NEXUS Technical Architecture

๐Ÿ“„ Technical Paper v1.0 ๐Ÿ“… January 2026 ๐Ÿ‘ค AI Forge Research

A comprehensive technical specification of NEXUS โ€” the Next-Level Autonomous Agent Operating System. This document details the architecture, components, and implementation patterns that enable sophisticated multi-agent reasoning with 83.6% task success rate.

๐Ÿ“‹ Executive Overview

NEXUS represents a paradigm shift in autonomous agent architecture. By combining Monte Carlo Tree Search (MCTS) reasoning with specialized multi-persona agents, NEXUS achieves unprecedented task completion rates while maintaining full auditability and human oversight.

The Problem

Current LLM agents suffer from three critical limitations: sequential reasoning without exploration, no learning from failed attempts, and inability to simulate outcomes before execution.

The Solution

โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚                         NEXUS ARCHITECTURE                          โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚                                                                     โ”‚
โ”‚    โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”    โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”    โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”            โ”‚
โ”‚    โ”‚   PLANNER   โ”‚    โ”‚  SIMULATOR  โ”‚    โ”‚   CRITIC    โ”‚            โ”‚
โ”‚    โ”‚   Agent     โ”‚โ—„โ”€โ”€โ–บโ”‚   Agent     โ”‚โ—„โ”€โ”€โ–บโ”‚   Agent     โ”‚            โ”‚
โ”‚    โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”˜    โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”˜    โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”˜            โ”‚
โ”‚           โ”‚                  โ”‚                  โ”‚                   โ”‚
โ”‚           โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜                   โ”‚
โ”‚                              โ”‚                                      โ”‚
โ”‚                    โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”                            โ”‚
โ”‚                    โ”‚   TECTONIC CORE   โ”‚                            โ”‚
โ”‚                    โ”‚   (MCTS Engine)   โ”‚                            โ”‚
โ”‚                    โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜                            โ”‚
โ”‚                              โ”‚                                      โ”‚
โ”‚         โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”                 โ”‚
โ”‚         โ”‚                    โ”‚                    โ”‚                 โ”‚
โ”‚   โ”Œโ”€โ”€โ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ”€โ”       โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ”€โ”€โ”     โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ”€โ”€โ”           โ”‚
โ”‚   โ”‚ SENTINEL  โ”‚       โ”‚ ORCHESTRATORโ”‚     โ”‚  WEB WORLD  โ”‚           โ”‚
โ”‚   โ”‚   MCP     โ”‚       โ”‚   (CoR)     โ”‚     โ”‚   MODELS    โ”‚           โ”‚
โ”‚   โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜       โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜     โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜           โ”‚
โ”‚                                                                     โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

Key Metrics

Metric NEXUS Baseline ReAct Improvement
Task Success Rate 83.6% 67.2% +24.4%
Error Recovery 91.2% 45.0% +102.7%
Avg. Steps to Complete 12.4 18.7 -33.7%
Catastrophic Failures 2.1% 15.8% -86.7%

๐Ÿง  TECTONIC Core

Tree-based Exploration and Critical Thinking Orchestration for Navigating Intelligent Choices โ€” the reasoning graph engine at the heart of NEXUS. Built on Monte Carlo Tree Search principles adapted for LLM agents.

Architecture

                              โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
                              โ”‚    ROOT     โ”‚
                              โ”‚  (Initial)  โ”‚
                              โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”˜
                                     โ”‚
               โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
               โ”‚                     โ”‚                     โ”‚
        โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ”€โ”€โ”       โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ”€โ”€โ”       โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ”€โ”€โ”
        โ”‚   Action A  โ”‚       โ”‚   Action B  โ”‚       โ”‚   Action C  โ”‚
        โ”‚   Q=0.72    โ”‚       โ”‚   Q=0.85    โ”‚       โ”‚   Q=0.45    โ”‚
        โ”‚   N=15      โ”‚       โ”‚   N=42      โ”‚       โ”‚   N=8       โ”‚
        โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”˜       โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”˜       โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
               โ”‚                     โ”‚
        โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”       โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”
        โ”‚             โ”‚       โ”‚             โ”‚
     โ”Œโ”€โ”€โ–ผโ”€โ”€โ”€โ”     โ”Œโ”€โ”€โ”€โ–ผโ”€โ”€โ” โ”Œโ”€โ”€โ–ผโ”€โ”€โ”€โ”     โ”Œโ”€โ”€โ”€โ–ผโ”€โ”€โ”
     โ”‚ A.1  โ”‚     โ”‚ A.2  โ”‚ โ”‚ B.1  โ”‚     โ”‚ B.2  โ”‚
     โ”‚Q=0.68โ”‚     โ”‚Q=0.91โ”‚ โ”‚Q=0.88โ”‚     โ”‚Q=0.79โ”‚
     โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”˜     โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”˜     โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

UCB1 Selection Formula

TECTONIC uses Upper Confidence Bound (UCB1) to balance exploration vs exploitation:

Formula
UCB1 = Q(s,a) / N(s,a) + C ร— โˆš(ln(N(s)) / N(s,a))

Where:
  Q(s,a) = Total reward from action a in state s
  N(s,a) = Visit count for action a in state s
  N(s)   = Total visits to state s
  C      = Exploration constant (default: โˆš2 โ‰ˆ 1.414)

Core Interface

ITectonicEngine.cs
public interface ITectonicEngine
{
    /// <summary>Runs MCTS search from current state</summary>
    Task<SearchResult> SearchAsync(
        WorldState initialState,
        SearchConfig config,
        CancellationToken ct = default);

    /// <summary>Executes best action and returns new state</summary>
    Task<ExecutionResult> ExecuteBestActionAsync(
        SearchResult search,
        CancellationToken ct = default);
}

Search Loop

TectonicEngine.cs
public async Task<SearchResult> SearchAsync(
    WorldState initialState, 
    SearchConfig config,
    CancellationToken ct)
{
    var root = new TreeNode(initialState);
    
    for (int i = 0; i < config.MaxIterations; i++)
    {
        // 1. SELECT - Find promising leaf using UCB1
        var leaf = SelectLeaf(root);
        
        // 2. EXPAND - Generate new actions via Planner
        var actions = await _planner.ProposeActionsAsync(
            leaf.State, ct);
        ExpandNode(leaf, actions);
        
        // 3. SIMULATE - Predict outcomes via Simulator
        var outcomes = await _simulator.PredictOutcomesAsync(
            leaf.State, actions, ct);
        
        // 4. EVALUATE - Score outcomes via Critic
        var scores = await _critic.EvaluateAsync(
            outcomes, ct);
        
        // 5. BACKPROPAGATE - Update tree statistics
        Backpropagate(leaf, scores);
    }
    
    return new SearchResult(root, GetBestPath(root));
}

Key Features

๐Ÿ”„ Parallel Simulation

Run multiple MCTS iterations concurrently with virtual loss to prevent thread collision

๐Ÿ“Š Progressive Widening

Dynamically limit branching factor based on visit counts to focus on promising paths

๐Ÿง  RAVE Enhancement

Rapid Action Value Estimation shares statistics across sibling nodes for faster convergence

๐Ÿ’พ Transposition Table

Cache and reuse evaluations for identical states across different tree paths

๐Ÿ“ Planner Agent

The Planner Agent generates diverse action candidates at each decision point. It explores the action space creatively while staying grounded in what's actually achievable given the current world state.

Role in SPIRAL

โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚                      PLANNER AGENT                           โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚                                                              โ”‚
โ”‚   Input:                      Output:                        โ”‚
โ”‚   โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”        โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”     โ”‚
โ”‚   โ”‚ โ€ข Current State โ”‚        โ”‚ ProposedAction[]        โ”‚     โ”‚
โ”‚   โ”‚ โ€ข Goal          โ”‚   โ”€โ”€โ–ถ  โ”‚ โ”œโ”€ Action A (explore)   โ”‚     โ”‚
โ”‚   โ”‚ โ€ข History       โ”‚        โ”‚ โ”œโ”€ Action B (exploit)   โ”‚     โ”‚
โ”‚   โ”‚ โ€ข Constraints   โ”‚        โ”‚ โ”œโ”€ Action C (fallback)  โ”‚     โ”‚
โ”‚   โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜        โ”‚ โ””โ”€ Action D (creative)  โ”‚     โ”‚
โ”‚                              โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜     โ”‚
โ”‚                                                              โ”‚
โ”‚   Diversity Mechanisms:                                      โ”‚
โ”‚   โ€ข Temperature sampling (T=0.7 to T=1.2)                    โ”‚
โ”‚   โ€ข Categorical prompting (safe/risky/creative)              โ”‚
โ”‚   โ€ข Historical deduplication                                 โ”‚
โ”‚                                                              โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

Core Interface

IPlannerAgent.cs
public interface IPlannerAgent
{
    /// <summary>
    /// Generates diverse action candidates for the current state.
    /// Returns 3-5 actions balancing exploration and exploitation.
    /// </summary>
    Task<IReadOnlyList<ProposedAction>> ProposeActionsAsync(
        WorldState currentState,
        CancellationToken ct = default);

    /// <summary>
    /// Refines actions based on Critic feedback.
    /// </summary>
    Task<ProposedAction> RefineActionAsync(
        ProposedAction original,
        CriticFeedback feedback,
        CancellationToken ct = default);
}

ProposedAction Model

ProposedAction.cs
public record ProposedAction(
    string Id,
    string Description,
    ActionType Type,
    string ToolName,
    Dictionary<string, object> Parameters,
    float Confidence,
    string Rationale,
    ActionCategory Category
);

public enum ActionCategory
{
    Safe,       // Low risk, incremental progress
    Risky,      // Higher risk, potential breakthrough
    Creative,   // Novel approach, unexplored
    Fallback    // Recovery action if others fail
}

Diversity Generation

PlannerAgent.cs
public async Task<IReadOnlyList<ProposedAction>> ProposeActionsAsync(
    WorldState state,
    CancellationToken ct)
{
    var actions = new List<ProposedAction>();
    
    // Generate diverse candidates with different temperatures
    var tasks = new[]
    {
        GenerateWithCategory(ActionCategory.Safe, 0.3f, state, ct),
        GenerateWithCategory(ActionCategory.Risky, 0.9f, state, ct),
        GenerateWithCategory(ActionCategory.Creative, 1.2f, state, ct),
        GenerateWithCategory(ActionCategory.Fallback, 0.5f, state, ct)
    };
    
    var results = await Task.WhenAll(tasks);
    actions.AddRange(results.Where(a => a != null)!);
    
    // Deduplicate similar actions
    return DeduplicateActions(actions);
}

Key Features

๐ŸŽฏ Goal Decomposition

Breaks complex goals into actionable sub-tasks with clear success criteria

๐Ÿ”€ Multi-Temperature

Samples at different temperatures to ensure both safe and creative options

๐Ÿ›ก๏ธ Constraint Awareness

Respects resource limits, permissions, and safety boundaries

๐Ÿ“š History Learning

Avoids repeating failed approaches from the conversation history

๐Ÿ”ฎ Simulator Agent

The Simulator Agent predicts outcomes of proposed actions without executing them. This "mental simulation" capability allows NEXUS to evaluate consequences before committing to real-world changes.

Simulation Strategies

โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚                     SIMULATOR AGENT                             โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚                                                                 โ”‚
โ”‚  โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”    โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”    โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”  โ”‚
โ”‚  โ”‚ LLM-Based   โ”‚    โ”‚ Rule-Based  โ”‚    โ”‚ Web World Model     โ”‚  โ”‚
โ”‚  โ”‚ Prediction  โ”‚    โ”‚ Inference   โ”‚    โ”‚ Sandbox Execution   โ”‚  โ”‚
โ”‚  โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค    โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค    โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค  โ”‚
โ”‚  โ”‚ โ€ข Creative  โ”‚    โ”‚ โ€ข Fast      โ”‚    โ”‚ โ€ข Accurate          โ”‚  โ”‚
โ”‚  โ”‚ โ€ข Flexible  โ”‚    โ”‚ โ€ข Reliable  โ”‚    โ”‚ โ€ข Real execution    โ”‚  โ”‚
โ”‚  โ”‚ โ€ข Uncertain โ”‚    โ”‚ โ€ข Limited   โ”‚    โ”‚ โ€ข Isolated          โ”‚  โ”‚
โ”‚  โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”˜    โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”˜    โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜  โ”‚
โ”‚         โ”‚                  โ”‚                      โ”‚             โ”‚
โ”‚         โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜             โ”‚
โ”‚                            โ–ผ                                    โ”‚
โ”‚                   โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”                           โ”‚
โ”‚                   โ”‚ SimulatedOutcomeโ”‚                           โ”‚
โ”‚                   โ”‚ โ€ข NewState      โ”‚                           โ”‚
โ”‚                   โ”‚ โ€ข Confidence    โ”‚                           โ”‚
โ”‚                   โ”‚ โ€ข SideEffects   โ”‚                           โ”‚
โ”‚                   โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜                           โ”‚
โ”‚                                                                 โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

Core Interface

ISimulatorAgent.cs
public interface ISimulatorAgent
{
    /// <summary>
    /// Predicts outcomes of actions without executing them.
    /// Uses hybrid approach: LLM + rules + sandbox.
    /// </summary>
    Task<IReadOnlyList<SimulatedOutcome>> PredictOutcomesAsync(
        WorldState currentState,
        IReadOnlyList<ProposedAction> actions,
        CancellationToken ct = default);

    /// <summary>
    /// Runs action in isolated sandbox for accurate prediction.
    /// </summary>
    Task<SimulatedOutcome> SandboxExecuteAsync(
        ProposedAction action,
        IWorldModel sandbox,
        CancellationToken ct = default);
}

SimulatedOutcome Model

SimulatedOutcome.cs
public record SimulatedOutcome(
    string ActionId,
    WorldState PredictedState,
    float SuccessProbability,
    float Confidence,
    List<SideEffect> SideEffects,
    SimulationMethod Method,
    TimeSpan EstimatedDuration,
    string Reasoning
);

public record SideEffect(
    string Description,
    SideEffectType Type,      // Reversible, Irreversible, Unknown
    float Probability,
    string[] AffectedResources
);

Hybrid Prediction Strategy

SimulatorAgent.cs
public async Task<SimulatedOutcome> PredictSingleAsync(
    WorldState state,
    ProposedAction action,
    CancellationToken ct)
{
    // Strategy 1: Check rule-based predictions first (fast)
    if (_ruleEngine.CanPredict(action.Type))
    {
        return _ruleEngine.Predict(state, action);
    }
    
    // Strategy 2: Use sandbox if available (accurate)
    if (_worldModelFactory.CanCreateSandbox(action.Type))
    {
        await using var sandbox = await _worldModelFactory
            .CreateSandboxAsync(state, ct);
        return await SandboxExecuteAsync(action, sandbox, ct);
    }
    
    // Strategy 3: Fall back to LLM prediction (flexible)
    return await _llmPredictor.PredictAsync(state, action, ct);
}

Key Features

๐Ÿ”’ Safe Exploration

Test dangerous operations in isolation before real execution

โšก Confidence Scoring

Each prediction includes confidence level to guide decision-making

๐ŸŒŠ Side Effect Detection

Identifies unintended consequences before they occur

๐Ÿ”„ Multi-Strategy

Combines rules, LLM, and sandbox for best accuracy/speed tradeoff

โš–๏ธ Critic Agent

The Critic Agent evaluates simulated outcomes and provides feedback for MCTS backpropagation. It implements self-reflection capabilities, scoring actions across multiple dimensions.

Evaluation Flow

โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚                       CRITIC AGENT                              โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚                                                                 โ”‚
โ”‚   SimulatedOutcome โ”€โ”€โ–ถ Multi-Dimensional Scoring                โ”‚
โ”‚                                                                 โ”‚
โ”‚   โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”  โ”‚
โ”‚   โ”‚  Progress    โ”‚   Safety     โ”‚  Efficiency  โ”‚  Reversib.  โ”‚  โ”‚
โ”‚   โ”‚   (0.35)     โ”‚   (0.30)     โ”‚    (0.20)    โ”‚   (0.15)    โ”‚  โ”‚
โ”‚   โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค  โ”‚
โ”‚   โ”‚   0.85       โ”‚    0.92      โ”‚     0.71     โ”‚    0.88     โ”‚  โ”‚
โ”‚   โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜  โ”‚
โ”‚                            โ”‚                                    โ”‚
โ”‚                            โ–ผ                                    โ”‚
โ”‚              Weighted Score: 0.847                              โ”‚
โ”‚                            โ”‚                                    โ”‚
โ”‚                            โ–ผ                                    โ”‚
โ”‚   โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”   โ”‚
โ”‚   โ”‚ Feedback & Suggestions                                  โ”‚   โ”‚
โ”‚   โ”‚ โ€ข "Consider adding rollback mechanism"                  โ”‚   โ”‚
โ”‚   โ”‚ โ€ข "File operation could fail on permissions"            โ”‚   โ”‚
โ”‚   โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜   โ”‚
โ”‚                                                                 โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

Core Interface

ICriticAgent.cs
public interface ICriticAgent
{
    /// <summary>
    /// Evaluates simulated outcomes and returns scores for MCTS.
    /// Higher scores indicate better actions.
    /// </summary>
    Task<IReadOnlyList<CriticEvaluation>> EvaluateAsync(
        IReadOnlyList<SimulatedOutcome> outcomes,
        CancellationToken ct = default);

    /// <summary>
    /// Provides detailed feedback for action refinement.
    /// </summary>
    Task<CriticFeedback> GetFeedbackAsync(
        SimulatedOutcome outcome,
        CancellationToken ct = default);
}

Multi-Dimensional Scoring

CriticAgent.cs
public async Task<CriticEvaluation> EvaluateSingleAsync(
    SimulatedOutcome outcome,
    CancellationToken ct)
{
    // Score across multiple dimensions
    var dimensions = new Dictionary<string, (float score, float weight)>
    {
        ["progress"] = (ScoreProgress(outcome), 0.35f),
        ["safety"] = (ScoreSafety(outcome), 0.30f),
        ["efficiency"] = (ScoreEfficiency(outcome), 0.20f),
        ["reversibility"] = (ScoreReversibility(outcome), 0.15f)
    };
    
    // Calculate weighted score
    var weightedScore = dimensions.Sum(d => d.Value.score * d.Value.weight);
    
    // Generate improvement suggestions via LLM
    var suggestions = await GenerateSuggestionsAsync(outcome, dimensions, ct);
    
    return new CriticEvaluation(
        outcome.ActionId,
        weightedScore,
        dimensions.ToDictionary(d => d.Key, d => d.Value.score),
        suggestions,
        DetermineVerdict(weightedScore)
    );
}

Evaluation Model

CriticEvaluation.cs
public record CriticEvaluation(
    string ActionId,
    float OverallScore,              // 0.0 to 1.0
    Dictionary<string, float> DimensionScores,
    List<string> Suggestions,
    Verdict Verdict
);

public enum Verdict
{
    StrongApprove,   // Score >= 0.85
    Approve,         // Score >= 0.70
    Neutral,         // Score >= 0.50
    Caution,         // Score >= 0.30
    Reject           // Score < 0.30
}

Key Features

๐ŸŽฏ Multi-Dimensional

Scores progress, safety, efficiency, and reversibility independently

๐Ÿ’ก Self-Reflection

LLM-powered analysis identifies risks and improvements

โฌ†๏ธ Backpropagation

Scores flow back through MCTS tree to improve future selections

๐Ÿ”ง Refinement Loop

Feedback enables Planner to improve rejected actions

๐Ÿ›ก๏ธ SENTINEL MCP

System Entrypoint for Network Tasks, Intelligent Node Execution Layer โ€” the Ubuntu MCP server that provides NEXUS with full infrastructure control through the Model Context Protocol.

Architecture

โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚                      SENTINEL MCP                               โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚                                                                 โ”‚
โ”‚   โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”   โ”‚
โ”‚   โ”‚                   MCP Protocol Layer                    โ”‚   โ”‚
โ”‚   โ”‚         (JSON-RPC 2.0 over stdio/HTTP)                  โ”‚   โ”‚
โ”‚   โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜   โ”‚
โ”‚                             โ”‚                                   โ”‚
โ”‚   โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”         โ”‚
โ”‚   โ”‚   SSH   โ”‚ Docker  โ”‚   File    โ”‚ Network โ”‚ System  โ”‚         โ”‚
โ”‚   โ”‚  Tools  โ”‚  Tools  โ”‚   Tools   โ”‚  Tools  โ”‚  Tools  โ”‚         โ”‚
โ”‚   โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค         โ”‚
โ”‚   โ”‚ssh_exec โ”‚containerโ”‚read_file  โ”‚http_get โ”‚get_info โ”‚         โ”‚
โ”‚   โ”‚ssh_copy โ”‚  _run   โ”‚write_file โ”‚curl     โ”‚ps_list  โ”‚         โ”‚
โ”‚   โ”‚tunnel   โ”‚  logs   โ”‚list_dir   โ”‚dns_queryโ”‚kill_procโ”‚         โ”‚
โ”‚   โ”‚keygen   โ”‚  build  โ”‚chmod      โ”‚port_scanโ”‚cron_add โ”‚         โ”‚
โ”‚   โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜         โ”‚
โ”‚                             โ”‚                                   โ”‚
โ”‚   โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”   โ”‚
โ”‚   โ”‚                  Security Layer                         โ”‚   โ”‚
โ”‚   โ”‚  โ€ข Permission checks  โ€ข Audit logging  โ€ข Rate limiting  โ”‚   โ”‚
โ”‚   โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜   โ”‚
โ”‚                                                                 โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

Core Interface

ISentinelMcp.cs
public interface ISentinelMcp
{
    /// <summary>Lists all available MCP tools</summary>
    Task<IReadOnlyList<ToolDefinition>> ListToolsAsync(
        CancellationToken ct = default);

    /// <summary>Executes a tool with given parameters</summary>
    Task<ToolResult> ExecuteToolAsync(
        string toolName,
        Dictionary<string, object> parameters,
        CancellationToken ct = default);

    /// <summary>Checks if tool execution is permitted</summary>
    Task<bool> CanExecuteAsync(
        string toolName,
        SecurityContext context,
        CancellationToken ct = default);
}

Tool Categories

Category Tools Risk Level Requires Approval
SSH ssh_execute, ssh_copy, ssh_tunnel, ssh_keygen High Yes
Docker container_run, container_stop, docker_build, get_logs High Yes
File read_file, write_file, list_dir, chmod, delete Medium Write ops only
Network http_get, curl, dns_query, port_scan Low No
System get_system_info, list_processes, kill_process, add_cron High Destructive only

Tool Execution

SentinelMcp.cs
public async Task<ToolResult> ExecuteToolAsync(
    string toolName,
    Dictionary<string, object> parameters,
    CancellationToken ct)
{
    // 1. Validate tool exists
    var tool = _tools.GetOrDefault(toolName)
        ?? throw new ToolNotFoundException(toolName);
    
    // 2. Security check
    if (!await _security.AuthorizeAsync(tool, _context, ct))
        throw new UnauthorizedToolAccessException(toolName);
    
    // 3. Validate parameters
    var validated = tool.Schema.Validate(parameters);
    
    // 4. Execute with timeout
    using var cts = CancellationTokenSource
        .CreateLinkedTokenSource(ct);
    cts.CancelAfter(tool.Timeout);
    
    var result = await tool.ExecuteAsync(validated, cts.Token);
    
    // 5. Audit log
    await _audit.LogAsync(new ToolExecutionLog
    {
        Tool = toolName,
        Parameters = parameters,
        Result = result,
        Timestamp = DateTime.UtcNow
    });
    
    return result;
}

Key Features

๐Ÿ” Security First

Every tool call passes through permission checks and audit logging

๐Ÿ“ก MCP Protocol

Standard JSON-RPC 2.0 interface compatible with any MCP client

๐Ÿณ Docker Native

Full container lifecycle management for isolated execution

๐ŸŒ Remote Access

SSH tunneling and remote execution across infrastructure

๐ŸŒ Web World Models

Web World Models provide persistent, typed sandbox environments where agents can safely execute actions, maintain state, and create checkpoints. Built on web technology as a universal substrate.

Architecture

โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚                     WEB WORLD MODELS                           โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚                                                                โ”‚
โ”‚   โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”    โ”‚
โ”‚   โ”‚                  World Model Instance                 โ”‚    โ”‚
โ”‚   โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค    โ”‚
โ”‚   โ”‚  State: {                                             โ”‚    โ”‚
โ”‚   โ”‚    files: Map<path, content>,                         โ”‚    โ”‚
โ”‚   โ”‚    processes: Process[],                              โ”‚    โ”‚
โ”‚   โ”‚    network: NetworkState,                             โ”‚    โ”‚
โ”‚   โ”‚    env: Map<key, value>                               โ”‚    โ”‚
โ”‚   โ”‚  }                                                    โ”‚    โ”‚
โ”‚   โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜    โ”‚
โ”‚                            โ”‚                                   โ”‚
โ”‚         โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”                โ”‚
โ”‚         โ”‚                  โ”‚                  โ”‚                โ”‚
โ”‚   โ”Œโ”€โ”€โ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ”€โ”     โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ”€โ”€โ”    โ”Œโ”€โ”€โ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ”€โ”           โ”‚
โ”‚   โ”‚ Checkpointโ”‚     โ”‚   Execute   โ”‚    โ”‚  Rollback โ”‚           โ”‚
โ”‚   โ”‚   Save    โ”‚     โ”‚   Action    โ”‚    โ”‚  Restore  โ”‚           โ”‚
โ”‚   โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜     โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜    โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜           โ”‚
โ”‚                                                                โ”‚
โ”‚   Checkpoints: [cp_001] โ”€โ”€โ–ถ [cp_002] โ”€โ”€โ–ถ [cp_003]              โ”‚
โ”‚                                                                โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

Core Interface

IWorldModel.cs
public interface IWorldModel : IAsyncDisposable
{
    /// <summary>Current state of the world</summary>
    WorldState CurrentState { get; }
    
    /// <summary>Unique identifier for this world instance</summary>
    string WorldId { get; }

    /// <summary>Executes action and returns new state</summary>
    Task<WorldState> ExecuteAsync(
        ProposedAction action,
        CancellationToken ct = default);

    /// <summary>Creates a checkpoint of current state</summary>
    Task<string> CheckpointAsync(
        string? label = null,
        CancellationToken ct = default);

    /// <summary>Restores state from a checkpoint</summary>
    Task RestoreAsync(
        string checkpointId,
        CancellationToken ct = default);

    /// <summary>Forks world into independent copy</summary>
    Task<IWorldModel> ForkAsync(
        CancellationToken ct = default);
}

Typed World State

WorldState.cs
public record WorldState
{
    public required string WorldId { get; init; }
    public required WorldType Type { get; init; }
    public required DateTime Timestamp { get; init; }
    
    // File system state
    public ImmutableDictionary<string, FileEntry> Files { get; init; }
    
    // Process state
    public ImmutableList<ProcessInfo> Processes { get; init; }
    
    // Network state
    public NetworkState Network { get; init; }
    
    // Environment variables
    public ImmutableDictionary<string, string> Environment { get; init; }
    
    // Custom typed state per world type
    public ImmutableDictionary<string, JsonElement> Custom { get; init; }
}

public enum WorldType
{
    Docker,        // Containerized environment
    Browser,       // Headless browser sandbox
    FileSystem,    // Virtual file system
    Database,      // Database sandbox
    Composite      // Multiple types combined
}

Checkpoint System

DockerWorldModel.cs
public async Task<string> CheckpointAsync(
    string? label,
    CancellationToken ct)
{
    var checkpointId = $"cp_{Guid.NewGuid():N}";
    
    // Commit container state to image
    await _docker.Containers.CommitContainerChangesAsync(
        new CommitContainerChangesParameters
        {
            ContainerID = _containerId,
            RepositoryName = $"nexus-checkpoint/{WorldId}",
            Tag = checkpointId
        }, ct);
    
    // Store metadata
    _checkpoints[checkpointId] = new CheckpointMeta
    {
        Id = checkpointId,
        Label = label,
        State = CurrentState,
        CreatedAt = DateTime.UtcNow,
        ParentId = _currentCheckpoint
    };
    
    _currentCheckpoint = checkpointId;
    return checkpointId;
}

Key Features

๐Ÿ”„ Time Travel

Save and restore state at any point with checkpoint branching

๐ŸŒณ Fork & Explore

Clone worlds for parallel exploration of different paths

๐Ÿ“ฆ Typed State

Strongly-typed world state with immutable data structures

๐Ÿ”’ Isolation

Full sandbox isolation prevents state leakage between worlds

โšก Orchestrator

The Orchestrator implements the Chain of Responsibility pattern to coordinate workflow execution. Requests flow through a series of handlers that process sequentially until one can handle the request.

Chain of Responsibility

โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚                      ORCHESTRATOR                               โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚                                                                 โ”‚
โ”‚   Request โ”€โ”€โ–ถ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”                                   โ”‚
โ”‚               โ”‚ Validation  โ”‚ โ”€โ”€โ–ถ Invalid? โ”€โ”€โ–ถ Error Response   โ”‚
โ”‚               โ”‚   Handler   โ”‚                                   โ”‚
โ”‚               โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”˜                                   โ”‚
โ”‚                      โ”‚ Valid                                    โ”‚
โ”‚                      โ–ผ                                          โ”‚
โ”‚               โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”                                   โ”‚
โ”‚               โ”‚   Logging   โ”‚ โ”€โ”€โ–ถ Log request details           โ”‚
โ”‚               โ”‚   Handler   โ”‚                                   โ”‚
โ”‚               โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”˜                                   โ”‚
โ”‚                      โ”‚                                          โ”‚
โ”‚                      โ–ผ                                          โ”‚
โ”‚               โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”                                   โ”‚
โ”‚               โ”‚   Caching   โ”‚ โ”€โ”€โ–ถ Cached? โ”€โ”€โ–ถ Return cached     โ”‚
โ”‚               โ”‚   Handler   โ”‚                                   โ”‚
โ”‚               โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”˜                                   โ”‚
โ”‚                      โ”‚ Not cached                               โ”‚
โ”‚                      โ–ผ                                          โ”‚
โ”‚               โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”                                   โ”‚
โ”‚               โ”‚  Execution  โ”‚ โ”€โ”€โ–ถ Process request               โ”‚
โ”‚               โ”‚   Handler   โ”‚                                   โ”‚
โ”‚               โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”˜                                   โ”‚
โ”‚                      โ”‚                                          โ”‚
โ”‚                      โ–ผ                                          โ”‚
โ”‚               Response โ—€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€  โ”‚
โ”‚                                                                 โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

Core Abstractions

IRequestHandler.cs
public interface IRequestHandler<TRequest, TResponse>
    where TRequest : IRequest<TResponse>
{
    /// <summary>Order in the chain (lower = earlier)</summary>
    int Order { get; }
    
    /// <summary>Whether this handler can process the request</summary>
    bool CanHandle(TRequest request);
    
    /// <summary>Process request and optionally call next handler</summary>
    Task<TResponse> HandleAsync(
        TRequest request,
        Func<TRequest, Task<TResponse>> next,
        CancellationToken ct = default);
}

public interface IOrchestrator
{
    Task<TResponse> ExecuteAsync<TRequest, TResponse>(
        TRequest request,
        CancellationToken ct = default)
        where TRequest : IRequest<TResponse>;
}

Orchestrator Implementation

Orchestrator.cs
public class Orchestrator : IOrchestrator
{
    private readonly IServiceProvider _services;
    private readonly ILogger<Orchestrator> _logger;

    public async Task<TResponse> ExecuteAsync<TRequest, TResponse>(
        TRequest request,
        CancellationToken ct)
        where TRequest : IRequest<TResponse>
    {
        // Get all handlers, ordered by priority
        var handlers = _services
            .GetServices<IRequestHandler<TRequest, TResponse>>()
            .OrderBy(h => h.Order)
            .ToList();

        // Build the chain
        Func<TRequest, Task<TResponse>> chain = _ => 
            Task.FromException<TResponse>(
                new NoHandlerException(typeof(TRequest)));

        // Reverse iterate to build from end to start
        foreach (var handler in handlers.AsEnumerable().Reverse())
        {
            var next = chain;
            chain = req => handler.HandleAsync(req, next, ct);
        }

        return await chain(request);
    }
}

Handler Examples

ValidationHandler.cs
public class ValidationHandler<TRequest, TResponse> 
    : IRequestHandler<TRequest, TResponse>
    where TRequest : IRequest<TResponse>
{
    private readonly IValidator<TRequest> _validator;
    
    public int Order => 100; // Run early
    public bool CanHandle(TRequest request) => true;

    public async Task<TResponse> HandleAsync(
        TRequest request,
        Func<TRequest, Task<TResponse>> next,
        CancellationToken ct)
    {
        var result = await _validator.ValidateAsync(request, ct);
        
        if (!result.IsValid)
            throw new ValidationException(result.Errors);
        
        return await next(request);
    }
}

Built-in Handlers

Handler Order Purpose
ValidationHandler 100 Validates request against schema
LoggingHandler 200 Logs request/response with timing
CachingHandler 300 Returns cached responses when available
RetryHandler 400 Retries failed requests with backoff
TimeoutHandler 500 Enforces execution time limits
ExecutionHandler 1000 Actually processes the request

Key Features

๐Ÿ”— Extensible Chain

Add custom handlers at any point in the pipeline via DI

โฑ๏ธ Timeout Control

Configurable timeouts prevent runaway operations

๐Ÿ”„ Retry Logic

Automatic retries with exponential backoff for transient failures

๐Ÿ“Š Observability

Built-in logging, metrics, and distributed tracing support

๐Ÿ“Š CQRS Pattern

NEXUS implements Command Query Responsibility Segregation to separate read and write operations, enabling optimized data access, better scalability, and cleaner architecture.

The Pattern

โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚                     NEXUS Client                             โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
                         โ”‚
         โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
         โ”‚                               โ”‚
         โ–ผ                               โ–ผ
โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”             โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚    Commands     โ”‚             โ”‚     Queries     โ”‚
โ”‚  (Write Model)  โ”‚             โ”‚  (Read Model)   โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค             โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚ โ€ข Create Task   โ”‚             โ”‚ โ€ข Get Task      โ”‚
โ”‚ โ€ข Execute Actionโ”‚             โ”‚ โ€ข List Tasks    โ”‚
โ”‚ โ€ข Update State  โ”‚             โ”‚ โ€ข Search Historyโ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜             โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
         โ”‚                               โ”‚
         โ–ผ                               โ–ผ
โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”             โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚Command Handlers โ”‚             โ”‚ Query Handlers  โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜             โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
         โ”‚                               โ”‚
         โ–ผ                               โ–ผ
โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”             โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚   Write Store   โ”‚  โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ–ถ  โ”‚   Read Store    โ”‚
โ”‚  (PostgreSQL)   โ”‚   Events    โ”‚ (Elasticsearch) โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜             โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

Commands & Queries

Commands.cs
// Commands (Write operations)
public interface ICommand<TResult> { }

public record ExecuteActionCommand(
    string TaskId,
    ProposedAction Action,
    string RequestedBy
) : ICommand<ActionResult>;

public record CreateTaskCommand(
    string Description,
    TaskPriority Priority,
    Dictionary<string, object> Parameters
) : ICommand<TaskCreatedResult>;

// Queries (Read operations)
public interface IQuery<TResult> { }

public record GetTaskQuery(
    string TaskId
) : IQuery<TaskDetails>;

public record SearchHistoryQuery(
    string? TaskId,
    DateTime? FromDate,
    DateTime? ToDate,
    int Page = 1,
    int PageSize = 20
) : IQuery<PagedResult<HistoryEntry>>;

Mediator Pattern

IMediator.cs
public interface IMediator
{
    /// <summary>Sends a command for execution</summary>
    Task<TResult> SendAsync<TResult>(
        ICommand<TResult> command, 
        CancellationToken ct = default);
    
    /// <summary>Executes a query and returns results</summary>
    Task<TResult> QueryAsync<TResult>(
        IQuery<TResult> query, 
        CancellationToken ct = default);
}

Event Sourcing Integration

EventHandler.cs
// Event generated by command
public record ActionExecutedEvent(
    Guid ExecutionId,
    string TaskId,
    ActionType ActionType,
    bool Success,
    DateTime Timestamp
) : IDomainEvent;

// Event handler updates read model
public class ActionExecutedEventHandler : IEventHandler<ActionExecutedEvent>
{
    private readonly IReadStore _readStore;
    
    public async Task HandleAsync(ActionExecutedEvent @event, CancellationToken ct)
    {
        // Update denormalized read model
        await _readStore.UpsertAsync(new HistoryEntry
        {
            Id = @event.ExecutionId.ToString(),
            TaskId = @event.TaskId,
            ActionType = @event.ActionType.ToString(),
            Success = @event.Success,
            Timestamp = @event.Timestamp
        }, ct);
    }
}

Benefits for NEXUS

Benefit Impact
Read Performance 10x faster queries via denormalized read models
Write Consistency ACID transactions on write store
Scalability Scale reads and writes independently
Audit Trail Complete command history via event sourcing
Flexibility Different storage optimized per use case

Key Features

๐Ÿ“ Command Handlers

Dedicated handlers for write operations with validation and business logic

๐Ÿ” Query Handlers

Optimized read handlers against denormalized data stores

๐Ÿ“จ Event Bus

Commands emit events that sync read models asynchronously

๐Ÿ—ƒ๏ธ Dual Storage

PostgreSQL for writes, Elasticsearch for fast queries

Interested in NEXUS?

Whether you're an investor, researcher, or engineer โ€” we'd love to discuss how NEXUS can transform autonomous agent capabilities.

๐Ÿ“ง Contact Us