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Create Agentic.ts
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Agentic.ts

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// Pseudocode for integrating Self-Adapting into an Agent Framework
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class SelfAdaptingAgent {
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private selfEditBuffer: Array<SelfEdit> = [];
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private sftThreshold: number = 10; // Train after 10 edits
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private model: LLM;
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async handleInteraction(input: string, context: any): Promise<string> {
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const response = await this.model.generateResponse(input, context);
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// ... (User interacts, provides feedback, or agent encounters a novel situation)
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// Generate Self-Edit
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const selfEdit = this.generateSelfEdit(input, response, context);
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this.selfEditBuffer.push(selfEdit);
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// Check if ready for SFT
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if (this.selfEditBuffer.length >= this.sftThreshold) {
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await this.performSFT();
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this.selfEditBuffer = []; // Clear buffer
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}
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return response;
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}
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private generateSelfEdit(input: string, output: string, context: any): SelfEdit {
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// Use LLM to analyze the interaction and suggest improvements
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// This is the core "self-generation" step
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const prompt = `
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You are an AI agent reflecting on a recent interaction.
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Input: ${input}
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Your Output: ${output}
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Context: ${JSON.stringify(context)}
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What is one way you could improve your response? Generate a corrected version or specify a hyperparameter change.
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`;
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const edit = await this.model.generate(prompt); // This generates the "self-edit"
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return { input, original_output: output, improved_output: edit };
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}
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private async performSFT() {
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// Use the selfEditBuffer to create a fine-tuning dataset
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const dataset = this.selfEditBuffer.map(edit => ({
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prompt: edit.input,
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completion: edit.improved_output
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}));
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// Perform lightweight SFT on the model
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await this.model.fineTune(dataset);
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// Optional: Log performance before/after for reward signal
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// await this.evaluatePerformance();
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}
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}
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interface SelfEdit {
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input: string;
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original_output: string;
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improved_output: string;
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}

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