DEV Community: Vishal Porwal

Building Custom AI Agents With JavaScript and React — From Prototype to Production

Vishal Porwal — Tue, 18 Aug 2026 08:52:32 +0000

Every AI agent tutorial ends the same way.
The agent completes a task. The output looks correct. The tutorial ends. The reader closes the tab feeling like they understand how to build AI agents.
Then they try to build one for a real enterprise application — and discover that the tutorial covered about fifteen percent of what production AI agent development actually requires.
This post is about the other eighty-five percent.

The prototype is the easy part
The core loop of an AI agent is not complex to implement in JavaScript:
async function runAgent(goal, tools) {
const messages = [{ role: 'user', content: goal }];
while (true) {
const response = await callModel(messages, tools);
if (response.type === 'complete') {
return response.output;
}
const toolResult = await executeTool(
response.toolName, response.toolInput
);
messages.push({ role: 'assistant', content: response });
messages.push({ role: 'tool', content: toolResult });
}
}
This works in development. With a clean dataset. With well-formed goals. With tools that behave as expected. Production is different.

What production adds to the agent core
Observability

Every iteration of the loop needs to be logged. Every tool call. Every model response. Every state transition. Not because you expect things to go wrong — because things will go wrong in ways you did not anticipate, and the only way to debug them is to have a complete record of what the agent did and why.
The logging format needs to satisfy audit requirements — structured, queryable, and retained for the period required by applicable compliance frameworks.

Error handling
The model will occasionally produce malformed tool calls. The tool APIs will occasionally return unexpected errors. The loop will occasionally not converge. Each of these needs explicit handling — not a generic catch block, but specific handling for each failure mode that logs what happened and prevents it from propagating into the enterprise systems the agent is connected to.

Timeout and circuit breaking
Unbounded loops are not acceptable in production enterprise applications. The agent needs a maximum iteration count. Each tool call needs a timeout. The orchestration layer needs circuit breakers that prevent cascading failures when a dependent service is slow or unavailable.
Rate limiting
Enterprise AI agents can hit model API rate limits in ways that development testing does not reveal. Rate limiting needs to be built into the orchestration layer before production deployment, not after the first rate limit error appears in the production logs.

What production adds to tool design
Development tools are optimistic. They assume clean inputs, fast responses, and well-formed outputs. Production tools are defensive. They assume messy inputs, variable response times, and occasional failures at every point in the call chain.
Production tools need input validation that catches the malformed inputs that language models occasionally produce. They need timeout handling. They need error handling that is specific to each failure mode. They need idempotency for tools that have side effects — so that retried tool calls do not produce unintended duplicate effects.

What production adds to the React interface

The development interface for an AI agent is often minimal — a text input for the goal, a display area for the output, a loading indicator while the agent runs.
The production interface for an enterprise AI agent is a governance surface. It needs to show the agent's goal. It needs to show the agent's reasoning — what tools it called, what results it received, what decisions it made. It needs to show the agent's outputs in a form that allows users to review, approve, reject, or correct them.
Building this interface with React requires thinking about component architecture explicitly:
function AgentInterface({ agentState, onApprove, onReject, onCorrect }) {
return (

isRunning={agentState.isRunning} />
onApprove={onApprove} onReject={onReject}
onCorrect={onCorrect} />
{agentState.error && }

);
}

The sequence that works

▪ Build observability first — before the agent can do anything interesting, it should be logging everything
▪ Build tools defensively — validation, error handling, timeouts, and logging before connecting tools to the agent
▪ Build the human review interface early — governance requirements are most visible here and hardest to retrofit
▪ Design state management explicitly — the implicit state management of simple React apps does not scale to agent complexity

Where to go deeper
JS Days 2026 — September 16–17, 2026, free and fully virtual — includes a session on building custom AI agents with JavaScript, React, and ReExt from Marc Gusmano, Sales Engineer at Sencha.
Free registration at jsdays.io.

Web Application Development in 2026: A Practical Guide to Building Scalable Apps

Vishal Porwal — Tue, 18 Aug 2026 05:36:41 +0000

Web application development in 2026 is much more than putting together a frontend and connecting it to an API.

Modern applications need to handle complex workflows, large datasets, authentication, security, responsive interfaces, accessibility, integrations, and continuous deployment.

That means architecture matters from day one.

Define the problem before choosing the framework

Start with the business requirements.

Ask:

Who are the users?
What workflows need to be supported?
How much data will the application handle?
What integrations are needed?
What security requirements exist?
How quickly is the application expected to scale?

Only after answering these questions should you select your technology stack.

Pick the frontend according to the workload

React, Angular, Vue, and Ext JS all solve different problems.

React is excellent when ecosystem flexibility is important.

Angular works well when teams want a structured, opinionated framework.

Vue provides a relatively approachable development experience.

Ext JS is particularly interesting for data-heavy enterprise applications.

It provides a large collection of enterprise UI components including JavaScript grids, forms, charts, trees, layouts, and dashboards.

That means developers don't necessarily need to assemble separate libraries for every major part of a business application.

Build reusable components

Reusable components are one of the easiest ways to control frontend complexity.

Instead of implementing the same:

Form
Dialog
Table
Search control
Filter
Navigation element

multiple times, create a consistent component model.

This improves both development speed and maintainability.

Treat data as a first-class concern Enterprise applications frequently have screens where data is the product.

Think about:

CRM dashboards
Financial applications
Inventory systems
Logistics platforms
Analytics tools
ERP software

These applications may require sorting, filtering, pagination, inline editing, grouping, exporting, and real-time updates.

A framework with mature data components can save considerable development effort.

This is an area where Ext JS has a strong advantage because data-intensive UI development is one of its core use cases.

Don't postpone security

Security should be part of the architecture.

At minimum, consider:

Authentication
Authorization
Encryption
Input validation
Secure sessions
Dependency security
Security testing

The uploaded development guidance similarly recommends authentication, encryption, validation, and regular security audits.

Test with production-like data

Never rely exclusively on small development datasets.

If your application will eventually handle 100,000 records, test with something close to that.

Check:

Rendering performance
API latency
Database queries
Memory consumption
Filtering
Sorting
Concurrent users

Virtualized rendering can be particularly useful for large datasets.

Use CI/CD and monitoring

Deployment should be automated wherever practical.

A modern pipeline can:

Run tests
Build the application
Check dependencies
Deploy
Monitor the release

After deployment, monitor errors and performance continuously.

Think about long-term maintenance

The cheapest framework isn't necessarily the cheapest solution.

Consider the total cost of:

Development
Third-party libraries
Integration
Training
Upgrades
Security maintenance
Technical debt

For a small application, an open-source ecosystem may be the obvious choice.

For a large enterprise application, a more integrated commercial framework such as Ext JS can sometimes make sense because it reduces the amount of infrastructure the team has to assemble and maintain.

Final takeaway

There is no single "best" web development stack.

The right choice depends on the problem.

For highly customized consumer interfaces, React may be ideal.

For structured enterprise teams, Angular is compelling.

For simpler progressive applications, Vue can be a great fit.

For data-heavy enterprise applications where grids, forms, charts, dashboards, and complex workflows are central, Ext JS is one of the strongest options worth evaluating in 2026.

The best framework isn't the one with the most hype.

It's the one that makes your specific application easier to build, scale, and maintain.

Front-End Frameworks in 2026: Which One Should You Choose?

Vishal Porwal — Mon, 17 Aug 2026 07:31:15 +0000

Choosing a frontend framework in 2026 isn't as simple as looking at popularity charts.

React, Angular, Vue, Svelte, and Ext JS are all capable of building modern applications. The difference is what they're optimized for.

For a simple product website, almost any modern framework can work.

For a large enterprise application with complex workflows and thousands of records, the requirements change completely.

What matters in 2026?

When evaluating a framework, I'd look at:

Performance with real-world data
Component availability
Developer productivity
TypeScript support
Accessibility
Maintainability
Ecosystem maturity
Security
Long-term support
Total cost of ownership

The last two are often overlooked.

A framework may be free but still require several paid or open-source libraries to provide the functionality an enterprise application needs.

React

React's biggest advantage is flexibility.

The ecosystem is enormous, making it possible to find solutions for almost every UI requirement.

The downside is that you often need to assemble those solutions yourself.

For example, a large application may require separate solutions for JavaScript grids, forms, charts, routing, state management, and other functionality.

That's fine when flexibility is the priority.

Angular

Angular takes a more structured approach.

It includes many application-level capabilities and encourages consistent patterns across teams.

That makes it attractive for large organizations, although its learning curve can be higher than lighter alternatives.

Vue

Vue provides a nice balance between simplicity and application capability.

Its Composition API makes modern development patterns accessible while retaining an approachable syntax.

It's a good option for teams that want flexibility without the amount of ecosystem decision-making associated with React.

Svelte

Svelte is interesting because it shifts much of the work to the compilation stage.

That can produce lightweight applications and strong runtime performance.

It's worth considering when bundle size and initial performance are particularly important.

Where Ext JS Fits

Ext JS approaches the problem differently.

Instead of primarily focusing on giving developers maximum ecosystem flexibility, it focuses heavily on enterprise application development.

It includes more than 140 pre-built UI components covering areas such as:

Data grids
Forms
Charts
Trees
Calendars
Layouts
Dashboards
Navigation
Business controls

For a data-intensive application, this can be a major advantage.

A team building an enterprise dashboard doesn't necessarily want to spend weeks evaluating different grid, chart, form, and layout libraries.

They may prefer having those capabilities available within one integrated framework.

Data Grids Are a Good Example

A basic table is easy to build.

Enterprise grids are not.

Once you add:

Large datasets
Virtual scrolling
Column virtualization
Sorting
Filtering
Grouping
Inline editing
Selection
Complex layouts

the problem becomes much more substantial.

This is an area where Ext JS has a clear advantage because data-intensive applications are one of its core use cases.

My 2026 Shortlist

React — best when ecosystem flexibility matters most.

Angular — best for large teams that prefer an opinionated architecture.

Vue — best for approachable modern development.

Svelte — best when lightweight compiled output is a priority.

Ext JS — best for complex, data-intensive enterprise applications.

And there's no reason every organization has to make the same decision.

If you're building a consumer-facing application, React may be the obvious choice.

If you're building a large internal enterprise platform with sophisticated data management requirements, I'd put Ext JS very high on the shortlist.

For existing React applications, ReExt is also worth considering because it allows Ext JS components to be used within React rather than requiring an immediate framework migration.

Final Thought

Framework selection shouldn't be a popularity contest.

Build a small proof of concept using your actual data and workflows. Measure performance. Look at development effort. Evaluate maintenance requirements.

Then choose the framework that fits the workload.

For data-heavy enterprise applications in 2026, Ext JS is one of the strongest options available because it provides a comprehensive set of components and capabilities without requiring teams to assemble everything from separate libraries.

That's a very different proposition from simply choosing the most popular JavaScript framework—and for the right application, it's a valuable one.

JavaScript Data Grids in 2026: What Are You Actually Choosing?

Vishal Porwal — Fri, 14 Aug 2026 09:56:02 +0000

I've been looking at JavaScript data grids again, and one thing is pretty clear:

You're not just choosing a JavaScript grid anymore. You're choosing a development philosophy.

There are basically two camps.

Headless:
You get the logic and build the UI.

Batteries-included:
You get the grid, UI behavior, components, and a lot of the infrastructure around it.

That changes how I'd evaluate the popular options.

TanStack Table

Great if you want complete control.

You get the table logic, but you're responsible for rendering and styling.

That's a good trade if your team already has a strong component system and wants the grid to fit perfectly into it.

AG Grid

If the grid is basically the application, AG Grid is hard to ignore.

Filtering, sorting, pagination, cell customization, exports, server-side operations, and large-data handling are all part of the package.

I'd look at it for analytics dashboards, admin systems, and other grid-heavy apps.

Handsontable

If your users say:

"I want it to work like Excel."

I'd probably start here.

Spreadsheet-style editing, validation, dropdowns, and copy/paste behavior are the main attraction.

Grid.js / Tabulator

Don't underestimate simple solutions.

If all you need is a good data table without a huge amount of complexity, a lightweight option can save you from adding unnecessary infrastructure.

Grid.js and Tabulator are worth checking for those use cases.

Ext JS

The interesting thing about Ext JS is that the grid isn't really the whole story.

You get a broader enterprise UI toolkit with things like:

Data grids
Charts
Forms
Pivot tables
Data handling
Large-data rendering

That matters when you're building an actual enterprise application rather than a page that happens to contain a table.

For example, if a financial dashboard needs a large grid, charts based on the same data, filtering, pivot analysis, and forms for updating records, having those pieces within one ecosystem can simplify the architecture.

So Which One?

My rough rule would be:

Need maximum UI control?
→ TanStack Table

Grid is the main product?
→ AG Grid

Spreadsheet-style editing?
→ Handsontable

Simple/lightweight table?
→ Grid.js or Tabulator

Grid + charts + forms + pivot tables in a larger enterprise app?
→ Ext JS

The biggest mistake would be choosing based only on benchmark numbers.

I'd look at the whole workload:

How much data?
How much editing?
How much custom UI?
How many other components?
How much code do we want to maintain?
What's the licensing budget?
What does the team already know?

A 10,000-row table in a simple admin panel and a million-row enterprise data application are completely different problems.

So instead of asking "What's the best JavaScript grid?", I'd ask:

"Which grid gives my team the right amount of control without creating unnecessary work?"

That's probably the more useful question in 2026.

AI Agents for JavaScript Developers — A Practical Introduction to React and ReExt

Vishal Porwal — Fri, 14 Aug 2026 08:57:59 +0000

JavaScript developers are being asked to build AI agents.
Not at some point in the future.
Right now.
In production applications.
For enterprise organizations that need AI systems that reason, act, and deliver real business outcomes.

Most JavaScript developers were not trained for this. The gap between what the JavaScript curriculum covers and what enterprise AI agent development requires is real.
This post is about closing that gap — specifically for developers working with React and ReExt.
What an AI agent actually is
An AI agent is a system in which a language model is used not just to respond to queries but to reason about goals and take actions toward achieving them.
The actions are taken through tools. Tools are functions the agent can call to interact with external systems. In a JavaScript enterprise environment, those tools might be:
▪ Database query functions
▪ Internal API calls
▪ Document retrieval systems
▪ Data transformation utilities
▪ Reporting functions
The agent receives a goal. It decides which tools to call. It calls them. It evaluates the results. It decides what to do next. It continues until the goal is accomplished. This is fundamentally different from a chatbot. The model is not generating a response. It is executing a workflow.
Why React for AI agents
React's contribution to AI agent development is state management clarity.

AI agent applications manage more concurrent state than most enterprise applications —
agent goals, tool selections, tool results, intermediate outputs, final results, human review states. React's component model provides the right abstractions for this complexity.

The workflow status display.
The tool call log.
The output surface.
The human review interface.
Each is a React component connecting to agent state through patterns React developers already know.
Why ReExt for AI agents

ReExt's contribution is enterprise data capability that React's ecosystem does not consistently provide.
AI agents generate outputs that need to be displayed in data interfaces — grids with thousands of classified records, charts showing agent-analyzed trends, trees representing agent-processed hierarchical data. ReExt gives React developers access to Ext JS's 140+ enterprise UI components through React's component model.
For AI agent applications specifically, the ReExt DataGrid matters because:
▪ It renders large result sets efficiently — buffered column rendering keeps performance proportional to visible data
▪ It supports cell editing for human review and correction workflows
▪ It handles real-time updates without performance degradation
▪ It provides the filtering and sorting that users need to navigate agent outputs
The three things JavaScript developers need to build AI agents
1. Agent Orchestration
The reasoning loop — goal in, tool selection, tool call, result evaluation, next decision. LangChain.js provides abstractions. OpenAI Assistants API provides another approach. Anthropic's tool use provides a third. Each has different trade-offs. Know them before committing.

2. Tool Design
The functions the agent calls. This is where most implementations hit production problems. Development tools break with production data. Build defensively. Handle errors explicitly. Assume the data will be messier than your tests revealed.

3. Interface Architecture
The React and ReExt layer that surfaces agent state and outputs for users. This is where governance requirements land — human review workflows, approval interfaces, correction mechanisms. It is not a UI afterthought. It is a core engineering requirement.
What production looks like
JavaScript developers who have shipped enterprise AI agents report the same pattern consistently.
The architecture felt complex before building it. After building it — with React providing the interface structure and ReExt providing the data layer — the architecture felt manageable.
The tool design felt like a small detail before production. In production, it was the most consequential engineering decision in the system.

The governance requirements felt like an enterprise formality before deployment. After deployment, they were the requirements that determined whether users could actually trust the system.
Treat it as enterprise software engineering from the start. The same rigor that applies to reliability, observability, and maintainability in any other production system applies here — and then some.

Where to go deeper
JS Days 2026 — September 16–17, 2026, free and fully virtual — includes a session specifically on this topic.
Marc Gusmano, Sales Engineer at Sencha, covers building custom AI agents with JavaScript, React, and ReExt — implementation patterns, integration challenges, production trade-offs.
Free registration at jsdays.io.

Front-End Frameworks in 2026: React vs Angular vs Vue vs Ext JS

Vishal Porwal — Thu, 13 Aug 2026 06:06:56 +0000

Framework debates usually turn into popularity contests.

React vs Angular.

Vue vs React.

Svelte vs everyone.

But when you're building an Enterprise Application development, popularity isn't enough.

A better question is:

What will make this application easier to build and maintain for the next 5–10 years?

That's where the comparison gets interesting.

React

React is still the obvious choice when ecosystem flexibility matters.

You can build almost anything with React, and there is a huge ecosystem around it.

The downside?

You often need to choose additional solutions for:

Routing
State management
Forms
Data grids
Charts
Design systems

That's not necessarily a problem. In fact, it's one of React's biggest strengths.

You get freedom.

But freedom also means architectural responsibility.

Angular

Angular is more opinionated.

For large teams, that can actually be a feature.

You get a structured development model, TypeScript, forms, routing, HTTP tooling, and testing support.

The learning curve is higher, but teams working on large applications can benefit from having established conventions.

Vue is a nice middle ground.

It's approachable, flexible, and supports progressive adoption.

If your team wants modern frontend development without adopting a particularly heavyweight architecture, Vue can be a very reasonable choice.

Ext JS

Ext JS approaches the problem differently.

Instead of giving you a framework and leaving you to assemble an enterprise UI stack, it provides a large collection of business-oriented components from the beginning.

That includes grids, forms, charts, trees, layouts, dashboards, calendars, and other enterprise UI building blocks.

This becomes particularly interesting when your application is data-heavy.

Think:

Trading platforms
ERP systems
CRM applications
Reporting platforms
Logistics dashboards
Operations portals
Financial applications

These applications aren't just rendering a few cards and buttons.

They're rendering and manipulating large amounts of structured data.

Data Grid Performance

This is one area where framework selection can have a huge impact.

Pagination isn't always enough.

Users may need:

Large datasets
Many columns
Sorting
Filtering
Grouping
Inline editing
Locked columns
Real-time updates

Ext JS 8.0 includes horizontal buffering and column virtualization to reduce the amount of data actually rendered in the DOM.

That's one reason I'd seriously consider Ext JS for data-heavy enterprise applications.

The Real Trade-Off

Here's how I'd summarize the choices:

React: maximum flexibility
Angular: maximum structure
Vue: approachable flexibility
Ext JS: integrated enterprise capabilities

There's no universal winner.

If you're building a content-heavy consumer product, I'd probably start with React or Vue.

If you're standardizing development across a large organization, Angular deserves consideration.

But if you're building a serious business application where JavaScript Grid, dashboards, forms, data visualization, and complex workflows are central to the product, Ext JS can be the strongest overall option.

Don't Forget Total Cost of Ownership

One mistake teams make is comparing:

React = free
Ext JS = commercial

…and stopping there.

That's not a complete TCO calculation.

You also need to consider:

Development time
Third-party licenses
Integration work
Maintenance
Dependency upgrades
Security reviews
Training
Support
Migration costs

A commercial framework can sometimes be more economical if it removes months of custom development and reduces long-term maintenance.

The opposite can also be true.

That's why the right approach is to build a small proof of concept using your actual workload.

My 2026 shortlist

If I were evaluating frameworks today:

Consumer/product UI → React or Vue

Large standardized enterprise team → Angular

Data-intensive enterprise application → Ext JS

Existing React application needing advanced enterprise components → React + ReExt

The important thing isn't picking the framework with the biggest community.

It's picking the one that solves the hardest parts of your application without creating another set of problems.

Building AI Agents With JavaScript, React, and ReExt — What Enterprise Developers Need to Know

Vishal Porwal — Wed, 12 Aug 2026 11:56:51 +0000

Most JavaScript developers integrating AI into their applications are not building AI agents.
T
They are building AI wrappers — an API call goes out, a response comes back, the UI updates. Clean. Simple. And fundamentally limited.

AI agents are different. They plan. They use tools. They run multi-step workflows. They evaluate their own outputs and decide what to do next.

Building them in an enterprise JavaScript environment — one that needs to meet governance requirements, integrate with complex data systems, and perform under real production load — is a different engineering challenge entirely.

What Makes an AI Agent Different From an AI Integration

A simple AI integration takes a user input, sends it to a model, and displays the output. An AI agent takes a goal and works toward it — calling tools, evaluating results, deciding what to do next, and continuing until the goal is accomplished.
The tools might be:
▪ A database query function
▪ An internal API call
▪ A document retrieval system
▪ A data transformation utility
▪ Another model call for evaluation or summarization

Why React and ReExt Together

React handles the interface layer. Component architecture and state management give you strong foundations for the complex interfaces enterprise AI agents require.

But enterprise AI agents need to surface their outputs in data interfaces that React's ecosystem does not consistently solve at scale. ReExt — Sencha's bridge between React and Ext JS — fills this gap with 140+ enterprise UI components accessible through React's component model.

The Three Layers of an Enterprise AI Agent

Layer 1 — Agent Core
The reasoning layer. The language model receives a goal, evaluates tools, selects one, calls it, evaluates the result, and decides what comes next. In JavaScript — LangChain.js or a custom implementation using OpenAI function calling or Anthropic tool use.

Layer 2 — Tool Layer
The functions the agent calls to interact with enterprise systems. Each tool needs a clear name, description, and defined input/output schema that the language model can understand.

Layer 3 — Interface Layer
Where React and ReExt work together. The ReExt DataGrid provides the performance, filtering, sorting, and editing capabilities that enterprise users need to review and correct agent outputs at scale.
What Production Looks Like
Production enterprise AI agents need:
▪ Logging that captures every tool call, model decision, and output
▪ Error handling that degrades gracefully when model outputs are unexpected
▪ Rate limiting and cost management for operational risk control
▪ Human review workflows for governance and compliance
▪ Audit trails that satisfy enterprise compliance requirements

Where to Go Deeper
Marc Gusmano, Sales Engineer at Sencha, covers this topic in depth at JS Days 2026 — Sencha's free virtual JavaScript conference on September 16–17, 2026.

I Stopped Treating Data Grids Like Fancy HTML Tables

Vishal Porwal — Wed, 12 Aug 2026 06:33:28 +0000

One of the easiest mistakes in frontend development is assuming that a data grid is basically just a table with some JavaScript grid
around it.

That works until the requirements start piling up.

You add sorting.

Then filtering.

Then inline editing.

Then grouping.

Then exports.

Then 20,000+ rows.

Then someone asks why keyboard navigation doesn't work.

Then someone opens the app on a laptop with a smaller screen.

And suddenly your "simple table" has become one of the most complicated components in the application.

The performance problem

If you're dealing with a serious amount of data, rendering everything at once isn't a great approach.

Virtualization is usually one of the first techniques worth looking at. Only the visible portion of the dataset needs to be represented in the DOM, rather than rendering thousands of rows that the user can't see.

For really large datasets, I'd also consider server-side filtering, sorting, and pagination.

The browser shouldn't have to do work that the server can handle more efficiently.

Don't forget accessibility

Data grids are surprisingly difficult from an accessibility perspective.

A good implementation needs more than readable colors.

You need things like:

Keyboard navigation
Proper ARIA roles
Clear focus states
Accessible headers
Screen reader support
Keyboard-accessible filtering and sorting
Sufficient contrast

This is another area where using a mature component can save a lot of time.

Mobile is another challenge

A 15-column desktop grid probably isn't going to be pleasant on a phone.

Instead of trying to make every column fit, consider showing the most important information and exposing additional fields through expandable rows or secondary interactions.

Mobile grids need to be designed around the available screen, not simply scaled down.

What about using a framework?

For smaller projects, I'd probably keep things lightweight.

But for enterprise applications where the grid is central to the product, a more complete component framework can make sense.

I've been looking at Ext JS for exactly this kind of use case. Its grid provides things like virtualization, filtering, grouping, editing, column management, and exporting, while the wider framework also provides components such as charts, forms, and layouts.

The interesting part for me is the integration. You aren't assembling a separate library for every feature around the grid.

Of course, that doesn't mean everyone should use Ext JS. If you're building a basic admin page with a few hundred records, bringing in a large framework may be unnecessary.

My rule of thumb

Don't choose a data grid based on how good its 10-row demo looks.

Test it with the workload your application will actually have.

10,000 rows + 20 columns + filtering + sorting + editing + keyboard navigation will tell you much more than a polished demo ever will.

What are you using for data grids right now, and what has been the biggest pain point once your dataset got large?

Front-End Frameworks in 2026: What Actually Matters for Enterprise Apps?

Vishal Porwal — Mon, 10 Aug 2026 09:47:02 +0000

I've been comparing React, Angular, Vue, and Ext JS for enterprise development, and one thing keeps coming up:

Framework popularity isn't enough to make the decision.

For a small application, almost any modern Front-End Frameworks can get the job done.

For a large data-heavy application, the trade-offs become much more obvious.

React: maximum flexibility

React gives you a huge ecosystem and a lot of architectural freedom.

That's great when your team wants to choose its own stack.

The downside is that you often need to assemble the pieces yourself.

For example:

React

UI library
Data grid
Form library
Charting
State management
Testing
Design system

That isn't necessarily a problem, but it creates more integration and maintenance work.

Angular: structure over flexibility

Angular takes a more opinionated approach.

You get routing, forms, CLI tooling, testing infrastructure, and a TypeScript-first architecture.

For large teams, having consistent conventions can be more valuable than unlimited flexibility.

Vue: easier onboarding

Vue is attractive when developer experience is a major priority.

Its learning curve is relatively gentle, while the Composition API provides enough flexibility for larger applications.

It's a good option for teams that don't want the complexity of a larger enterprise framework.

Ext JS: built for data-heavy applications

Ext JS takes a different approach.

Instead of starting with a minimal framework and assembling an ecosystem around it, it provides a large collection of enterprise components out of the box.

The current version includes 140+ components, including:

Data grids
Charts
Forms
Trees
Calendars
Layout managers

The grid functionality is particularly relevant for applications handling large datasets because virtualization and buffering are built into the component.

That's useful for applications such as:

Financial dashboards
Reporting systems
Admin platforms
Analytics applications
Data-intensive internal tools
Don't forget total cost of ownership

There's another thing I think developers should consider more often.

The license price isn't the total cost.

A free framework can still require significant engineering time for:

Selecting dependencies
Integrating libraries
Maintaining versions
Building missing components
Supporting legacy code
Training new developers

On the other hand, a commercial framework introduces licensing costs.

So the useful comparison is:

License cost
+
Development time
+
Maintenance
+
Upgrade effort
+
Support

That's a much better way to evaluate a framework.

One more option: use both

If you already have a React application, you don't necessarily need to rewrite it.

ReExt can bridge React and Ext JS components, allowing teams to introduce Ext JS components selectively.

For example, you could keep React for most of the application and use an Ext JS data grid for one particularly data-heavy screen.

That kind of incremental approach can make more sense than a full migration.

My conclusion

I wouldn't choose a framework based purely on benchmark numbers or popularity.

I'd start with the workload.

Dynamic consumer UI? React is a strong choice.

Large structured enterprise team? Angular can make sense.

Simple developer experience? Vue is worth considering.

Complex data-heavy enterprise application? Ext JS deserves a serious evaluation.

The framework that looks best on paper isn't necessarily the one that will cost the least to maintain five years from now.

What's your biggest factor when choosing a framework: performance, ecosystem, developer experience, or long-term maintenance?

Open-Source vs Commercial Front-End Frameworks: My Enterprise Evaluation Checklist

Vishal Porwal — Thu, 06 Aug 2026 06:56:31 +0000

Whenever we evaluate front-end frameworks, we score them based on more than just popularity.

Here's the checklist.

Open-source strengths
Huge communities
Flexible architecture
Large plugin ecosystem
Zero licensing fees
Faster experimentation

Commercial strengths
Enterprise UI components
Vendor support
Stable release cycles
Long-term maintenance
Better governance
Security-focused development
Questions I ask
How long will this application live?
How many developers will maintain it?
How much custom UI needs to be built?
Are compliance requirements important?
Do we need enterprise support?

If the project is a startup product, open-source usually wins.

If it's a large enterprise platform with complex dashboards, reporting, and long-term maintenance, integrated solutions like Ext JS become worth evaluating.

The framework itself matters less than how much engineering effort it saves over the lifetime of the project.

How does your team decide between open-source and commercial frameworks?

How I Evaluate Front-End Frameworks for Enterprise Projects (Not Just React vs Vue)

Vishal Porwal — Wed, 05 Aug 2026 09:49:24 +0000

Choosing a front-end framework has become more complicated than ever.

Here's the checklist I now use before starting a project.

Application size

Small app?

Medium SaaS?

Large enterprise dashboard?

The answer changes everything.

Component requirements

Do we need:

Data grids
Charts
Forms
Trees
Scheduling
Accessibility
Responsive layouts

If yes, building everything ourselves can become expensive.

Long-term maintenance

I also estimate:

dependency count
upgrade effort
onboarding time
documentation quality
vendor/community support

Performance
Performance isn't only bundle size.

Large enterprise applications often care more about rendering thousands of records efficiently.

My takeaway

React, Vue, Angular, and Svelte are all excellent choices.

For enterprise applications that require extensive built-in UI components and data handling, frameworks like Ext JS are also worth evaluating because they reduce the amount of infrastructure teams need to assemble.

Ultimately, framework selection should optimize long-term productivity—not just developer trends.

How does your team evaluate frameworks before starting a new project?

Why Large Data Handling Is the JavaScript Problem

Vishal Porwal — Wed, 05 Aug 2026 06:07:51 +0000

Large data handling rarely announces itself as a problem.
It creeps in. The grid that performed fine in development starts to slow down in staging. The horizontal scroll that worked smoothly with fifty columns becomes choppy with two hundred. The initial render that took milliseconds in testing takes seconds in production — because the dataset that existed during development and the dataset that exists six months after launch are not the same dataset.
By the time most JavaScript teams recognize large data handling as a problem they need to solve, they are already solving it under pressure. In production. With users waiting.
The underestimation happens at the beginning. Teams build for the data they have, not the data they will eventually have. They optimize for the use cases that are obvious in planning, not the ones that emerge after real users start doing real work with the application. And they discover — usually too late — that the approaches that work at smaller scale stop working when the data grows.
This is one of the most consistent patterns in enterprise JavaScript development. And it is the focus of one of the sessions at JS Days 2026.

Sencha's free virtual JavaScript conference, scheduled for September 16–17, 2026, brings together developers, software architects, and engineering leaders to explore the technologies and architectural decisions shaping modern web applications.
For JavaScript developers working on data-intensive applications, this year's program offers several sessions worth examining closely.
The Session: Techniques for Large Data Handling in Ext JS
Rafael Méndez, Sencha MVP, presents a dedicated session on practical techniques for managing large datasets efficiently in Ext JS applications.
The discussion covers the rendering strategies, architectural patterns, and implementation approaches that allow applications to stay responsive when handling the kind of data volumes common in analytics dashboards, financial reporting tools, and real-time monitoring systems.
This is not a session about what large data handling could look like in theory. It is a session about what it actually looks like when an application needs to manage hundreds of columns, thousands of rows, and users who expect the interface to stay responsive throughout.
For JavaScript developers responsible for applications where data complexity has grown — or will grow — beyond what the original architecture anticipated, this session provides the kind of practical guidance that only comes from real production experience.

Why Teams Keep Getting This Wrong
The most common large data handling mistake in JavaScript development is not a technical mistake. It is a planning mistake.
Teams design for average cases and discover that enterprise users do not operate in average cases. They design for the dataset size that exists at launch and discover that enterprise datasets grow faster than anyone planned for. They design for the use cases that were specified in requirements and discover that real users find use cases that were never specified.

By the time these discoveries happen, the application is already in production. The fix is no longer a design decision — it is an incident response.

The architectural decisions that prevent this pattern from repeating — buffered column rendering, efficient row virtualization, patterns that keep performance proportional to what is visible rather than what exists — are the ones that Rafael Méndez's session is built around.
For developers making these decisions now, before the data grows, the session provides a practical framework for getting them right the first time.

When AI Makes Large Data Handling Harder
Large data handling is becoming more complex, not less — and AI integration is one of the reasons why.

When intelligent processing needs to operate alongside the filtering, sorting, editing, and rendering capabilities that enterprise users expect from a production data grid, the performance challenges multiply. AI-driven grading logic, intelligent sorting, and real-time classification all add processing overhead to interfaces that are already managing significant data volumes.

Andres Villalba, Sales Engineer at Sencha, presents a session on building AI-driven grading logic directly with the JavaScript DataGrid — examining how intelligent processing can be layered into data-heavy enterprise interfaces without sacrificing the performance and reliability those interfaces require in production.
For JavaScript developers navigating the intersection of large data handling and AI integration — this session addresses the combination that most teams are not yet prepared for.

Enterprise Dashboards Built for Real Data Complexity
Large data handling challenges are not abstract. They are specific to industries, use cases, and the real operational requirements that enterprise applications need to serve.

Wemerson Januario, Developer Advocate at Sencha, walks through how Ext JS dashboards handle the specific data requirements of supply chain and fleet management applications — two domains where real-time data visibility, large datasets, and complex operational logic are standard requirements rather than edge cases.
Rather than presenting generic approaches to data-heavy interfaces, the session examines the implementation decisions that matter when the data complexity is real, and the users depending on the interface cannot afford for it to slow down.
Insights From Production Applications
Some of the most instructive lessons in software engineering emerge only after a product reaches production.
César Martell, Software Developer, presents a detailed walkthrough of a real recruitment application at JS Days 2026, examining the technical decisions made throughout the development lifecycle and the lessons learned along the way.
For JavaScript developers making architectural choices for growing applications — including applications where data volumes are expected to grow significantly after launch — these observations often provide the most directly applicable reference points available.
A Second Day Focused on Interactive Learning
Day 2 of the conference shifts toward AI-driven data logic, large data handling techniques, and safe AI analytics deployment — with expanded time for community discussion and interactive exchange.
Beyond structured sessions, the day includes live Q&A and open discussion rooms, giving attendees direct access to speakers and other members of the JavaScript community who are working through the same large data challenges.

These conversations often extend beyond individual technologies to address broader engineering topics — architecture, maintainability, performance, and the practical realities of building JavaScript applications that need to stay responsive as data complexity grows.
Content That Remains Relevant Long After the Event
The sessions at JS Days 2026 most relevant to large data handling cover a wide range of disciplines and implementation approaches. Collectively, they reflect a broader theme.
Large data handling is not a problem that gets solved once. It is a problem that evolves as applications mature, datasets grow, and user expectations rise. The architectural decisions, rendering strategies, and implementation patterns covered across these sessions are the ones that determine whether an application scales gracefully or struggles visibly under the weight of its own success.
Whether you are an experienced JavaScript architect, a frontend developer responsible for data-intensive interfaces, or an engineer who has already encountered large data performance problems in production — the program is designed to deliver technical insights that remain applicable long after the conference concludes.

Event Details
JS Days 2026
Dates: September 16–17, 2026
Format: Fully virtual
Cost: Free

Registration requires only a short online form at jsdays.io. Once registered, attendees receive agenda updates, speaker announcements, and event access information ahead of the conference.
JS Days 2026 is organized by Sencha, part of Idera, Inc. — the team behind Ext JS, ReExt, and GXT. Sencha has helped organizations build secure, scalable, enterprise-grade JavaScript applications for decades across industries including financial services, healthcare, manufacturing, government, and enterprise software. Today, Sencha technologies are trusted by more than 2 million developers and 150,000+ organizations worldwide.