Cloud Based Coding Agents

Decentra writes, tests, and executes code entirely within cloud-based DVM sandboxes, without relying on local dependencies.

Objective: Cloud-Native Code Authoring and Execution

Enable Decentra to create, validate, and run code entirely within cloud-based DVM sandboxes. This removes the need for local development environments while ensuring secure, isolated execution for any codebase or workload.

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Cloud-Based Development with Decentra

Decentra operates fully within cloud sandboxes, allowing code to be authored, tested, and executed without relying on local machines, dependency installation, or system configuration. DVM sandboxes provide isolated runtime environments where code can be executed safely, iterated on rapidly, and validated without impacting user devices or shared infrastructure.

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Role of DVM Sandboxes

DVM sandboxes act as ready-to-use cloud execution environments. They are preconfigured and immediately available, allowing Decentra to run code as soon as it is generated. There is no need to manage dependencies, operating system setup, or runtime conflicts. Each sandbox is fully isolated, ensuring clean execution, predictable behavior, and no cross-contamination between runs. This model supports any programming language, framework, or library without local constraints.

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Why Cloud Execution Matters

Without sandboxed cloud execution, Decentra would be limited to local runtimes or constrained execution contexts, increasing setup complexity and security risk. With DVM sandboxes, Decentra can:

• Write and run code anywhere Generate and execute code without relying on local environments or user-side configuration.

• Execute safely by default Run untrusted or experimental code inside isolated sandboxes with no impact on host systems.

• Support any technology stack Work across languages, frameworks, and libraries without requiring local installation.

• Iterate at high speed Test changes immediately without environment preparation or cleanup overhead.

• Scale execution horizontally Run multiple code executions in parallel using independent sandbox instances.

This enables a fully cloud-native development model where code creation, testing, and execution are decoupled from local infrastructure.

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Practical Applications

Code Generation & Validation

Decentra can generate code based on user requirements, execute it inside a sandbox, and validate behavior immediately—without requiring users to prepare development environments.

Code Analysis & Review

Existing codebases can be executed and inspected in sandboxes to verify functionality, detect issues, and analyze runtime behavior independently of the original environment.

Automated Testing

Test suites can be generated and executed in isolated sandboxes, ensuring consistent runtime conditions and preventing interference between parallel test runs.

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Scenario: End-to-End Cloud Code Execution

Decentra receives a request to build a data processing script. It generates Python logic, provisions a DVM sandbox, uploads the code, executes it against sample data, validates the output, and returns the results—all without requiring any local setup, dependencies, or configuration from the user.

Implementation: Cloud-Based Code Execution

1

Generate Code

Agent generates code based on requirements using LLM.

2

Create Sandbox

Agent creates a sandbox with appropriate resources for the code.

3

Upload Code

Agent uploads generated code files to the sandbox.

4

Install Dependencies

Agent installs required dependencies in the sandbox environment.

5

Execute Code

Agent runs the code with test inputs in the isolated sandbox.

6

Validate Output

Agent checks execution results and validates correctness.

7

Provide Results

Agent returns execution results and validated code to the user.

Example (TypeScript)

example.ts

import SandboxSDK from '@avmcodes/sandbox-sdk';

const client = new SandboxSDK({
  apiKey: process.env['SANDBOX_SDK_API_KEY'],
});

async function executeGeneratedCode(code: string, dependencies: string[]) {
  // Create sandbox for code execution
  const sandbox = await client.sandboxes.create({
    name: 'Code Execution Sandbox',
    resources: {
      cpus: 2,
      memory: 1024,
    },
  });

  try {
    // Install dependencies if needed
    if (dependencies.length > 0) {
      await client.sandboxes.execute(sandbox.id, {
        command: `pip install ${dependencies.join(' ')}`,
        timeout: 120,
      });
    }

    // Write code to file in sandbox
    await client.sandboxes.execute(sandbox.id, {
      command: `cat > /tmp/script.py << 'EOF'\n${code}\nEOF`,
    });

    // Execute the code
    const result = await client.sandboxes.execute(sandbox.id, {
      command: 'python /tmp/script.py',
      timeout: 300,
    });

    // Return execution results
    return {
      success: result.status === 'completed' && result.exit_code === 0,
      stdout: result.stdout,
      stderr: result.stderr,
      exitCode: result.exit_code,
    };
  } finally {
    // Clean up sandbox
    // Sandbox cleanup would happen here
  }
}

Next Steps

• Expand language and framework coverage Enable DVM sandboxes to support a broader range of programming languages and runtime frameworks through configurable execution profiles.

• Introduce code upload and lifecycle APIs Provide interfaces for uploading, organizing, versioning, and managing code artifacts used during sandbox execution.

• Add automated dependency resolution Detect required libraries at runtime and handle installation automatically within sandbox environments before execution.

• Implement execution result caching and sharing Store and reuse execution outputs, logs, and artifacts to accelerate repeated runs and allow results to be shared across workflows or teams.