How It Works

Query → Domain Detection → Try Draft Model → Quality Check
                                                  │
                                          Pass ───┘─── Fail
                                           │            │
                                        Return      Escalate to
                                        Result      Verifier Model

Agent Step → Harness Decision → allow / switch_model / deny_tool / stop
                 │
                 ├── Check budget remaining
                 ├── Check compliance allowlist
                 ├── Score KPI dimensions
                 ├── Check tool call cap
                 ├── Check latency cap
                 └── Check energy cap

HarnessConfig(
    mode="enforce",           # off | observe | enforce
    budget=0.50,              # Max USD for the run
    max_tool_calls=10,        # Max tool/function calls
    max_latency_ms=5000.0,    # Max wall-clock ms per call
    max_energy=100.0,         # Max energy units
    compliance="gdpr",        # gdpr | hipaa | pci | strict
    kpi_weights={"quality": 0.6, "cost": 0.3, "latency": 0.1},
    kpi_targets={"quality": 0.9},
)

import cascadeflow
from cascadeflow import CascadeAgent, ModelConfig

# Harness: enforce budget and compliance
cascadeflow.init(mode="enforce")

# Cascade: speculative model routing
agent = CascadeAgent(models=[
    ModelConfig(name="gpt-4o-mini", provider="openai", cost=0.000375),
    ModelConfig(name="gpt-4o", provider="openai", cost=0.00625),
])

with cascadeflow.run(budget=1.00) as session:
    result = await agent.run("Analyze this contract for GDPR compliance")
    print(session.summary())