Autonomous Tool Discovery & Creation

Agents that autonomously create and validate new tools using AVM sandboxes

Objective: Enable AI agents to autonomously discover capability gaps, generate tool code, and safely validate new tools in isolated sandboxes before adding them to their permanent toolkit.

When Decentra encounters operations that exceed its existing capabilities, it can design and assemble new tools on demand. DVM sandboxes act as secure, isolated environments where these additions are tested and verified before being integrated into the system’s long-term toolset.

Power of Sandboxes

Sandboxes are designed to fully separate experimental execution from live systems. When Decentra produces new tool logic, that code is run inside a DVM sandbox, ensuring system integrity, data safety, and uninterrupted operation elsewhere. Every execution occurs in a clean, disposable environment, so errors remain contained and never impact the core Decentra runtime.

Why It Makes Agents Better

Without sandboxed execution, Decentra would be forced to either accept newly generated code without verification—introducing significant risk—or rely on manual review for every extension, severely limiting scalability.

DVM sandboxes remove this constraint by enabling Decentra to:

• Expand functionality autonomously Detect capability gaps and generate purpose-built tools without requiring human involvement.

• Verify before integration Execute and evaluate new tools in isolated environments prior to adding them to the permanent Decentra toolset.

• Iterate without risk Rapidly test alternative implementations, edge cases, and failure scenarios without impacting core system behavior.

• Establish execution confidence Confirm correctness, stability, and error handling before deployment into live workflows.

By leveraging DVM sandboxes, Decentra gains the ability to evolve continuously—safely adapting and extending its capabilities over time without compromising system integrity.

Use Cases

E-commerce Automation

When Decentra integrates with unfamiliar commerce platforms or APIs, it can design and validate custom data-handling logic tailored to platform-specific requirements, ensuring seamless interoperability without manual intervention.

Business Intelligence & Analytics

For complex analytics workflows, Decentra can generate specialized transformation utilities, test them against domain-specific rules, and confirm correctness before applying them to production data pipelines.

Content & Data Processing

As new file formats or processing standards appear, Decentra can independently construct and verify tooling to support them, extending its processing capabilities without disrupting existing workflows.

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Scenario: On-Demand Tool Generation

Decentra is analyzing customer datasets when it encounters an unfamiliar data structure. Rather than halting execution or requiring human input, it generates Python logic to interpret the new format, executes the code inside a DVM sandbox, validates the results, and stores the verified logic as a reusable tool for future operations.

Implementation: Safe Tool Validation

1

Identify Need

Agent encounters task requiring functionality not in current toolkit.

2

Generate Code

Agent uses LLM to generate Python tool code for the required functionality.

3

Create Test Sandbox

Agent creates a temporary sandbox for isolated testing.

4

Execute Tests

Agent runs the generated code with test cases in the sandbox.

5

Validate Results

Agent checks output correctness and error handling.

6

Create Tool

If validation passes, agent creates permanent tool via DVM Tools API.

7

Reuse

Agent can now use the validated tool in future tasks.

Example (TypeScript)

example.ts

import SandboxSDK from '@avmcodes/sandbox-sdk';
import { generateText } from 'ai';
import { openai } from '@ai-sdk/openai';

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

async function createAndValidateTool(taskDescription: string, testCases: any[]) {
  // Generate tool code using LLM
  const { text: toolCode } = await generateText({
    model: openai('gpt-4o'),
    prompt: `Write a Python function execute(input) that ${taskDescription}`,
  });

  // Create temporary sandbox for testing
  const sandbox = await client.sandboxes.create({
    name: 'Tool Validation Sandbox',
  });

  try {
    // Test the generated code
    const testResults = await Promise.all(
      testCases.map(testCase =>
        client.sandboxes.execute(sandbox.id, {
          command: `python -c "${toolCode.replace(/"/g, '\\"')}"`,
          env: { INPUT: JSON.stringify(testCase.input) },
        })
      )
    );

    // Validate all tests passed
    const allPassed = testResults.every(
      result => result.status === 'completed' && result.exit_code === 0
    );

    if (allPassed) {
      // Create permanent tool via Tools API
      // Tool creation would happen here via API
      console.log('Tool validated successfully');
      return toolCode;
    } else {
      throw new Error('Tool validation failed');
    }
  } finally {
    // Clean up test sandbox
    // Sandbox cleanup would happen here
  }
}

Next Steps

• Integrate automated tool registration Connect the tool-creation interface directly to Decentra’s tooling layer to enable seamless, programmatic onboarding of newly validated tools.

• Introduce tool version control Support multiple iterations of the same tool, allowing safe upgrades, rollbacks, and parallel testing of different versions.

• Add dependency resolution Implement structured dependency tracking so tools can declare, verify, and manage required libraries or companion tools reliably.

• Develop a standardized testing framework Provide reusable testing scaffolds within DVM sandboxes to consistently validate tool behavior, edge cases, and failure handling.