Agentic.ts

// Pseudocode for integrating Self-Adapting into an Agent Framework

class SelfAdaptingAgent {
  private selfEditBuffer: Array<SelfEdit> = [];
  private sftThreshold: number = 10; // Train after 10 edits
  private model: LLM;

  async handleInteraction(input: string, context: any): Promise<string> {
    const response = await this.model.generateResponse(input, context);

    // ... (User interacts, provides feedback, or agent encounters a novel situation)

    // Generate Self-Edit
    const selfEdit = this.generateSelfEdit(input, response, context);
    this.selfEditBuffer.push(selfEdit);

    // Check if ready for SFT
    if (this.selfEditBuffer.length >= this.sftThreshold) {
      await this.performSFT();
      this.selfEditBuffer = []; // Clear buffer
    }

    return response;
  }

  private generateSelfEdit(input: string, output: string, context: any): SelfEdit {
    // Use LLM to analyze the interaction and suggest improvements
    // This is the core "self-generation" step
    const prompt = `
      You are an AI agent reflecting on a recent interaction.
      Input: ${input}
      Your Output: ${output}
      Context: ${JSON.stringify(context)}
      What is one way you could improve your response? Generate a corrected version or specify a hyperparameter change.
    `;
    const edit = await this.model.generate(prompt); // This generates the "self-edit"
    return { input, original_output: output, improved_output: edit };
  }

  private async performSFT() {
    // Use the selfEditBuffer to create a fine-tuning dataset
    const dataset = this.selfEditBuffer.map(edit => ({
      prompt: edit.input,
      completion: edit.improved_output
    }));

    // Perform lightweight SFT on the model
    await this.model.fineTune(dataset);

    // Optional: Log performance before/after for reward signal
    // await this.evaluatePerformance();
  }
}

interface SelfEdit {
  input: string;
  original_output: string;
  improved_output: string;
}