내 뇌는 나에게 거짓말을 해서 AI에 면역 시스템을 구축했습니다.

Your AI system needs 3-5 engineers to stay alive. Sentinel makes it self-sustaining. Every AI in production today degrades silently. Models drift. Prompts rot. Quality erodes 0.3% per day — invisible on any dashboard, devastating over weeks. By the time a human notices, thousands of dollars are lost. Today's fix: hire engineers to monitor dashboards, debug degradation, and manually tweak prompts. That's 3-5 people per AI system. Permanently. It doesn't scale. Sentinel is different. Three lines of code. Your AI takes care of itself. from sentinel import Organism body = Organism("my-agent", autonomous=True) body.observe("conversion_rate", 0.34) That's it. Sentinel now: - Builds a personal baseline for every metric (not industry benchmarks — YOUR system's normal) - Detects silent drift days before any threshold-based alert would fire - Diagnoses WHY it's happening (model update? prompt rot? data poisoning?) - Heals itself without human intervention - Remembers what worked last time and applies it automatically - Predicts phase transitions before they happen No dashboards to watch. No thresholds to configure. No engineers to wake up at 3am. Deploy once. It lives. Datadog alerts when latency > 3000ms . But if your system normally runs at 200ms and slowly drifts to 800ms, that alert never fires. You've lost 4x performance and nobody noticed. Sentinel builds an EWMA baseline for every metric. 800ms in a system that normally does 200ms is a Z-score of 3+. Sentinel catches it immediately. Zero configuration. body = Organism("my-agent") # Just feed it numbers. It figures out the rest. body.observe("conversion_rate", 0.34) body.observe("satisfaction", 4.2) body.observe("latency_ms", 450) body.observe("error_rate", 0.02) # Ask how it's doing print(body.state) # "stable" print(body.pain) # 0.12 (0=perfect, 1=critical) print(body.feeling) # "Healthy. Minor fluctuations." Persistence detection: 3+ consecutive readings above ±1.5σ → warning. Above ±2.0σ → critical. Catches sustained degradation. Trend detection: 5+ monotonically rising/falling Z-scores → warning. Catches slow drift that no single reading would trigger. Velocity detection: Jump of >2.5σ between readings → critical. Catches sudden failures. Before any complex system collapses, two mathematical signatures appear: variance increases and autocorrelation increases. This is physics, not heuristics. It happens before market crashes, before ecosystem collapses, and before AI systems fail. # Check if a phase transition is coming warning = body.immune.baselines["conversion_rate"].critical_slowing_down() print(warning) # { # "variance_trend": "rising", # "autocorrelation_trend": "rising", # "early_warning_score": 0.72, # "phase_transition_risk": "high" # } Single metrics lie. Patterns don't. When latency rises AND satisfaction drops AND response length increases simultaneously, that's a specific syndrome: model provider update. # Sentinel diagnoses WHAT is wrong, not just THAT something is wrong diagnosis = body.diagnosis # [SyndromeMatch( # syndrome="model_update_drift", # match_score=0.83, # recommended_action="evaluate_model_version" # )] Built-in syndromes: model_update_drift , prompt_rot , data_poisoning , capacity_exhaustion , engagement_collapse . Add your own: from sentinel.core.correlation import Syndrome body.correlation.define_syndrome(Syndrome( name="seasonal_dip", conditions={"conversion": "falling", "traffic": "rising"}, description="Traffic up but conversion down — likely seasonal behavior shift", recommended_action="adjust_pricing_strategy" )) The AI doesn't just detect problems. It fixes them. body = Organism("my-agent", autonomous=True) # Register healing actions body.immune.on_warning("conversion_rate", lambda alert: switch_to_safe_prompt()) body.immune.on_critical("conversion_rate", lambda alert: escalate_to_human()) # Or let the organism decide based on past experience body.autonomous = True # It heals itself using memory of what worked before Every subsystem communicates through a central event bus. When the immune system detects drift: - Endocrine system adjusts internal parameters (LLM temperature drops, caution increases) - Memory system records the event (for future pattern matching) - Respiratory system reduces compute budget (conserve resources under stress) - State machine evaluates lifecycle transition (STABLE → STRESSED) All simultaneously. All automatic. # Subscribe to any event in the organism body.bus.on("immune.drift", lambda e: print(f"Drift detected: {e.data}")) body.bus.on("organism.state_change", lambda e: print(f"State: {e.data}")) body.bus.on("skin.blocked", lambda e: print(f"Attack blocked: {e.data}")) No flapping between states. Real lifecycle transitions with minimum durations: NASCENT → STABLE → THRIVING ↓ ↓ STRESSED ← ← ← ↓ SICK → HEALING → RECOVERING → STABLE ↓ HIBERNATING (safe shutdown, state preserved) STABLE → STRESSED requires 3 consecutive ticks above threshold. STRESSED → STABLE requires 5 ticks below. The organism