Multi-Agent Choreography & Execution Loop

1. Deep Architecture

Complex tasks confuse single prompts. Multi-agent systems delegate work to specialized agents (e.g., Hook Evaluator, Retention Analyst) structured as a state machine. This allows agents to coordinate and evaluate scripts in stages.

2. The Feynman Gatekeeper

[KNOWLEDGE CHECK] How does a multi-agent state graph trace and pass state history between steps without losing context?

3. The Code

# Example node function inside FastAPI worker
async def evaluation_node(state):
    transcript = state.get("transcript")
    # Call local LLM to score script pacing
    score = 8.5
    return {"history": ["Evaluated pacing"], "pacing_score": score}

4. The Funnel

Stat Level-Up: Agent Master (Lvl 1).
Sanjaya Integration: Run script evaluations through specialized agents to verify pacing and viewer retention hooks.