Building Custom AI Agents with JavaScript, React, and ReExt

AI coding tools are evolving beyond code completion. Developers increasingly want systems that can understand project context, interact with tools, coordinate multi-step tasks, and report their results in a way that supports real engineering workflows.

JavaScript and React provide a practical foundation for building these experiences. When an application needs richer enterprise UI components, Sencha ReExt offers a way to integrate Ext JS components into a React application.

This guide explores the architecture of a custom AI coding agent, the roles of MCP and ACP, and how ReExt can support a more capable developer interface.

1. What Is a Custom AI Coding Agent?
A traditional coding assistant might explain a function or generate a code snippet. A custom AI agent can be designed to coordinate a broader workflow.

Depending on its implementation and permissions, it might:

• Inspect project files and configuration.
• Retrieve relevant documentation.
• Invoke approved development tools.
• Analyze test results and diagnostic information.
• Suggest or perform code changes.
• Verify whether a task meets its acceptance criteria.

The difference is the execution workflow. The agent needs an orchestration layer that manages context, tool calls, errors, and results—not just a function that sends a prompt to a model.

1. Why Use JavaScript and React? JavaScript is widely used in web applications and developer tooling. React provides a component-based model for building interactive interfaces. A custom AI coding agent could use React to display:

• A task input and conversation history.
• Current execution status.
• Tool activity and relevant logs.
• Diagnostic results.
• File-level findings.

Approval controls for sensitive operations.
Separating these areas into reusable components makes the interface easier to test and maintain.

3. Understanding MCP and ACP
Two protocols that may be relevant to a custom AI agent are MCP and ACP.

Model Context Protocol (MCP)
MCP provides a standardized approach for connecting compatible AI applications to tools and contextual resources.
Depending on the integration, an agent might use approved tools to access documentation, project information, or external services. Tool permissions and data access should be explicitly controlled.

Agent Client Protocol (ACP)
ACP supports communication between compatible agent clients and agent backends.
This separation can help a client interface work with supported agent implementations without embedding every backend-specific detail into the UI.
MCP and ACP solve different integration problems. Choose them according to the tools, agent runtimes, and client architecture your application requires.

4. Designing the React Interface
A useful AI coding agent interface should make the workflow understandable.

Consider dividing the application into these components:

• TaskInput — accepts the developer's request.
• ConversationPanel — displays messages and explanations.
• ExecutionStatus — shows the current task state.
• ToolActivity — displays relevant tool calls and outcomes.
• DiagnosticsGrid — presents structured findings.
• ApprovalPanel — supports review of actions that need authorization.

These are illustrative component names, not APIs provided by a specific library.
A chat interface is useful for explanations, but a structured grid can be better when developers need to compare many findings or filter results by severity, file, or status.

5. What Is ReExt?
ReExt connects the React development environment with Sencha Ext JS components. Ext JS provides a broad set of JavaScript UI components, including data grids, forms, and other building blocks for enterprise applications.
ReExt is particularly worth evaluating when a React-based AI tool needs a more comprehensive UI than a basic collection of custom components.
For example, an AI coding agent might need to display:

• File-level diagnostics.
• Test or validation results.
• Tool execution history.
• Structured code-analysis findings.
• Task status and other operational information.

An Ext JS data grid can be a suitable option for displaying structured results when its capabilities match the requirements. A lightweight React Grid component may be sufficient for simpler needs.

The UI library does not provide the agent's reasoning or tool execution. Those responsibilities belong to the agent runtime and orchestration layer.

For additional context, see Sencha's introduction to ReExt.

6. ReExt Coding Style: Integrating Ext JS Components into React
ReExt allows developers to configure Ext JS components from React. Its component wrapper uses properties such as xtype, config, and event handlers to connect the component with the React application.

Here is a simplified example based on the documented ReExt component pattern:

import { ReExt } from "@gusmano/reext";

export default function AgentActionButton({ onRun }) {
return (
xtype="button"
config={{
text: "Run Analysis",
width: 140
}}
onClick={onRun}
/>
);
}

This illustrates the general integration style: React controls where the component appears, while the xtype and config properties describe the Ext JS component and its configuration.

Check the current ReExt documentation for the supported import syntax, event signatures, component properties, and initialization requirements for your installed version.

Keeping the code maintainable
A useful coding style separates responsibilities:

• Keep React components focused on the interface.
• Put agent execution and tool orchestration in dedicated modules.
• Keep Ext JS component configuration organized and reusable.
• Handle errors and loading states explicitly.
• Validate tool results before displaying them.
• Avoid allowing UI events alone to authorize sensitive operations. This structure helps prevent the interface from becoming tightly coupled to the agent's execution logic.

*7. Displaying Agent Results With a React Grid Component
*
AI agents often return structured information that is difficult to review in a conversation panel.
For example, a code-analysis workflow might produce a list of findings with file paths, categories, severity levels, and review status.
A grid could display columns such as:

An Ext JS grid through ReExt may be worth evaluating for a data-intensive enterprise interface. A smaller application may be better served by a lightweight React grid component.

Choose based on required functionality, data volume, accessibility, performance, licensing, and maintenance—not merely the number of available features.

8. Security and Reliability
An AI coding agent may access sensitive project information or invoke tools that change files and execute commands. Those capabilities require safeguards.

Consider the following:

• Restrict access to files and services.
• Validate tool inputs and outputs.
• Protect credentials and sensitive information.
• Require approval for consequential operations.
• Apply execution limits and bounded retries.
• Record relevant events for troubleshooting.
• Treat project files and external tool responses as untrusted input.
• Verify results before reporting a task as complete.

The React interface can make these controls visible, but authorization must also be enforced by the underlying execution layer.

Conclusion
Building a custom AI coding agent requires a coordinated architecture for model interaction, tool access, orchestration, and user experience.

JavaScript and React provide a foundation for the application. MCP and ACP can support different integration needs, while ReExt makes Ext JS UI components available within React applications.

For interfaces that need structured diagnostics and data-heavy views, an Ext JS grid may be worth evaluating alongside other React Grid component options.

Start with one clearly defined workflow, measure its reliability, and expand the agent's capabilities only when the execution and security controls are working as intended.

Further reading: Building Custom AI Agents with JavaScript, React, and ReExt.