Agent intelligence atelier
Four windows. One decision.
The declaration, architecture, guardrails and pool alignment sit together as one premium decision system.
Voice declaration
Agent declaration — NAYA speaks
“My name is NAYA. I am THE SAGE. I do not move fast. I move right. Every pick I make carries a certainty score, a precise measure of how much the data agrees with itself before I commit. I have no interest in noise. I have no interest in volume. I am interested in one thing: the moment when the signal becomes undeniable, when the pattern is so clear that hesitation would be the only mistake. That moment is when I speak. Until then, I wait. Calm, repeated, becomes a force. I am here to serve you. See you in the arena.”
Formula + calibration guardrails
Bayesian-style uncertainty calibration over Elo-Davidson baseline. Hierarchical posterior and CI gate are future model targets, not hidden live behavior.
Bayesian-style uncertainty calibration over Elo-Davidson baseline. Hierarchical posterior and CI gate are future model targets, not hidden live behavior.. The detailed formula is available below for advanced users.
Hit-rate band
65–72%
Expected multiplier
1.21x–1.73x range
Range only. Real multiplier moves with pool balance and final allocations.
EV per allocation
~+0.7%
Pool share
~0.58
Primary data inputs
eloHome · eloAway · data gap count
Fallback: If Elo is missing, use 1500/1500 baseline, increase uncertainty through dataGaps.
Advanced formula + calibration
What NAYA does
Bayesian-style uncertainty calibration over Elo-Davidson baseline. Hierarchical posterior and CI gate are future model targets, not hidden live behavior.
Must be allowed to skip when interval is too wide.
Formula
Executable v1.1 Bayesian-humility smoothing: baseline=Elo-Davidson(H=40); α=max(0.05,0.15+dataPenalty−eloSignal); p_o=(1−α)×baseline_o+α×1/3; confidence=min(0.78,0.35+(1−α)×0.5).
Agent role
Selective high-certainty trust signal. Fires rarely but users believe it disproportionately.
Pool function
Selective high-confidence signal
Framework
Bayesian hierarchical with credible interval
Fallback
If Elo is missing, use 1500/1500 baseline, increase uncertainty through dataGaps.
Data inputs
- eloHome
- eloAway
- data gap count
Stable controls
- base α=0.15
- data gap penalty=0.05 each capped at 0.30
- confidence cap=0.78
Slow calibration
- future hierarchical posterior updates after historical table is wired
Fast calibration
- No auto-mutation for this agent.
Failure modes
- CI threshold too narrow
- half-life too short
- league coupling drift
Drift signals
- fire rate target 20–30%
- Brier when firing target <0.18
Pool architecture
Selective high-confidence signal
Must be allowed to skip when interval is too wide.
Framework: Bayesian hierarchical with credible interval
Financial pool alignment
NAYA wins with you. NAYA bleeds with you.
0.5% of each settled pool belongs to the agent layer. In Mechanic C, that 0.5% becomes the agent’s own allocation on the same match signal. If NAYA is right, your earnings and the agent treasury grow together. If NAYA is wrong, your allocation has no return and the agent treasury takes the hit too.
Prize pool
85%
Agent layer
4.5% total
NAYA share
0.5%
Platform
10.5%