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Annotate agent functions with policy metadata. The decorator attaches budget, compliance, and KPI configuration to the function for the harness to use at runtime.

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Signature

def agent(
    budget: Optional[float] = None,
    compliance: Optional[str] = None,
    kpi_weights: Optional[dict[str, float]] = None,
    kpi_targets: Optional[dict[str, float]] = None,
    max_tool_calls: Optional[int] = None,
)

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Parameters

ParameterTypeDefaultDescription
budgetfloat | NoneNoneMax USD for this agent
compliancestr | NoneNoneCompliance mode
kpi_weightsdict | NoneNoneKPI dimension weights
kpi_targetsdict | NoneNoneKPI dimension targets
max_tool_callsint | NoneNoneMax tool/function calls

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Usage

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Basic

@cascadeflow.agent(budget=0.20)
async def my_agent(query: str):
    return await llm.complete(query)

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With compliance

@cascadeflow.agent(budget=0.50, compliance="gdpr")
async def eu_agent(query: str):
    return await llm.complete(query)

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With KPI weights

@cascadeflow.agent(
    budget=1.00,
    kpi_weights={"quality": 0.8, "cost": 0.2},
    kpi_targets={"quality": 0.9},
)
async def premium_agent(query: str):
    return await llm.complete(query)

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Multiple agents with different policies

@cascadeflow.agent(budget=0.10, kpi_weights={"cost": 0.9, "quality": 0.1})
async def triage_agent(query: str):
    return await llm.complete(query)

@cascadeflow.agent(budget=2.00, kpi_weights={"quality": 0.9, "cost": 0.1})
async def analysis_agent(query: str):
    return await llm.complete(query)

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Notes

  • The decorator does not wrap or modify the function’s execution. It attaches metadata that the harness reads at runtime.
  • Works with both sync and async functions.
  • Requires init() to have been called for the metadata to take effect.
  • Can be combined with run() — the run’s constraints are checked in addition to the decorator’s.