build?</h2>
You'll build two pieces:
<ol>
<li>A minimal MCP-style server (Node): one HTTP server that exposes two tools (e.g. <code>get_status</code> and <code>run_report</code>). Each tool accepts arguments and returns a result in the usual MCP content shape. You run it once (e.g. on port 3001).</li>
<li>A React app that calls those tools: a button or form that sends a request (tool name + arguments) and displays the response. How the request gets to the server depends on the method:
<ul>
<li>Method 1 (Next.js): React calls a Next.js API route; the route proxies the request to the MCP server.</li>
<li>Method 2 (Vite + Express): React (Vite) calls an Express server; Express proxies to the MCP server.</li>
<li>Method 3 (Browser-only): React calls the MCP server directly over HTTP (CORS must allow your app origin).</li>
</ul>
</li>
</ol>
The server we build is minimal on purpose: it uses a simple HTTP JSON API that mirrors the MCP tool response shape for learning. It is not a full MCP server (real MCP uses JSON-RPC 2.0 over stdio or HTTP). When you're ready, swap it for a full <a href="https://github.com/modelcontextprotocol/sdk">MCP SDK</a> server and keep the same client-side pattern.
&gt;&gt;&nbsp;Explore further: How to <a href="https://reliasoftware.com/blog/nodejs-chatbot-ai">Build a Node.js Chatbot Backend with Express and OpenAI</a>?
<h2>Prerequisites</h2>
<ul>
<li>Node.js 18+ and npm (or pnpm).</li>
<li>Basic React (components, <code>useState</code>, <code>fetch</code>).</li>
<li>One of: Next.js (App Router), Vite + Express, or a plain <a href="https://reliasoftware.com/blog/how-to-create-react-app-using-vite">Vite React app</a> for the browser-only method.</li>
</ul>
&gt;&gt;&nbsp;You might need: A Full Guide to <a href="https://reliasoftware.com/blog/migrate-react-router-to-tanstack-router">Migrate from React Router to TanStack Router</a>
<h2>Build the Minimal MCP Server</h2>
Create a small Node project that exposes two tools over HTTP. The response shape mirrors the MCP tool result format (<code>{ content: [{ type: "text", text: "&hellip;" }] }</code>)&mdash;useful for learning, though real MCP clients use JSON-RPC 2.0 (see the SDK link below).
<h3>Step 1:&nbsp;Create the project</h3>
<pre class="language-javascript"><code>mkdir mcp-server &amp;&amp; cd mcp-server
npm init -y
npm install express cors</code></pre>
<h3>Step 2: Add the server file</h3>
Create <code>server.mjs</code> (we use the <code>.mjs</code> extension so Node treats it as an ES module; if you prefer <code>server.js</code>, add <code>"type": "module"</code> to your <code>package.json</code>):
<pre class="language-javascript"><code>import express from "express";
import cors from "cors";

const app = express();
app.use(cors());
app.use(express.json());

const tools = {
 get_status() {
 return { content: [{ type: "text", text: "OK &ndash; server is running. Time: " + new Date().toISOString() }] };
 },
 run_report({ scope = "week" } = {}) {
 const report = `Report (${scope}): generated at ${new Date().toISOString()}. Add your real logic here.`;
 return { content: [{ type: "text", text: report }] };
 },
};

app.post("/mcp/tools", (req, res) =&gt; {
 const { name, arguments: args } = req.body || {};
 const fn = tools[name];
 if (!fn) {
 return res.status(400).json({ error: "Unknown tool", content: [{ type: "text", text: "Unknown tool: " + name }] });
 }
 try {
 const result = tools[name](args || {});
 res.json(result);
 } catch (e) {
 res.status(500).json({ content: [{ type: "text", text: "Error: " + e.message }] });
 }
});

const PORT = 3001;
app.listen(PORT, () =&gt; console.log("MCP server on &lt;http://localhost&gt;:" + PORT));</code></pre>
<h3>Step 3: Run it</h3>
<pre class="language-javascript"><code>node server.mjs</code></pre>
Leave it running. You'll point your React app (or proxy) at <code>http://localhost:3001/mcp/tools</code> with POST body <code>{ "name": "get_status", "arguments": {} }</code> or <code>{ "name": "run_report", "arguments": { "scope": "week" } }</code>.
<blockquote>
Pro Tip: For production you'd use the official MCP SDK (stdio or HTTP transport) and register tools with proper schemas. This minimal server keeps the tutorial focused on the React side.
</blockquote>
<h2>3 Methods to Integrate MCP Tools into a React UI</h2>
<h3>Method 1: Next.js (API Route Proxy)</h3>
Most <a href="https://reliasoftware.com/blog/what-is-next-js">React apps use Next.js</a>. Here the browser never talks to the MCP server directly; it calls a Next.js API route that forwards the request.
1. Create the Next app
<pre class="language-javascript"><code>npx create-next-app@latest my-mcp-app --typescript --app --no-src-dir
cd my-mcp-app</code></pre>
2. Add the API route
Create <code>app/api/mcp/tools/route.ts</code>:
<pre class="language-javascript"><code>import { NextRequest, NextResponse } from "next/server";

const MCP_SERVER = process.env.MCP_SERVER_URL ?? "&lt;http://localhost:3001&gt;";

export async function POST(request: NextRequest) {
 const body = await request.json();
 try {
 const res = await fetch(`${MCP_SERVER}/mcp/tools`, {
 method: "POST",
 headers: { "Content-Type": "application/json" },
 body: JSON.stringify(body),
 });
 if (!res.ok) {
 return NextResponse.json({ error: "MCP server error" }, { status: res.status });
 }
 const data = await res.json();
 return NextResponse.json(data);
 } catch {
 return NextResponse.json({ error: "MCP server unreachable" }, { status: 502 });
 }
}</code></pre>
3. Add a page that calls the route
In <code>app/page.tsx</code> (or a new page):
<pre class="language-javascript"><code>"use client";

import { useState } from "react";

type MCPResult = { content?: Array&lt;{ type: string; text?: string }&gt; };

async function callMCPTool(name: string, args: Record&lt;string, unknown&gt; = {}): Promise&lt;string&gt; {
 const res = await fetch("/api/mcp/tools", {
 method: "POST",
 headers: { "Content-Type": "application/json" },
 body: JSON.stringify({ name, arguments: args }),
 });
 if (!res.ok) throw new Error("MCP call failed");
 const data: MCPResult = await res.json();
 return data.content?.[0]?.text ?? "";
}

export default function Home() {
 const [result, setResult] = useState&lt;string | null&gt;(null);
 const [loading, setLoading] = useState(false);

 const handleGetStatus = async () =&gt; {
 setLoading(true);
 try {
 const text = await callMCPTool("get_status");
 setResult(text);
 } finally {
 setLoading(false);
 }
 };

 const handleRunReport = async () =&gt; {
 setLoading(true);
 try {
 const text = await callMCPTool("run_report", { scope: "week" });
 setResult(text);
 } finally {
 setLoading(false);
 }
 };

 return (
 &lt;main style={{ padding: "2rem" }}&gt;
 &lt;h1&gt;React + MCP (Next.js)&lt;/h1&gt;
 &lt;div style={{ display: "flex", gap: "0.5rem", marginBottom: "1rem" }}&gt;
 &lt;button type="button" onClick={handleGetStatus} disabled={loading}&gt;
 Get status
 &lt;/button&gt;
 &lt;button type="button" onClick={handleRunReport} disabled={loading}&gt;
 Run report
 &lt;/button&gt;
 &lt;/div&gt;
 {result != null &amp;&amp; &lt;pre style={{ background: "#f4f4f4", padding: "1rem" }}&gt;{result}&lt;/pre&gt;}
 &lt;/main&gt;
 );
}</code></pre>
4. Run both
<ul>
<li>Terminal 1: <code>node server.mjs</code> (from the MCP server folder).</li>
<li>Terminal 2: <code>npm run dev</code> in the Next app. Set <code>MCP_SERVER_URL=http://localhost:3001</code> if the server is not on the same host.</li>
</ul>
So: the browser only sees <code>/api/mcp/tools</code>. Your Next.js backend owns auth, rate limiting, and logging before calling the MCP server.
<h3>Method&nbsp;2: Vite + Express (Express Proxy)</h3>
If your stack is Vite for the frontend and Express for the backend, run the React app and Express on different ports; Express proxies to the MCP server.
1. Create Vite React app
<pre class="language-javascript"><code>npm create vite@latest my-mcp-client -- --template react-ts
cd my-mcp-client
npm install</code></pre>
2. Install proxy dependencies and create the Express proxy
In the same repo or a sibling folder, install Express and CORS for the proxy, then add a small server file (e.g. <code>server.mjs</code> in the project root or in a <code>server/</code> folder):

<pre class="language-javascript"><code>npm install express cors</code></pre>
<pre class="language-javascript"><code>import express from "express";
import cors from "cors";

const app = express();
app.use(cors({ origin: "&lt;http://localhost:5173&gt;" }));
app.use(express.json());

const MCP_SERVER = process.env.MCP_SERVER_URL ?? "&lt;http://localhost:3001&gt;";

app.post("/api/mcp/tools", async (req, res) =&gt; {
 try {
 const r = await fetch(`${MCP_SERVER}/mcp/tools`, {
 method: "POST",
 headers: { "Content-Type": "application/json" },
 body: JSON.stringify(req.body),
 });
 if (!r.ok) return res.status(r.status).json({ error: "MCP server error" });
 const data = await r.json();
 res.json(data);
 } catch {
 res.status(502).json({ error: "MCP server unreachable" });
 }
});

app.listen(3002, () =&gt; console.log("Proxy on &lt;http://localhost:3002&gt;"));</code></pre>
3. <a href="https://reliasoftware.com/blog/react-component-library">React component</a>
In <code>src/App.tsx</code>, reuse the same component markup from method 1&mdash; only <code>callMCPTool</code> changes (point it to the Express proxy on port 3002):

<pre class="language-javascript"><code>const API = "&lt;http://localhost:3002/api/mcp/tools&gt;";

async function callMCPTool(name: string, args: Record&lt;string, unknown&gt; = {}): Promise&lt;string&gt; {
 const res = await fetch(API, {
 method: "POST",
 headers: { "Content-Type": "application/json" },
 body: JSON.stringify({ name, arguments: args }),
 });
 if (!res.ok) throw new Error("MCP call failed");
 const data = await res.json();
 return data.content?.[0]?.text ?? "";
}</code></pre>
Run the MCP server on 3001, the proxy on 3002, and Vite on 5173. The React app only talks to 3002; Express adds a single place for auth and rate limiting.
<h3>Method 3: Browser-Only (Direct HTTP + CORS)</h3>
For local dev or demos, React can call the MCP server directly if the server allows your app's origin. The minimal server in the previous section already uses <code>cors()</code> with no origin restriction, so it will accept requests from any origin (suitable only for development).
1. Use the same MCP server (with <code>cors()</code> enabled).
2. Create a Vite React app (or any React app) and call the server URL:
<pre class="language-javascript"><code>const MCP_URL = "&lt;http://localhost:3001&gt;";

async function callMCPTool(name: string, args: Record&lt;string, unknown&gt; = {}): Promise&lt;string&gt; {
 const res = await fetch(`${MCP_URL}/mcp/tools`, {
 method: "POST",
 headers: { "Content-Type": "application/json" },
 body: JSON.stringify({ name, arguments: args }),
 });
 if (!res.ok) throw new Error("MCP call failed");
 const data = await res.json();
 return data.content?.[0]?.text ?? "";
}</code></pre>
3. Run the server and the app. Open the app in the browser (e.g. <code>http://localhost:5173</code>). Buttons that call <code>callMCPTool("get_status")</code> and <code>callMCPTool("run_report", { scope: "week" })</code> will work as long as the server is on 3001 and CORS allows the page origin.
<blockquote>
Warning: Do not expose this setup to the internet without auth and a strict CORS policy. For production, use method 1 or 2 so the browser never talks to the MCP server directly.
</blockquote>
<h2>When to Use vs Not to Use MCP with React?</h2>
When to use with React:
<ul>
<li>You want Cursor, Copilot, or another client to trigger actions or read data from your app.</li>
<li>You prefer one standard integration instead of custom plugins per vendor.</li>
<li>You're building internal tools or dashboards that should be AI-friendly (e.g. "create ticket", "run report" as tools).</li>
</ul>
When not to: Your app is purely user-facing with no need for AI to call back, or you only call external AI APIs from React, use normal fetch or an SDK for that. MCP is for the AI-facing surface, not for replacing React Server Actions or your data layer.
<h2>FAQs</h2>
How is MCP different from a REST API?
A REST API is designed for app-to-app communication with custom endpoints you define. MCP is a standardised protocol that lets AI systems discover and call your tools automatically, one integration instead of one custom endpoint per vendor. See the&nbsp;<a href="https://modelcontextprotocol.io/">official MCP documentation</a> and the <a href="https://github.com/modelcontextprotocol">MCP SDK on GitHub</a> for the full spec.
Why use MCP with React?
So AI assistants can use your app's data and actions through one standard protocol instead of custom integrations per vendor.
Does React run the MCP server?
Usually not. The MCP server runs in Node or your backend. Your React app calls your own API (Next.js or Express), which then talks to the MCP server. That keeps auth and security in one place.
Is MCP production-ready?
The protocol is used in production by several vendors. Your implementation (auth, rate limiting, CORS) must be production-ready; the protocol does not enforce that for you.
How do I secure MCP in production?
Authenticate callers, use a backend proxy so the browser never talks to the MCP server directly, add rate limiting, and log tool calls for audit.
&gt;&gt;&nbsp;Read more: <a href="https://reliasoftware.com/blog/react-production-ready-ai-chatbot">Build A Production-Ready React AI Chatbot with Streaming, RAG, &amp; More</a>
Can I use MCP in the browser?
Yes, if the server is exposed over HTTP and CORS allows your origin. For production, prefer a backend proxy (method 1 or 2).
Does MCP replace my React data fetching?
No. MCP is for AI systems to call into your app. Use your normal data fetching (Server Actions, <a href="https://reliasoftware.com/blog/graphql-with-react">GraphQL</a>, fetch) for user-driven flows.
Why do tool descriptions matter?
LLMs use the tool's schema and description when deciding what to call. Clear descriptions and actionable error messages improve reliability.

<h2>Conclusion</h2>
You built a minimal MCP-style server and a React client that calls it via three patterns: Next.js API route proxy, Vite + Express proxy, or browser-only HTTP. Use the method that matches your stack; in production, use a proxy (Next.js or Express) and add auth, CORS, and rate limiting. Start with one or two tools, validate the flow, then expand.
Next Steps:
<ul>
<li>Swap to the official MCP SDK for full JSON-RPC 2.0 support, tool schemas, and resources/prompts.</li>
<li>Add SSE or streaming transport so tools can push progress updates to the client.</li>
<li>Register tools with JSON Schema descriptions so LLMs can discover and choose tools automatically.</li>
</ul>
<div id="article_content" class="inner_article__entries__X3PKd">
&gt;&gt;&gt; Follow and Contact&nbsp;<a href="https://reliasoftware.com/">Relia Software</a> for more information!
</div>

Learn how to build a minimal MCP server and a React client that talks to it, using Next.js, Vite + Express, or browser-only with a clear path to production.

How to Build an MCP Server and Integrate it with a React App?

 Build an MCP Server and Integrate it with a React App

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React MCP Guide: Build an MCP Server and Connect It to a React UI

Virtuoso, Testim, Mabl, Functionize, AcceIQ, testRigor, LambdaTest AI Agents, Testsigma, Diffblue, etc, are leading AI agents in software testing recently.

Top 12 Leading AI Agents in Software Testing For Businesses

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Top 12 Best AI Agents in Software Testing For Businesses

<a href="https://reliasoftware.com/blog/what-is-agentic-ai">AI agent</a> is a system that can observe what is happening, make decisions, and take actions on its own instead of following fixed instructions. Because of this, AI agents work well in software testing, where applications change often and need flexible, adaptive testing. They help developers and businesses test faster, cover more real user scenarios, and reduce the time spent writing and maintaining test scripts.
In this article, you&rsquo;ll learn how AI agents in software testing work, the roles they play in modern QA, and best practices for using them effectively. Reading this guide will help you understand when AI agents make sense for your <a href="https://reliasoftware.com/blog/software-testing-life-cycle">software testing process</a> and how they can improve software quality and delivery speed.
&gt;&gt; Read more:&nbsp;
<ul>
<li dir="ltr" style="font-style: italic;" role="presentation">Top 6 Open-source <a href="https://reliasoftware.com/blog/ai-agent-frameworks">AI Agent Frameworks</a></li>
<li dir="ltr" style="font-style: italic;" role="presentation">7 Best <a href="https://reliasoftware.com/blog/golang-ai-agent-frameworks">Golang AI Agent Frameworks</a> You Should Know</li>
</ul>
<h2 dir="ltr" style="text-align: left;">What are AI Agents in Software Testing?</h2>
AI agents in software testing are systems that can automatically determine what to test and execute those tests within an application or software without fixed testing scripts. They act as real testers who analyze the app, create and run test cases, and improve their actions based on past results. They often use Natural Language Processing (NLP) and generative AI to understand text, user flows, and create new test scenarios.
<a href="https://reliasoftware.com/blog/top-automation-testing-tools">Traditional automation testing tools</a> depend on fixed scripts that often break when the app changes, such as when a button moves or a field name is updated. Meanwhile, AI agents use patterns, text, and visual clues to understand what they are testing, so they can still continue their work if the layout changes. This helps QA teams test faster, discover new issues, and keep up with apps that change frequently.
&gt;&gt; Read more: What is <a href="https://reliasoftware.com/blog/automation-testing-functions-tools-cost-and-benefits">Automation Testing</a> in Software Testing?
<h2 dir="ltr" style="text-align: left;">Roles of AI Agent in Software Testing</h2>
<h3 dir="ltr" style="text-align: left;">Automated <a href="https://reliasoftware.com/blog/test-case-design-techniques">Test Case Generation</a></h3>
AI agents can create test cases by using the app like a real user. They click through screens, fill in forms, and try different actions to see how the system works, then turn those actions into test cases. This helps QA teams cover more user flows and edge cases without writing every test manually, and also keeps tests easier to update when the app changes.
<h3 dir="ltr" style="text-align: left;">Intelligent Test Execution</h3>
AI agents execute tests more smartly and efficiently than traditional automation tools. They can run tests 24/7, in parallel, and at any time without manual triggers. Based on recent code changes, past failures, or risk levels, the agent can decide which tests should run first to ensure critical areas are validated early.
This approach helps QA teams shorten test cycles and get faster feedback from each build. Instead of running every test blindly, AI agents focus execution on the most important scenarios, improving reliability while saving time and resources.
<h3 dir="ltr" style="text-align: left;">Adaptive Test Scripts</h3>
Adaptive test scripts, also known as self-healing test scripts, allow AI agents to keep tests working even when the application changes. When a button moves, a field is renamed, or the UI structure is updated, the agent can detect these changes using visual and text clues and automatically adjust the test to match the new structure.&nbsp;
Unlike traditional scripts that break easily, self-healing scripts reduce maintenance effort in fast-moving, agile environments. This helps QA teams spend less time fixing broken tests and more time focusing on improving overall software quality.
&gt;&gt; Read more: How to Write A Powerful <a href="https://reliasoftware.com/blog/test-plan-in-software-testing">Test Plan in Software Testing</a>?
<h3 dir="ltr" style="text-align: left;">Shift-Left Testing</h3>
Shift-left testing is a <a href="https://reliasoftware.com/blog/levels-of-testing-in-software-testing">testing approach</a> where quality checks are done earlier in <a href="https://reliasoftware.com/blog/software-development-life-cycle-sdlc">the software development process</a> instead of waiting until the end. By getting involved from the start, AI agents can review APIs, requirements, or early builds to create and run tests before the full user interface is ready. This helps teams find problems sooner, reduce rework, and keep the product more stable as it grows.
&gt;&gt; Read more:
<ul>
<li dir="ltr" role="presentation">What is <a href="https://reliasoftware.com/blog/what-is-unit-testing">Unit Testing</a>? Definition, Benefits &amp; Process</li>
<li dir="ltr" role="presentation">What is&nbsp;<a href="https://reliasoftware.com/blog/integration-testing">Integration Testing</a>? Example, Approaches, Process</li>
<li>Mastering <a href="https://reliasoftware.com/blog/functional-testing">Functional Testing</a> for Delivering Bug-Free Software</li>
</ul>
<h3 dir="ltr" style="text-align: left;">Visual Testing</h3>
Visual testing checks how an application looks on the screen to make sure the layout, text, and buttons appear correctly. Instead of comparing pixels, AI agents review the screen in a more human way, allowing them to spot issues such as missing content or broken layouts even when small design changes occur.
AI agents can also compare screenshots across different devices, screen sizes, and resolutions to ensure a consistent user experience. This helps QA teams quickly detect UI differences and visual problems without being distracted by minor or expected design updates.
<h3 dir="ltr" style="text-align: left;">Self-Learning and Predictive Analysis</h3>
AI agents improve as they run more tests and collect more results. They learn which parts of the app tend to break and which ones usually work fine, then use that knowledge to decide what to test next. Based on past results, they can focus on risky areas, remove redundant tests, and spot problems earlier before they affect users.
<h3 dir="ltr" style="text-align: left;">Supporting Performance &amp; <a href="https://reliasoftware.com/blog/security-testing-in-software-testing">Security Testing</a></h3>
AI agents can support performance and security testing by running tests under heavy load and risky conditions. They generate different types of traffic, try unusual inputs, and monitor how the application responds. This helps teams find performance issues and security weaknesses that normal tests might not catch.
&gt;&gt; Read more:&nbsp;
<ul>
<li dir="ltr" style="font-style: italic;" role="presentation">Top 7 <a href="https://reliasoftware.com/blog/web-app-security-testing-tools">Web App Security Testing Tools</a> For Developers</li>
<li dir="ltr" style="font-style: italic;" role="presentation">7 Leading <a href="https://reliasoftware.com/blog/performance-testing-tools">Performance Testing Tools</a> for Developers</li>
</ul>
<figure class="image align-center"><img title="Roles of AI Agent in Software Testing" src="https://api.reliasoftware.com/uploads/roles_of_ai_agents_in_software_testing_7bd5b6ebe1.webp" alt="roles of ai agents in software testing" width="1000" height="600">
<figcaption>Roles of AI Agent in Software Testing</figcaption>
</figure>
<h2 dir="ltr" style="text-align: left;">Types of AI Agents in Software Testing</h2>
<h3 dir="ltr" style="text-align: left;">Simple Reflex Agents</h3>
Simple reflex agents act based on fixed rules by checking what is happening on the screen and responding with a set action, such as confirming a button is visible or an error message appears. These agents are useful for basic checks, but they do not remember past actions or learn from experience.
<h3 dir="ltr" style="text-align: left;">Model-Based Reflex Agents</h3>
Model-based reflex agents keep track of both current inputs and past experiences, allowing them to handle more complex scenarios. They use this information to understand how pages and actions are connected, which helps them test complete user journeys such as moving from login to checkout.
<h3 dir="ltr" style="text-align: left;">Goal-Based Agents</h3>
Goal-based agents work toward a specific target, such as completing a signup, placing an order, or reaching a certain page. They choose actions based on what will move them closer to that goal, which makes them useful for testing full user flows and making sure important tasks in the app work from start to finish.
<h3 dir="ltr" style="text-align: left;">Utility-Based Agents</h3>
Utility-based agents choose actions based on how useful each option is. They compare different paths in the application and pick the one that gives the best result, such as higher test coverage or lower risk. This helps them focus on the most valuable areas to test instead of just following one fixed route.
<h3 dir="ltr" style="text-align: left;">Learning Agents</h3>
Learning agents are AI agents that improve by learning from past tests. They track what fails and what stays stable, then adjust how they test based on that experience. Over time, this helps them make better testing decisions and work more effectively.
<figure class="image align-center"><img title="Types of AI Agents in Software Testing" src="https://api.reliasoftware.com/uploads/types_of_ai_agents_in_software_testing_a85f664df6.webp" alt="types of ai agents in software testing" width="1000" height="600">
<figcaption>Types of AI Agents in Software Testing</figcaption>
</figure>
<h2 dir="ltr" style="text-align: left;">Advantages Of AI Agents in Software Testing</h2>
<ul>
<li dir="ltr" role="presentation">Faster release cycles: AI agents automate and adapt test execution, helping teams shorten testing phases and deliver software more quickly.</li>
<li dir="ltr" role="presentation">Broader coverage: It can explore a wider range of scenarios and edge cases that human testers might miss.</li>
<li dir="ltr" role="presentation">Improved accuracy: Consistently follow testing rules and analyze results carefully, helping reduce human errors and false test failures.</li>
<li dir="ltr" role="presentation">Lower costs: Automating repetitive tasks and finding bugs earlier in the process leads to significant cost savings.</li>
<li dir="ltr" role="presentation">Better use of QA resources: AI agents allow QA teams avoid wasted time and effort on repetitive work and fixing tests, and more time on quality planning and confident releases.</li>
</ul>
<h2 dir="ltr" style="text-align: left;">Best Practices for Implementing AI Agents in Software Testing</h2>
Start Small First
You should start by using AI agents on a small set of tests or in test environments instead of applying them to the whole app at once. This helps teams see how the agents work, manage risks, and avoid issues in production. Once the results are reliable, the agents can be expanded to more parts of the application.
Prioritize Data Quality
AI agents depend on data to work, so data quality is very important. Teams should ensure to use clean, realistic test data that reflects real user behavior and common scenariosto train the agents effectively from day one. Good data helps AI agents make better testing decisions, find real issues, and produce more reliable results from the start.
Set Clear Boundaries and Rules
Teams should clearly specify which parts of the application the AI agent can access, what actions it is allowed to perform, and when it should stop testing. These boundaries help keep testing safe and aligned with business goals.
Monitor Agent Behavior
It is important for teams to monitor both test results and how the AI agent behaves during testing, including its actions and decisions. Regular review and adjustment help ensure the agent stays focused on high-risk areas and continues to deliver reliable value as the application changes.
Combine AI Agents with Human Oversight
AI agents are effective at running and exploring tests, but human review is still essential. QA teams should review critical test flows and important findings to ensure results make sense and meet quality expectations.
Choose the Right Tools
Consider using platforms with pre-built AI agents, such as those available from LambdaTest or ACCELQ, rather than building from scratch. These tools help teams get started faster, reduce setup effort, and fit more easily into existing testing workflows, allowing QA teams to focus on improving test quality instead of building and maintaining AI systems.
<h2 dir="ltr" style="text-align: left;">Challenges of AI Agents in Software Testing</h2>
<ul>
<li dir="ltr" role="presentation">Dependence on data quality: Inaccurate or insufficient data can limit the effectiveness of AI-driven testing decisions.</li>
<li dir="ltr" role="presentation">Reduced predictability: Non-deterministic behavior may make test outcomes harder to reproduce and audit.</li>
<li dir="ltr" role="presentation">Limited transparency: Understanding how an AI agent reached a decision can be challenging, which may affect trust and compliance.</li>
<li dir="ltr" role="presentation">Governance and security concerns: Enterprises must ensure proper controls to prevent unintended actions, data leakage, or compliance issues.</li>
</ul>
<h2 dir="ltr" style="text-align: left;">FAQs</h2>
1. Can AI agents replace human QA testers?
No. AI agents help QA teams but do not replace people. They handle repetitive and large-scale testing, while humans are still needed for test planning, business logic checks, and decision-making.
&gt;&gt; Read more: <a href="https://reliasoftware.com/blog/will-ai-replace-software-engineers">Will AI Replace Software Engineers Altogether</a>?
2. How do teams check if AI agent results are reliable?
Teams review test logs, compare results across different runs, and manually check important test flows. Clear rules and human review are often used for critical areas.
3. Do AI agents work with <a href="https://reliasoftware.com/blog/5-best-practices-for-enhancing-ci-cd-pipeline-security">CI/CD pipelines</a>?
Yes. AI agents can be added to CI/CD pipelines to run tests on new builds, focus on risky changes, and give faster feedback during development.
4. Are AI agents safe to use in enterprise or regulated environments?
They can be, if proper controls are in place. Many enterprises use clear rules, audit logs, and human oversight to meet security and compliance needs.
5. What is the difference between using AI to help testing and testing an AI agent?
Using AI to help with testing means using AI features to test a normal application. Testing an AI agent means checking how the agent itself makes decisions, learns, and reacts to different situations over time.
6. What is the difference between <a href="https://reliasoftware.com/blog/ai-in-software-testing">AI in software testing</a> and AI agents in software testing?
AI in software testing means using AI to assist certain testing tasks while people still control the overall process. AI agents in software testing go a step further by acting like testers themselves, deciding what to test, taking actions on their own, and learning from past test runs.
<h2 dir="ltr" style="text-align: left;">Conclusion</h2>
AI agents in software testing are becoming an important part of modern QA as applications grow more complex and change more quickly. They help development teams conduct tests more efficiently, reduce maintenance effort, and improve overall test coverage.
Using AI agents in software testing is not about replacing human testers, but about supporting developers and businesses with smarter and more flexible testing approaches. When applied with clear rules, oversight, and best practices, AI agents can significantly improve software quality and help teams deliver reliable products faster.
&gt;&gt;&gt; Follow and Contact <a href="https://reliasoftware.com/">Relia Software</a> for more information!

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The <a href="https://reliasoftware.com/blog/what-is-model-context-protocol" target="_blank" rel="noopener noreferrer">Model Context Protocol</a> (MCP) represents a significant advancement in AI assistant integration. The Go implementation of the Model Context Protocol (MCP) provides developers with a robust, performant, and type-safe solution for building MCP-compatible applications. This guide reviews the technical architecture, implementation details, and best practices for Golang MCP, helping both implementers and decision-makers understand its value.<h2>Why Go for MCP?</h2>Go is a natural fit for MCP because it offers:<ul><li>Type safety for request and response handling.</li><li>Efficient <a href="https://reliasoftware.com/blog/concurrency-in-golang" target="_blank" rel="noopener noreferrer">concurrency with goroutines</a>, ideal for multiple client connections.</li><li>Strong built-in support for <a href="https://reliasoftware.com/blog/build-a-golang-http-client-with-net-http" target="_blank" rel="noopener noreferrer">HTTP requests</a>, JSON, and streaming.</li><li>Easy deployment across platforms, making it suitable for production-ready AI applications.</li></ul>With the MCP Go SDK, developers get everything needed for creating protocol servers that integrate smoothly with Large Language Models (LLM applications) like Claude or GPT.<h2>Core Architecture and Design Principles</h2>The server implementation follows a client-server pattern. MCP clients, such as Claude Desktop, connect to Go servers hosting protocol servers. Communication is handled through JSON-RPC 2.0, ensuring compatibility with other languages and environments.Key design elements include:<ul><li>Tools defined with <code>mcp.NewTool</code>, <code>mcp.WithDescription</code>, <code>mcp.WithString</code>, and <code>mcp.Required</code>.</li><li>Tool handler functions that validate input, return structured output, and manage Server error cases.</li><li>Transport support, including <code>StdioTransport</code>, WebSockets, and Server-Sent Events.</li><li>Use of jsonschema for schema validation and type safety.</li></ul>This combination provides a secure, flexible way to expose external tools to AI applications.<h2>Technical Components</h2><h3>Server Implementation</h3>The Golang MCP server handles:<ul><li>Protocol negotiation and message routing</li><li>Capability registry (tools/resources)</li><li><a href="https://reliasoftware.com/blog/golang-jwt-authentication" target="_blank" rel="noopener noreferrer">Security and authentication layers</a></li><li>Concurrent client connections via goroutines</li></ul>Its modular design allows developers to extend functionality with custom tools or resource providers.<h3>Client Library</h3>The client library supports:<ul><li>Connection management and retries</li><li>Capability discovery</li><li>Sync/async modes</li><li>Error handling and connection pooling</li></ul>This ensures reliable integration even in distributed systems with unstable networks.<h3>Tools and Resources</h3><ul><li>Tools = executable functions invoked by AI assistants.</li><li>Resources = data repositories or APIs queried for information.</li></ul>Go adds:<ul><li>Type-safe schemas checked at compile time</li><li>Automatic caching and concurrency controls</li><li>Streaming support for large datasets</li></ul><h2>Installation and Setup Procedures</h2><h3>Environment Prerequisites</h3>Setting up a golang MCP development environment requires <a href="https://reliasoftware.com/blog/functions-in-go-1-21-release" target="_blank" rel="noopener noreferrer">Go 1.21</a> or later, as the implementation leverages modern <a href="https://reliasoftware.com/blog/generics-in-golang" target="_blank" rel="noopener noreferrer">Go features including generics</a> and enhanced <a href="https://reliasoftware.com/blog/advanced-golang-error-handling-techniques" target="_blank" rel="noopener noreferrer">error handling capabilities</a>. The development environment should include standard Go tooling such as go mod for dependency management and go test for running the comprehensive test suite included with the MCP libraries.Additional prerequisites include proper configuration of GOPATH and GOROOT environment variables, though modern Go development typically relies on Go modules for dependency management. Developers should ensure their systems have adequate memory and processing resources, as MCP servers may handle multiple concurrent connections and resource-intensive operations.<h3>Installation Process</h3>The installation process for golang MCP begins with initializing a new Go module and importing the necessary MCP libraries. The primary packages include the server library for building MCP servers, the client library for creating MCP clients, and various utility packages for common operations. The installation uses standard Go module commands, ensuring consistent dependency management across different development environments.<pre><code class="language-typescript">go mod init my-mcp-project
go get github.com/mark3labs/mcp-go/mcp
go get github.com/mark3labs/mcp-go/server
go get github.com/mark3labs/mcp-go/client</code></pre>The installation process includes automatic dependency resolution for required packages such as JSON-RPC libraries, HTTP handling utilities, and logging frameworks. The Go module system ensures that all dependencies are properly versioned and compatible with the MCP implementation.<h3>Configuration and Initialization</h3>Configuration of golang MCP applications involves setting up server parameters, defining available tools and resources, and configuring security settings. The configuration typically uses structured configuration files in JSON or YAML format, though the Go implementation also supports programmatic configuration for dynamic scenarios.The initialization process includes server startup procedures, client connection establishment, and capability registration. The Go implementation provides comprehensive logging and monitoring capabilities, enabling developers to track server performance, client connections, and tool usage patterns. Proper initialization ensures that all components are ready to handle incoming requests and maintain consistent state across operations.<h2>MCP Server Implementation</h2>A basic MCP server implementation in Go demonstrates the fundamental patterns for creating MCP-compatible services. Here’s how to create a simple MCP server:<pre><code class="language-typescript">package main

import (
 "context"
 "log"
 "github.com/mark3labs/mcp-go/mcp"
 "github.com/mark3labs/mcp-go/server"
)

func main() {
// Create server instance
 srv := server.NewMCPServer("example-server", "1.0.0")

// Register a simple tool
 srv.RegisterTool(&amp;mcp.Tool{
 Name: "echo",
 Description: "Echo the input message",
 InputSchema: mcp.Schema{
 Type: "object",
 Properties: map[string]mcp.Schema{
 "message": {Type: "string"},
 },
 },
 }, handleEcho)

// Start serverif err := srv.Start(context.Background()); err != nil {
 log.Fatal(err)
 }
}

func handleEcho(ctx context.Context, arguments map[string]interface{}) (*mcp.CallToolResult, error) {
 message, ok := arguments["message"].(string)
 if !ok {
 return nil, fmt.Errorf("message must be a string")
 }

 return &amp;mcp.CallToolResult{
 Content: []mcp.Content{{
 Type: "text",
 Text: message,
 }},
 }, nil
}</code></pre>This basic implementation demonstrates proper error handling, type assertion, and result formatting according to MCP specifications. The server automatically handles protocol negotiation, capability advertisement, and message routing.<h2>Client Integration Patterns</h2>Client integration patterns in golang MCP focus on establishing reliable connections to MCP servers and efficiently utilizing available tools and resources. The client library provides both high-level convenience methods and low-level control for advanced use cases:<pre><code class="language-typescript">package main

import (
 "context"
 "fmt"
 "github.com/mark3labs/mcp-go/client"
)

func main() {
// Create client instance
 client := client.NewMCPClient()

// Connect to MCP server
 ctx := context.Background()
 if err := client.Connect(ctx, "stdio", "path/to/server"); err != nil {
 log.Fatal(err)
 }
 defer client.Close()

// List available tools
 tools, err := client.ListTools(ctx)
 if err != nil {
 log.Fatal(err)
 }

// Call a tool
 result, err := client.CallTool(ctx, "echo", map[string]interface{}{
 "message": "Hello, MCP!",
 })
 if err != nil {
 log.Fatal(err)
 }

 fmt.Printf("Tool result: %+v\n", result)
}</code></pre>The client implementation includes automatic retry logic, connection pooling, and comprehensive error handling to ensure reliable operation in production environments.<h2>Advanced Configuration and Customization</h2>Advanced golang MCP configurations support complex scenarios including custom transport protocols, authentication mechanisms, and resource providers. The framework's extensible architecture allows developers to implement custom components while maintaining compatibility with the MCP specification:<pre><code class="language-typescript">// Custom transport implementationtype CustomTransport struct {
// Transport-specific fields
}

func (t *CustomTransport) Send(ctx context.Context, message []byte) error {
// Custom transport logicreturn nil
}

func (t *CustomTransport) Receive(ctx context.Context) ([]byte, error) {
// Custom receive logicreturn nil, nil
}

// Custom resource providertype DatabaseResource struct {
 db *sql.DB
}

func (r *DatabaseResource) GetResource(ctx context.Context, uri string) (*mcp.Resource, error) {
// Database query logicreturn &amp;mcp.Resource{
 URI: uri,
 Content: []mcp.Content{{Type: "text", Text: "database result"}},
 }, nil
}</code></pre>These advanced patterns enable integration with existing systems, custom security requirements, and specialized data sources while maintaining MCP compatibility.<h2>Current Standards and Protocol Compliance</h2>The Go SDK (<code>mark3labs/mcp-go</code>) is designed to follow the MCP spec closely, ensuring reliable interoperability with other Model Context Protocol servers. Every request and response is validated against <code>jsonschema</code>, which provides type safety and prevents runtime errors when integrating with LLM applications.Developers can expect:<ul><li>Strict enforcement of <code>protocolVersion</code> for consistent communication</li><li>Support for schemas defined with <code>mcp.WithDescription</code>, <code>mcp.WithString</code>, and <code>mcp.Required</code></li><li>Clear error handling paths for <code>Server error</code> responses</li><li>Compatibility tests to confirm integration with official clients such as the Claude Desktop app</li></ul>Security practices in Go MCP servers typically include TLS encryption, <code>API_KEY</code> authentication, and optional OAuth. Logs and metrics can be exported to monitoring systems, helping teams maintain compliance with enterprise standards.<h2>Integration with AI Assistants and Tools</h2>The Go implementation makes it easy to connect AI models to real-world services. A developer can expose a REST API as a tool by wrapping it in a tool handler, defining the input/output schema, and registering it with <code>NewMCPServer</code>. Assistants like Claude can then call it through a tool call without needing to know the underlying API.Example use cases include:<ul><li>Running HTTP requests to company APIs</li><li>Querying internal databases with safe schemas</li><li>Providing structured access to files and documents through URI schemes and <code>URITemplate</code></li><li>Building custom automation pipelines that combine multiple external tools</li></ul>The MCP client supports testing integrations locally, while config files such as <code>mcp.json</code> or <code>claude_desktop_config.json</code> make tools discoverable by AI applications. This allows teams to deploy a single Go MCP server and reuse it across different assistants.<h2>Future Developments and Roadmap</h2>The Go MCP ecosystem is actively evolving alongside the MCP protocol. Current development discussions include:<ul><li>Support for Server-Sent Events for real-time streaming of tool outputs</li><li>Stronger validation of output schema definitions to prevent misuse in LLM applications</li><li>Extended Transport Options beyond stdio, including <a href="https://reliasoftware.com/blog/golang-grpc" target="_blank" rel="noopener noreferrer">gRPC for high-throughput services</a></li><li>Better helper utilities in the official SDK to simplify tool registration and schema generation</li></ul>Community activity around <code>mark3labs/mcp-go</code> and <code>github.com/metoro-io/mcp-golang</code> continues to grow. Developers regularly contribute Go libraries, simple examples, and integration guides. As the protocol matures, these contributions will make it easier for newcomers to adopt Go MCP in both experimental and production-grade environments.<h2>Conclusion</h2>The Go implementation of the Model Context Protocol offers developers a practical, scalable, and secure foundation for integrating Large Language Models with external data sources. By using <code>mark3labs/mcp-go</code> and the MCP Go SDK, teams can define tools with <code>mcp.NewTool</code>, register them on a server instance, and expose them to assistants like Claude in a standardized way.For Software Engineers building new AI-driven systems, this means:<ul><li>Rapid prototyping with a simple MCP server using <code>go.mod</code> and a few lines of code</li><li>Robust deployments with structured schemas, authentication, and monitoring</li><li>Flexibility to connect both internal and public APIs for AI applications</li></ul>As the MCP ecosystem expands, Go will remain one of the best languages for building MCP servers, thanks to its concurrency model, simplicity, and production readiness.&gt;&gt;&gt; Follow and Contact&nbsp;<a href="https://reliasoftware.com/">Relia Software</a> for more information!

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That's why we're your one-stop shop for web app development. Whether you need a custom application or a streamlined web presence, we have the expertise to deliver.&nbsp;
Don't settle for a website gathering dust &ndash; Let Relia turn your vision into a vibrant digital space, converting browsers into brand loyalists.

We build powerful web apps that connect you with customers, drive results, and launch your digital strategy into overdrive.

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Web App Development

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Custom Web App Development Services

Relia Software is the top web app development company. We offer great services for building robust web applications. Check out for our detailed services here!