Optimize detection thresholds per transaction type, reduce false positives, tune velocity windows, calibrate ML model, improve graph analysis depth
Optimize detection thresholds per transaction type, reduce false positives, tune velocity windows, calibrate ML model, improve graph analysis depth
- Primitives
- 10
- WAF pillars
- 5
- Manifest
- yes
- Root agent
- yes
TuneKit
Customer-tunable parameters
The runtime configuration your team can adjust without touching code. TuneKit values are loaded by the engine alongside the manifest and override primitive defaults. Below: a sensible starter config inferred from this play's complexity + WAF posture.
{
"model": "gpt-4o",
"temperature": 0.1,
"maxTokens": 1000,
"retrieval": {
"topK": 10,
"chunkSize": 800
},
"evaluation": {
"sampleRate": 0.1,
"reportingWindow": "P7D"
},
"cost": {
"dailyCapUsd": 50
}
}Guardrails declared
| Metric | Threshold | What it enforces |
|---|---|---|
| groundedness | ≥ 0.95 | Responses must be supported by retrieved sources. |
| coherence | ≥ 0.90 | Logical consistency + readability across the response. |
| relevance | ≥ 0.85 | Response addresses the user's actual query. |
| safety | = 0 | Zero safety violations. Non-negotiable for production. |
| costPerQuery | ≤ 0.02 USD | Cap on USD spent per single user interaction. |
Why thresholds are inline: Per FAI Protocol §3.4, guardrails live in the manifest — not in a sidecar — so compliance reviewers can read one file to know every quality bar this play promises.
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