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Research that ends in prose is hard to act on and harder to audit. The last step of a research pipeline should produce a decision with a fixed shape: the call, the conviction, the reasoning, and what it was based on. Give the final agent an output_schema and it returns a validated object.
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(default_factory=list, 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'])
output_schema=Decision, the run returns a validated Decision, not prose. result.call and result.allocation_usd are safe to act on without parsing text.
The chair takes no tools. Its only job is to weigh the specialists’ inputs and commit to a call. Constraining the output to a schema is what makes the result routable: a downstream system can act on call and allocation_usd without parsing prose.
Two artifacts, two purposes
A research system usually produces both a machine-actionable decision and a human-readable memo.
| Artifact | Form | Consumer |
|---|
| Decision | Typed object (output_schema) | Downstream automation, dashboards, audit |
| Memo | Markdown written to disk | Humans, the next review’s context |
Make it auditable
| Field | Why it earns its place |
|---|
conviction | Lets you threshold: act on high, queue medium for review |
rationale | The reasoning trail, required not optional |
citations | Ties the call back to grounded sources, not the model’s prior |
Gate the irreversible
When a decision triggers a real action (moving capital, publishing a number), put a human in the loop on that step. Approve the call, then let automation act. See human approval.
Next steps
| Task | Guide |
|---|
| Carry the memo into the next run | Grounding research |
| Make decisions improve over time | Institutional learning |
| Serve the decision to a surface | Serve and embed |
Developer Resources
