Harness Modes

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Modes

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off

cascadeflow.init(mode="off")

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observe

cascadeflow.init(mode="observe")

import { CascadeAgent } from '@cascadeflow/core';

// TypeScript uses CascadeAgent with quality config for observe-like behavior
const agent = new CascadeAgent({
  models: [
    { name: 'gpt-4o-mini', provider: 'openai', cost: 0.000375 },
    { name: 'gpt-4o', provider: 'openai', cost: 0.00625 },
  ],
});

• Initial production rollout to validate metrics before enforcing
• Shadow-mode testing to understand what the harness would do
• Cost and usage analytics without affecting agent behavior

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enforce

cascadeflow.init(mode="enforce")

• You have validated metrics in observe mode
• You need hard budget caps to prevent runaway costs
• Compliance requirements mandate model gating

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Rollout Guidance

1. Deploy with observe — No risk to agent behavior. Collect metrics, review decision traces, validate that the harness sees what you expect.
2. Review traces — Check that compliance allowlists, budget calculations, and KPI scoring match your expectations.
3. Switch to enforce — Once validated, change the mode. The harness will now enforce constraints.
4. Monitor — Use session.summary() and session.trace() to monitor enforcement in production.

import os

# Environment-driven mode selection
mode = os.getenv("CASCADEFLOW_MODE", "observe")
cascadeflow.init(mode=mode)

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Mode Behavior Matrix