DEV Community

Malik Abualzait
Malik Abualzait

Posted on

Code Your AI Superpower: Building a Robust Framework from Scratch

#ai #tech #programming #tutorial

Building Framework

Building a Framework for AI Behavior

The Problem of Duplicated Configuration

If you've ever run the same app in multiple environments, you know the pain of duplicated configuration. Agent swarms have the same problem: the moment you try multiple orchestrators, your agent definitions start living in different formats. Prompts drift. Model settings drift. A "small behavior tweak" turns into archaeology across repos.

Why AI Behavior Isn't Code

AI behavior isn't code. Prompts aren't functions. They change too often and too experimentally to be hard-wired into orchestrator code. This is where a centralized configuration management system comes in – think of it as a shared brain for your agents, storing all the knowledge they need to operate.

Introducing AI Configs

LaunchDarkly AI Configs lets you treat agent definitions like shared configuration instead. Define them once, store them centrally, and let any orchestrator fetch them. Update a prompt or model setting in the LaunchDarkly UI, and the new version rolls out without a redeploy.

Benefits of Centralized Configuration Management

• Simplified Maintenance: No more duplicated code or manual updates across multiple environments
• Version Control: Track changes to your agent definitions and roll back if needed
• Collaboration: Let teams work together on AI behavior without version conflicts
• Scalability: As your use cases grow, so does your configuration management system

How to Implement AI Configs in Your Project

Step 1: Define Agent Definitions

Define your agent definitions as JSON objects, including prompts and model settings. For example:

{
  "name": "my_agent",
  "prompts": [
    {
      "text": "What is the capital of France?",
      "model_id": "french-model"
    }
  ]
}
Enter fullscreen mode Exit fullscreen mode

Step 2: Store Definitions in AI Configs

Store your agent definitions in LaunchDarkly's central repository. This allows you to manage and version-control your agent configurations.

Code Example: Fetching Agent Definitions from AI Configs 

const ldClient = require('@launchdarklab/ldclient-js');
const fetch = require('node-fetch');

async function getAgentDefinitions() {
  const client = await ldClient.init({
    // Your LaunchDarkly project settings here
  });
  const definitions = await client.getFeatureManager().getVariation('my_agent', { key: 'value' });
  return JSON.parse(definitions);
}
Enter fullscreen mode Exit fullscreen mode

Best Practices for Implementing AI Configs

• Keep Definitions Up-to-Date: Regularly review and update your agent definitions to reflect changes in your model or prompt behavior
• Use a Version Control System: Store your agent definitions in a version control system, like Git, to track changes and collaborate with teams

Conclusion

Building a framework for AI behavior is crucial when working with multiple environments and orchestrators. By using a centralized configuration management system, you can simplify maintenance, track versions, and scale your configurations as needed. Remember to keep your definitions up-to-date and use a version control system to collaborate with your team. With these best practices in mind, you'll be able to build robust AI systems that adapt quickly to changing requirements.

By Malik Abualzait

Top comments (0)