Card memory game, currency converter, charity finder, micro-learning platform, smart recipe planner, etc, are popular mobile app ideas for developers.

15 Best Practical Mobile App Ideas for Businesses in 2026

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Top 15 Useful Mobile App Ideas for Businesses 2026

Modern Go applications handle many operations at the same time like HTTP requests, background tasks, database queries, and external services. Because of this, it is important to control how long each task runs and to stop work that is no longer needed.
However, without a shared approach, cancellation and time limits can be handled in its own way, then the code becomes harder to manage and wastes resources. Thus, Go provides a standard mechanism to handle these concerns across function boundaries by Golang Context package.
This blog walks through how context is used in real Go code, with practical examples for HTTP, database queries, and background work. Continue reading to see common patterns and mistakes you should avoid when building Go services.
<h2>What is Context in Golang?</h2>
Go&rsquo;s <code>context</code> package is a standard mechanism for controlling cancellation, timeouts, and request-scoped data across goroutines in modern Go services, especially around <a href="https://reliasoftware.com/blog/build-a-golang-http-client-with-net-http">HTTP requests</a> and database operations. It provides a single, consistent way to signal when work should stop and to limit how long operations are allowed to run.
Without golang context, each library would roll its own cancellation solution, leading to fragmented patterns, harder debugging, and subtle resource leaks. By standardizing on <code>context.Context</code>, Go makes cooperative cancellation and time‑bounded operations part of the language&rsquo;s idioms for HTTP request handling and database access.
Example (no context, anti‑pattern):
<pre class="language-go"><code>func handle(w http.ResponseWriter, r *http.Request) {
 // No context usage, work keeps running even if client disconnects.
 time.Sleep(10 * time.Second)
 w.Write([]byte("done"))
}
</code></pre>
This handler will keep sleeping even if the client closes the connection, wasting resources.
<h2>Understanding the Context Interface</h2>
The Context interface exposes Deadline, Done, Err, and Value, giving enough information to coordinate cancellation and deadlines. It is safe for concurrent use and is designed to be passed down call chains rather than stored for later.
You almost never implement Context yourself, instead, you use the factory functions (WithCancel, WithTimeout, WithDeadline, WithValue) provided by the context package. This keeps behavior consistent and interoperable across different libraries and frameworks.
Example: inspecting ctx in a helper function
<pre class="language-go"><code>func doWork(ctx context.Context) error {
 if deadline, ok := ctx.Deadline(); ok {
 log.Printf("Deadline set at: %v", deadline)
 } else {
 log.Println("No deadline set")
 }

 select {
 case &lt;-time.After(2 * time.Second):
 // Simulate work.
 return nil
 case &lt;-ctx.Done():
 // React to cancellation.
 return ctx.Err()
 }
}
</code></pre>
<h2>Root Contexts: Golang Context Background and TODO</h2>
Root contexts are the starting points of a context tree: <code>context.Background()</code> and <code>context.TODO()</code>. They are never canceled and do not carry deadlines or values, but they are used to bootstrap context usage at system boundaries.
<ul>
<li>context.Background is the correct root for main, init, and top‑level tests.</li>
<li>context.TODO is a placeholder when you know a context is needed but have not yet decided the right parent.</li>
</ul>
In HTTP handlers you normally do not create these directly because the request already provides a context, but they are important in command‑line tools, background jobs, or when kicking off long‑running processes.
Example: CLI tool using Background
<pre class="language-go"><code>func main() {
 ctx := context.Background()

 if err := run(ctx); err != nil {
 log.Fatal(err)
 }
}

func run(ctx context.Context) error {
 // Further derivations happen here, e.g. WithTimeout.
 return doWork(ctx)
}
</code></pre>
<h2>Derived Contexts: WithCancel, WithTimeout, WithDeadline</h2>
Derived contexts add cancellation and timeouts scoped to a subtree of operations. The function that creates a derived context is responsible for calling cancel to release resources.
<ul>
<li>WithCancel(ctx) returns a child context and a cancel function you must call exactly once;</li>
<li>WithTimeout(ctx, d) cancels after a duration;</li>
<li>WithDeadline(ctx, t) cancels at a specific time.</li>
</ul>
A critical best practice is to always defer cancel immediately after creating the context to avoid leaks. Even when you &ldquo;know&rdquo; the timeout will fire, cancel frees timers and internal state earlier.
Example: Canceling work explicitly
<pre class="language-go"><code>func doWithCancel(parent context.Context) error {
 ctx, cancel := context.WithCancel(parent)
 defer cancel() // Always defer.

 ch := make(chan error, 1)
 go func() {
 // Simulated long work.
 time.Sleep(5 * time.Second)
 ch &lt;- nil
 }()

 // Cancel after 1 second for demonstration.
 time.AfterFunc(1*time.Second, cancel)

 select {
 case err := &lt;-ch:
 return err
 case &lt;-ctx.Done():
 return ctx.Err()
 }
}
</code></pre>
Example: Using WithTimeout for bounded operations
<pre class="language-go"><code>func doWithTimeout(parent context.Context) error {
 ctx, cancel := context.WithTimeout(parent, 2*time.Second)
 defer cancel() // Important.

 select {
 case &lt;-time.After(5 * time.Second):
 // Would finish too late.
 return nil
 case &lt;-ctx.Done():
 return ctx.Err() // Likely context.DeadlineExceeded.
 }
}
</code></pre>
<h2>Using Context in HTTP Handlers (Incoming Requests)</h2>
The net/http package binds a context to every <code>*http.Request through r.Context()</code>. This context is canceled when the client disconnects, the handler returns, or a server‑defined timeout is reached.
Best practice is to treat <code>req.Context()</code> as the root context for all work related to that request. Pass it to any goroutines, database calls, or external services so they can react to cancellation and avoid doing work for abandoned requests.
Example: HTTP handler using context
<pre class="language-go"><code>func handler(w http.ResponseWriter, r *http.Request) {
 ctx := r.Context()

 // Simulated slow work respecting context.
 if err := doWork(ctx); err != nil {
 if errors.Is(err, context.Canceled) {
 log.Println("request canceled by client")
 return
 }
 http.Error(w, "internal error", http.StatusInternalServerError)
 return
 }

 w.Write([]byte("OK"))
}

func doWork(ctx context.Context) error {
 select {
 case &lt;-time.After(5 * time.Second):
 return nil
 case &lt;-ctx.Done():
 return ctx.Err()
 }
}
</code></pre>
Here, if the client closes the connection before 5 seconds, <code>ctx.Done()</code> is triggered and the work stops early.
<h2>Outbound HTTP Requests with Context</h2>
When your service calls other services, <code>http.NewRequestWithContext</code> or <code>req.WithContext(ctx)</code> attaches a context to the outbound request. If the context is canceled or its deadline exceeds, the HTTP client will abort the request and return an error consistent with the context state.
A common pattern is to derive a tighter timeout for each external call from the incoming request&rsquo;s context. This ensures that if the original HTTP request is canceled, all outbound calls cancel too, but each dependency still has its own latency budget.
Example: Outbound call with per‑call timeout
<pre class="language-go"><code>func callExternalAPI(ctx context.Context, client *http.Client, url string) ([]byte, error) {
 // Derive a 2‑second timeout from the incoming context.
 ctx, cancel := context.WithTimeout(ctx, 2*time.Second)
 defer cancel()

 req, err := http.NewRequestWithContext(ctx, http.MethodGet, url, nil)
 if err != nil {
 return nil, err
 }

 resp, err := client.Do(req)
 if err != nil {
 return nil, err // Could be due to ctx timeout.
 }
 defer resp.Body.Close()

 return io.ReadAll(resp.Body)
}
</code></pre>
This pattern integrates cleanly into higher‑level HTTP handlers by passing <code>r.Context()</code> into <code>callExternalAPI</code>.
<h2>Database Operations with context.Context</h2>
The database/sql package exposes <code>QueryContext</code>, <code>ExecContext</code>, and <code>PrepareContext</code>, which accept <code>context.Context</code> as the first argument. This allows queries and commands to be bounded by deadlines and to be canceled when requests or upstream operations end.
Using golang database context properly reduces the chance of long‑running queries tying up connections unnecessarily. Drivers that support context should check <code>ctx.Done()</code> and attempt to interrupt queries or close connections when cancellation is signaled.
Example: Query with request context
<pre class="language-go"><code>func getUser(ctx context.Context, db *sql.DB, id int64) (*User, error) {
 const query = `SELECT id, name, email FROM users WHERE id = ?`

 row := db.QueryRowContext(ctx, query, id)

 var u User
 if err := row.Scan(&amp;u.ID, &amp;u.Name, &amp;u.Email); err != nil {
 return nil, err
 }
 return &amp;u, nil
}
</code></pre>
Example: Query with stricter timeout than request
<pre class="language-go"><code>func getUserFast(ctx context.Context, db *sql.DB, id int64) (*User, error) {
 ctx, cancel := context.WithTimeout(ctx, 500*time.Millisecond)
 defer cancel()

 const query = `SELECT id, name, email FROM users WHERE id = ?`

 row := db.QueryRowContext(ctx, query, id)

 var u User
 if err := row.Scan(&amp;u.ID, &amp;u.Name, &amp;u.Email); err != nil {
 // If timeout, err may wrap context.DeadlineExceeded.
 return nil, err
 }
 return &amp;u, nil
}
</code></pre>
Combining these patterns with HTTP request context means database calls will stop when clients disconnect or when timeouts are exceeded.
&gt;&gt; Explore further:
<ul>
<li style="font-style: italic;">How to&nbsp;<a href="https://reliasoftware.com/blog/pgx-golang">Work with PostgreSQL in Golang using pgx Package</a>?</li>
<li style="font-style: italic;">How to <a href="https://reliasoftware.com/blog/golang-sqlite">Use SQLite in Go</a>? Setup, Patterns, and Performance</li>
</ul>
<h2>Using Context Values Safely</h2>
<code>context.WithValue</code> lets you attach key‑value pairs to a context for request‑scoped metadata. Typical uses include correlation IDs, user information, and feature flags that really belong to the lifetime of that specific HTTP request.
However, values should be small and immutable, and context should not be treated as a generic bag of dependencies. Putting large or core dependencies like *sql.DB or configuration in context reduces clarity and makes APIs harder to understand and test.
Example: Storing a correlation ID
<pre class="language-go"><code>type ctxKeyCorrelationID struct{}

func WithCorrelationID(ctx context.Context, id string) context.Context {
 return context.WithValue(ctx, ctxKeyCorrelationID{}, id)
}

func CorrelationID(ctx context.Context) (string, bool) {
 v := ctx.Value(ctxKeyCorrelationID{})
 id, ok := v.(string)
 return id, ok
}
</code></pre>
Using it in an HTTP handler:
<pre class="language-go"><code>func handler(w http.ResponseWriter, r *http.Request) {
 ctx := r.Context()

 id := r.Header.Get("X-Correlation-ID")
 if id == "" {
 id = uuid.NewString()
 }
 ctx = WithCorrelationID(ctx, id)

 logWithCorrelation(ctx, "handling request")
 // Pass ctx down to further calls.
}

func logWithCorrelation(ctx context.Context, msg string) {
 if id, ok := CorrelationID(ctx); ok {
 log.Printf("[corr_id=%s] %s", id, msg)
 return
 }
 log.Println(msg)
}
</code></pre>
This keeps the correlation ID logically tied to the request&rsquo;s context while avoiding global state.
<h2>Function Signatures and Propagation Best Practices</h2>
The Go community has converged on conventions for where and how to use context.
<ul>
<li>Any function that performs I/O, blocks, or spawns goroutines should accept context.Context as its first parameter (named ctx).</li>
<li>Pure, short, CPU‑only functions usually do not include context.</li>
<li>Contexts should be passed down the call stack, not stored on structs or reused across unrelated operations.</li>
</ul>
Code becomes easier to reason about: when you read a signature func (s *Service) DoSomething(ctx context.Context, req *Request) error, you know immediately that cancellation, timeouts, and request‑scoped metadata are involved.
Example: Propagation through layers
<pre class="language-go"><code>// HTTP handler layer.
func (h *Handler) CreateUser(w http.ResponseWriter, r *http.Request) {
 ctx := r.Context()

 var input CreateUserInput
 if err := json.NewDecoder(r.Body).Decode(&amp;input); err != nil {
 http.Error(w, "bad request", http.StatusBadRequest)
 return
 }

 user, err := h.service.CreateUser(ctx, input)
 if err != nil {
 http.Error(w, "internal error", http.StatusInternalServerError)
 return
 }

 json.NewEncoder(w).Encode(user)
}

// Service layer.
func (s *Service) CreateUser(ctx context.Context, in CreateUserInput) (*User, error) {
 // Business logic here.
 return s.repo.InsertUser(ctx, in)
}

// Repository layer.
func (r *Repo) InsertUser(ctx context.Context, in CreateUserInput) (*User, error) {
 // Uses ctx for DB operations.
 return insertUserDB(ctx, r.db, in)
}
</code></pre>
Every layer can participate in cancellation, timeouts, and logging via the same ctx.
<h2>Common Pitfalls and Modern Tooling</h2>
Some mistakes show up repeatedly in real Go code:
<ul>
<li>Forgetting to call cancel after WithCancel/WithTimeout/WithDeadline, leading to leaked timers and goroutines.</li>
<li>Using context.Background deep in the stack instead of propagating the caller&rsquo;s context, effectively disabling cancellation.</li>
<li>Misusing WithValue as a general data store.</li>
</ul>
Modern tooling helps. For example, gopls includes a lostcancel analyzer that detects contexts whose cancel function is never called. Combined with code reviews and clear team guidelines, this keeps context usage consistent across large codebases.
Example: lostcancel pattern (do NOT do this)
<pre class="language-go"><code>func bad(ctx context.Context) error {
 ctx, _ = context.WithTimeout(ctx, time.Second)
 // Missing cancel() call here &ndash; analyzer will warn.
 return doWork(ctx)
}
</code></pre>
<h2>Closing Thoughts</h2>
Go&rsquo;s context package is central to writing resilient HTTP and database services that manage timeouts, cancellations, and request‑scoped metadata cleanly. By following idiomatic patterns like propagating request context, choosing proper deadlines, using golang database context methods, and avoiding misuse of WithValue, you make your services more predictable, observable, and resource‑efficient.
As <a href="https://reliasoftware.com/blog/functions-in-go-1-21-release">Go continues to evolve in versions 1.21</a> and beyond, context remains the standard mechanism for coordinating concurrent work, especially in networked and <a href="https://reliasoftware.com/blog/how-to-build-microservices-in-golang-with-echo">microservice environments</a>. Mastering go http context and database/sql context patterns is therefore essential for anyone building production services in Go today.
&gt;&gt;&gt; Follow and Contact&nbsp;<a href="https://reliasoftware.com/">Relia Software</a> for more information!

Go&rsquo;s context package is the standard way to control cancellation, timeouts, and request-scoped data across goroutines in Go services, especially for HTTP and databases.

A Complete Guide to Use Golang Context with Code Examples

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Auto-scaling to do, housing market web, study finder, private desktop journal, testimonial board, etc, are innovative web app ideas for startups in 2026.

10 Innovative Web App Ideas For Startups & Enterprises 2026

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SQL Injection, Command Injection Attacks, Cross-Site Scripting, Path Traversal, Race Condition,... are critical Golang security vulnerabilities you must be know.

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Moving an existing React Router app to TanStack Router can feel daunting, but you don't need a full rewrite. A TanStack Router migration done step-by-step: auditing routes and loaders, mappingg definitions, moving data loading, then searching params to keep risk low and let you ship incrementally.
This guide walks through when migration makes sense, how to audit and map your current setup, how to migrate loaders and search params, and how to test and go-live. For setup and API (createRootRoute, createRoute, createRouter, loaders), see the&nbsp;<a href="https://tanstack.com/router/latest/docs" rel="noopener noreferrer">TanStack Router docs</a>. If you'd rather use a framework with built-in routing, see <a href="https://nextjs.org/docs" rel="noopener noreferrer">Next.js docs</a>.
<h2>When Migration Makes Sense?</h2>
Migrate to TanStack Router when type-safe params and search, or integrated loaders and caching, would materially improve your app. You're already paying the cost of maintaining routes and loaders; moving to a single, typed route tree and first-class loaders can reduce bugs and simplify data flow.
Howerver, keep staying on React Router when your team is productive and you're not hitting type-safety or data-loading pain. Migration takes time; if the current setup works, defer. Use a framework (e.g. Next.js) when you need <a href="https://reliasoftware.com/blog/react-server-side-rendering-best-practices">server-side rendering</a>, file-based routing, or framework-specific features
<blockquote>
Key Takeaway: Migrate when the benefits (type safety, loader/cache DX) outweigh the cost. When in doubt, try one feature area (e.g. one section of routes) first.
</blockquote>
<h2>5 Steps to Migrate React Router app to TanStack Router</h2>
<h3>Step 1: Audit Your Current Routes and Loaders</h3>
Before changing code, list what you have. In a React Router app, routes are usually defined as a component tree (e.g. <code>createBrowserRouter</code> with <code>Route</code> elements). For each route, note:
<ul>
<li>Path (including dynamic segments, e.g. <code>/users/:userId</code>).</li>
<li>Loader: what it fetches and what it returns.</li>
<li>Dependencies: parent loaders, search params, or redirects.</li>
</ul>
Write a short table or doc: route path, component, loader signature, and any search params the route reads or sets. That becomes your migration map. Identify the root route and the nesting structure so you can mirror it in TanStack Router.
<blockquote>
Best Practice: Audit the routes you'll migrate first (e.g. one product area). Leave the rest on React Router until that slice is done, or plan a phased cutover.
</blockquote>
<h3>Step 2: Map Route Definitions: Component Tree vs Code/File-Based</h3>
React Router uses a component tree: you nest <code>&lt;Route&gt;</code> elements and pass <code>path</code>, <code>element</code>, and <code>loader</code>. TanStack Router uses code-based route definitions (or file-based with <code>@tanstack/router-vite-plugin</code>): you call <code>createRootRoute</code>, <code>createRoute</code>, and build a route tree with <code>addChildren</code>.
Map each React Router route to a TanStack route:
<ul>
<li>Root &rarr; <code>createRootRoute()</code>.</li>
<li>Each child route &rarr; <code>createRoute({ getParentRoute: () =&gt; parent, path: '&hellip;' })</code>.</li>
<li>Dynamic segments: in React Router you use <code>:id</code>; in TanStack Router you use <code>$id</code> (e.g. <code>/users/$userId</code>).</li>
</ul>
Example: from a React Router tree with <code>/</code>, <code>/users</code>, and <code>/users/:userId</code>, you get:
<pre class="language-typescript"><code>import { createRootRoute, createRoute, createRouter } from '@tanstack/react-router';

const rootRoute = createRootRoute();

const indexRoute = createRoute({
 getParentRoute: () =&gt; rootRoute,
 path: '/',
});

const usersRoute = createRoute({
 getParentRoute: () =&gt; rootRoute,
 path: '/users',
});

const userRoute = createRoute({
 getParentRoute: () =&gt; usersRoute,
 path: '$userId',
});

const routeTree = rootRoute
 .addChildren([indexRoute, usersRoute.addChildren([userRoute])]);

export const router = createRouter({ routeTree });
</code></pre>
The structure mirrors your current nesting; only the API shape changes. Use the same path strings so URLs stay identical. When migrating, lazy-load route components and <a href="https://reliasoftware.com/blog/react-suspense">add a Suspense boundary</a> at the route level so heavy screens don't block the initial bundle.
Optionally align route files with a feature-based layout (e.g.&nbsp;<code>routes/</code> with feature entry points) so the migrated app matches modern frontend structure.
<h3>Step 3: Migrate Loaders and Data Dependencies</h3>
In React Router, loaders are functions that receive <code>{ params, request }</code> and return data. In TanStack Router, loaders are attached to the route and receive a context that includes <code>params</code> (and search when you add validation). Move each loader body over and adapt the argument shape.
React Router example:
<pre class="language-typescript"><code>// React Router: loader on route
export async function loader({ params }: { params: { userId: string } }) {
 const res = await fetch(`/api/users/${params.userId}`);
 if (!res.ok) throw new Error('Failed to load user');
 return res.json();
}
</code></pre>
Move that logic into the route's <code>loader</code> in TanStack Router. The fetch and error handling stay the same; only the registration and param typing change.
TanStack Router equivalent:
<pre class="language-typescript"><code>const userRoute = createRoute({
 getParentRoute: () =&gt; usersRoute,
 path: '$userId',
 loader: async ({ params }) =&gt; {
 const res = await fetch(`/api/users/${params.userId}`);
 if (!res.ok) throw new Error('Failed to load user');
 return res.json() as Promise&lt;{ id: string; name: string }&gt;;
 },
});
</code></pre>
Keep the same fetch logic and error handling; only the way the loader is registered and how params are typed changes. During migration, ensure loaders that need multiple fetches use parallel requests (e.g. <code>Promise.all</code>) instead of sequential awaits to avoid waterfalls; see the TanStack Router guide for loader patterns.
In the route component, use the router's loader data (e.g. from route context or <code>useLoaderData</code>) instead of React Router's hook. Parent/child loader dependencies map to TanStack's parent route loaders. Child routes then read parent loader data from route context.
<h3>Step 4: Search Params and Validation</h3>
React Router exposes raw search params (e.g. <code>useSearchParams()</code>); you parse and validate in components. TanStack Router lets you define a search schema on the route with <code>validateSearch</code>. Centralize parsing and validation there so components get a typed object.
Example: a users list with <code>page</code> and <code>sort</code>:
<pre class="language-typescript"><code>const usersListRoute = createRoute({
 getParentRoute: () =&gt; rootRoute,
 path: '/users',
 validateSearch: (search: Record&lt;string, unknown&gt;) =&gt; {
 const page = Number(search.page);
 const sort = String(search.sort || 'name');
 return {
 page: Number.isFinite(page) &amp;&amp; page &gt;= 1 ? page : 1,
 sort: ['name', 'created'].includes(sort) ? sort : 'name',
 };
 },
});
</code></pre>
Then in components, read and set search via the router's typed APIs so you never pass invalid values. Migrate one route at a time: add <code>validateSearch</code>, then switch the component to use the router's search API instead of <code>useSearchParams</code>.
<blockquote>
Pro Tip: <a href="https://reliasoftware.com/blog/zod-schema-validation-react-tutorial">Use Zod library</a> (or another schema lib) inside <code>validateSearch</code> for complex shapes and clear error handling. TanStack Router works with any function that returns the validated shape or throws.
</blockquote>
<h3>Step 5: Test and Go-Live</h3>
After mapping routes, loaders, and search params, test the migrated slice end-to-end. Click through every link and form that affects the URL or loader data. Check that:
<ul>
<li>URLs match the old app (same paths and query strings).</li>
<li>Loader data loads and displays correctly.</li>
<li>Search params are read and written with the validated shape.</li>
<li>Error and loading states still work (TanStack Router supports error boundaries and pending state).</li>
</ul>
Run your existing tests; update any that depend on React Router (e.g. wrapper or mock router). Then deploy behind a feature flag or migrate one section at a time so you can roll back if needed. Once the TanStack Router migration is stable, remove React Router and the old route tree.
<h2>Conclusion</h2>
A TanStack Router migration from React Router is manageable when you break it into steps: decide when it's worth it, audit routes and loaders, map route definitions to TanStack's API, migrate loaders and search params, then test and go-live.
You don't need a full rewrite; you can move one area of the app first and keep the rest on React Router until you're ready. Hope this guide will help you!
&gt;&gt;&gt; Follow and Contact&nbsp;<a href="https://reliasoftware.com/">Relia Software</a> for more information!

Migrating from React Router to TanStack Router done by: 1: Auditing routes and loaders; 2: Mapping definitions; 3: Moving data loading: 4: Searching params.

A Full Guide to Migrate from React Router to TanStack Router

Migrate from React Router to TanStack Router

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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;
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<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:
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<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;
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<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!

AI agents in software testing are systems that can automatically determine what to test and execute those tests inside an application without any test scripts.

AI Agents in Software Testing: Roles, Types & Best Practices

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