Guide To the Maestro

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Considerations

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<aside> 💡 FQM Stands for the model identifier which is “Function Quest Maestro”

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"From Function Quest Logic to Symphony Mastery: Building an RL-Powered Conductor"

🎯 Executive Summary

This document outlines the development strategy for creating a Conductor Orchestrator Model - a Reinforcement Learning agent that orchestrates the Symphony IDE's agentic model ensemble. The approach follows a three-step methodology: implementing Function Quest logic, testing and fine-tuning on Function Quest challenges, then deploying to real-world Symphony orchestration.

🧠 Core Architecture Philosophy

Function Quest as Training Foundation

The Conductor model will work using Function Quest mechanics where:

• Each Symphony Agentic Model = A "λ" **Function** in Function Quest
• Model outputs (enhanced prompt, backlog.csv, plan.json, etc.) = Function return values
• Orchestration decisions = Function call sequences
• Project completion = Level victory condition

Reinforcement Learning Foundation

The model will be an RL model (specific type TBD) that learns through:

• State representation: Current project state, available models, intermediate outputs
• Action space: Which Symphony model to invoke, with what parameters, in what sequence
• Reward function: Based on successful task completion and output quality
• Policy learning: Optimal orchestration strategies through Function Quest training