Supervisor-Worker Agent Pattern for Cost Optimization

The economics

Frontier models (GPT-5, Claude Opus 4) cost 15-30x cheap models (Haiku 4.5, Gemini Flash, GPT-4o-mini). Most agent steps don't need frontier quality -- summarization, extraction, formatting, classification all run fine on cheap models. If the frontier plans and dispatches, the cheap models execute, you pay frontier rates only for the hard thinking.

Step 1: Define the supervisor interface

Supervisor receives a goal, emits a list of sub-tasks with the right model for each.

from typing import Literal
from pydantic import BaseModel

class SubTask(BaseModel):
    id: str
    tool: Literal["summarize", "extract_entities", "classify", "draft_response", "final_synthesis"]
    input: str
    model_tier: Literal["cheap", "mid", "frontier"]
    rationale: str

class Plan(BaseModel):
    tasks: list[SubTask]

SUPERVISOR_PROMPT = """
You plan agent workflows. Given a user goal, emit a list of sub-tasks.

Available tools and their cost tiers:
- summarize (cheap): collapse text to N sentences
- extract_entities (cheap): pull structured facts
- classify (cheap): assign a label
- draft_response (mid): write a candidate response
- final_synthesis (frontier): combine, judge, polish

Constraints:
- Use the cheapest tier that plausibly works
- Reserve frontier for steps requiring judgment or high-stakes output
- Reserve mid for drafting
- Default to cheap for extraction/classification/summarization
"""