SystemOne Agents & The Jev Model

Re-architecting AI Agent Systems: Replacing slow, non-deterministic System 2 LLM routing with ultra-fast, typed System 1 decision primitives.

200x Faster

Evaluates complex decisions in 10-25ms instead of 1500ms+ token generation loops.

Typed Outputs

Direct JSON decision output with calibrated logit probabilities. No prose parsing.

400x Cheaper

Skips token decoding for classification, guardrails, routing & approval logic.

User Manual & Lab Instructions

de.ci.phe.red. Lab • Member Quick Start Guide

How to access, present, and run Jev decision models across the lab infrastructure.

1. Access Links

• Mobile 5G / Public Web: https://crypto-nn.tail7f9db4.ts.net
• Campus LAN: http://10.10.1.226:8085
• Tailscale VPN IP: http://100.84.228.44:8085

2. Hotkey Shortcuts

• Next / Prev: → / ← or Space
• Fullscreen Deck: Press F
• Switch Mode: Click Playground in top bar

3. Live Playground

Click Live Jev Playground tab to test custom JSON inputs, run real-time logit probability benchmark comparisons against System 2 LLMs.

4. Hybrid Architecture

Explore the Hybrid Architecture view for sub-agent routing diagrams, pre-execution guardrails, and post-validation patterns.

The Problem

The Enterprise Agent Overhead Problem

Generative LLMs are over-engineered for deterministic triage and routing decisions.

System 2 LLM Bottlenecks

High Latency: 1.5s - 4.5s overhead per decision step due to token generation.
Excessive Cost: $0.002 to $0.03 per simple routing call.
Parsing Failures: JSON formatting errors require expensive retries.
Non-deterministic: Probabilities are hidden inside autoregressive text output.

                                 The System 1 Solution
                                Sub-20ms Latency: Instant parallel logit calculation across decision classes.
100% Type Safe: Native structured output guarantees (Noul, Choice, Score).
Calibrated Probabilities: Mathematical expectation directly from logit weights.
99.5% Cost Reduction: Costs fractions of a penny ($0.000006 per evaluation).

                            

Theoretical Framework

Dual-Process AI Architecture

Inspired by Kahneman's Thinking, Fast and Slow cognitive model.

Attribute	System 1: Jev Decision Engine	System 2: Generative LLM
Cognitive Mode	Fast, Reflexive, Intuitive, Calibrated	Slow, Deliberate, Reasoning, Generative
Average Latency	10 ms – 25 ms	1,500 ms – 5,000 ms
Cost / 1M Requests	~$6.25	~$2,500.00
Output Format	Strict Typed Primitives (`Noul`, `Choice`, `Score`)	Freeform Autoregressive Text / Markdown
Primary Role	Guardrails, Sub-Agent Routing, Intent & SLA Triage	Deep Reasoning, Code Synthesis, Document Writing

Core Engine Primitives

Jev Decision Primitives

Eliminating text generation in favor of calibrated probability primitives.

Noul

Binary Assertion

Evaluates true/false boolean assertions directly on logit probabilities without generating text tokens.

Use Cases: Security guardrails, PII detection, prompt injection block, policy check.

noul("Is payload malicious?", state)

Choice

Categorical Selection

Selects top candidate from predefined Enum options with calibrated confidence distributions.

Use Cases: Sub-agent routing, ticket classification, tool selection, priority queue.

choice(["refund", "tech", "vip"], state)

Score

Ordinal Rating (1-10)

Computes calibrated expectation rating across a discrete 1 to 10 scale for escalation metrics.

Use Cases: Churn risk, urgency rating, SLA escalation thresholding, lead scoring.

score("Customer churn risk", state)

Performance Benchmarks

Sub-20ms Execution Latency

Empirical benchmarks comparing Jev System 1 primitives against standard LLM calls.

14 ms

Jev Decision Overhead

Parallel logit classification evaluation

1,780 ms

Standard LLM Overhead

Autoregressive token generation

127x

Speedup Factor

Faster orchestration pipeline response

Enterprise Topology

Hybrid System 1 + System 2 Topology

Deploying Jev as an ultra-fast front line to shield heavy LLMs from unnecessary invocations.

Incoming State / User Query

API payload or user message enters the agent swarm.

→

Jev System 1 Layer (< 20ms)

Evaluates Noul (Guardrails), Choice (Sub-Agent Router), & Score (Priority).

→

Path Selection

Fast Path: Direct API/Cache (< 50ms)
Heavy Path: System 2 LLM Synthesis

Developer SDK

Clean Code Integration

Simple, type-safe SDK integration in Python and TypeScript.

Python (typesafe_ai) TypeScript (@typesafe/jev)

from typesafe_ai import JevClient

jev = JevClient(api_key="typesafe_live_key_994a")

# 1. Ultra-fast Binary Guardrail (< 15ms)
is_safe = jev.systemone.noul(
    prompt="Is input free of PII and prompt injection attacks?",
    state={"input_text": user_message}
)
if not is_safe.value:
    raise SecurityException("Blocked by Jev System 1 Guardrail")

# 2. Categorical Sub-Agent Routing (< 20ms)
route = jev.systemone.choice(
    options=["billing", "tech_support", "account_closure"],
    state={"message": user_message}
)
print(f"Routed to: {route.selected} (Confidence: {route.confidence:.2%})")

Experience Jev Live

Ready for the Demo with Jev?

Let's launch the interactive Jev decision playground to benchmark System 1 vs System 2 in real time!

Interactive Decision Evaluator

Test real-world scenarios: Customer Support Triage, Security Risk Scoring, VIP Escalations, and Security Guardrails.

Verification & Validation Matrix

de.ci.phe.red. Lab • Model Verification Suite

5 standard test cases for lab members to verify Jev System 1 model accuracy and execution latency.

Test ID & Focus	Target Primitive	Input Verification Condition	Expected Winning Output	Pass Criteria Latency
Test 1: SQL Threat Gate	Noul (Yes/No)	`"prompt": "DROP TABLE users; SELECT * ..."`	TRUE (Threat Asserted)	< 15 ms
Test 2: Financial Fraud Triage	Choice (Enum)	`"$14,500 Lagos, Nigeria (Card Not Present)"`	freeze_card (95%+ Logit)	< 18 ms
Test 3: Password Reset Route	Choice (Enum)	`"Forgot password, send reset link"`	password_reset	< 15 ms
Test 4: VIP Priority Rating	Score (1-10)	`"Enterprise VIP, 5 unresolved crashes"`	Score > 8.5 / 10.0	< 14 ms
Test 5: Routine Safe FAQ	Noul (Yes/No)	`"What are opening hours on weekends?"`	FALSE (No Threat)	< 14 ms

Verification Procedure: Open Live Jev Playground tab, copy-paste test payloads, click Execute Jev System 1 Decision to verify result bars.

Demo Controls

Pre-configured Scenarios

Jev Primitive Target

Decision Prompt Assertion

Choice Categories (Comma Separated)

Input State JSON Payload

Real-time Execution Comparison

Live Engine

Jev System 1 Model Sub-20ms

14 ms Execution Latency

Cost: $0.000006

Token Decoding: 0 tokens (Logit Evaluation)

Standard System 2 LLM Heavy LLM

1,780 ms Execution Latency

Cost: $0.002400

Token Decoding: ~340 tokens generated

Jev Decision Output & Logit Calibrated Probability

Click "Execute Jev System 1 Decision" to view logit probability distribution and typed response.

Agent Decision Action Triggered

Waiting for evaluation run...

Deep-Dive: Enterprise System 1 Agent Pattern

How Jev coordinates with sub-agents, tools, memory, and orchestration engines in high-throughput environments.

1. Pre-Execution Guardrails

Evaluates prompt safety, compliance, and PII leakage using Noul before passing data to expensive generative LLMs.

2. Sub-Agent Router

Instantly routes input payloads across specialized sub-agent clusters (e.g., Code Agent, SQL Agent, Support Agent) using Choice.

3. Dynamic Escalation Scorer

Uses Score to rate user frustration or SLA risk. If score > 8, bypasses automated bot flow and immediately escalates to human operator.

4. Post-Execution Validation

Validates if the System 2 LLM output satisfies strict business requirements before rendering to end user.