Structured deliverable

from typing import List, Literal

from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from pydantic import BaseModel, Field


class Decision(BaseModel):
    call: Literal["BUY", "HOLD", "PASS"] = Field(..., description="The committee decision")
    conviction: Literal["low", "medium", "high"] = Field(..., description="Confidence in the call")
    allocation_usd: float = Field(..., description="Dollar allocation, 0 if not BUY")
    rationale: str = Field(..., description="Why, referencing the analyst inputs")
    citations: List[str] = Field(..., description="Sources and prior memos used")


chair = Agent(
    name="Committee Chair",
    model=OpenAIResponses(id="gpt-5.5"),
    output_schema=Decision,
    instructions=(
        "Synthesize the analyst inputs into a decision. Every BUY needs a "
        "dollar amount. Every decision must reference at least one risk."
    ),
)


def briefing(*analyst_outputs: str) -> str:
    return "Analyst inputs:\n\n" + "\n\n".join(analyst_outputs)


result = chair.run(briefing(market, fundamentals, technicals, risk)).content
# Decision(call='BUY', conviction='high', allocation_usd=2_000_000.0,
#          rationale='Momentum and fundamentals align; sized within the
#                      sector cap the Risk Officer set.',
#          citations=['memo:NVDA-2024Q3', 'research:semiconductors'])