TECHNICAL DOCUMENTATION

Azokle Intelligence

Zero-Knowledge Agentic AI

A comprehensive technical overview of decentralized, privacy-preserving artificial intelligence architecture designed for the post-cloud era.

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Feb 2026
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Abstract

Azokle Intelligence presents a fundamental reimagining of artificial intelligence infrastructure. We propose a zero-knowledge, agentic AI system that operates entirely at the network edge, ensuring complete data sovereignty while delivering state-of-the-art cognitive capabilities.

Our architecture combines cryptographic proofs,multi-agent reinforcement learning, andneural architecture optimization to create AI systems that are both powerful and privacy-preserving by design—no centralized infrastructure required.

01

Introduction

The prevailing paradigm in artificial intelligence is fundamentally flawed. Centralized systems require users to surrender their data to cloud infrastructure, creating unacceptable privacy risks, concentration of power, and single points of failure. This model is unsustainable.

The Problem

  • Users must trust cloud providers with sensitive data
  • Centralized infrastructure creates single points of failure
  • Model opacity prevents verification of fair and accurate behavior
  • Regulatory capture threatens AI development globally

Azokle Intelligence introduces a new path forward: decentralized, zero-knowledge AI that runs locally on user devices while maintaining the capability to coordinate with other agents through encrypted, verifiable protocols.

Our Solution

  • Zero-knowledge proofs for private, verifiable computation
  • Edge-native inference with sub-second response times
  • Multi-agent coordination via encrypted protocols
  • Open-source foundation enabling community audit
02

System Architecture

LAYER 1

User Interface

Natural language input, task delegation, result visualization

LAYER 2

Agent Orchestration

Multi-agent coordination, task planning, execution monitoring

LAYER 3

Edge Inference

Local model execution, quantized weights, hardware acceleration

ENCRYPTEDVERIFIEDLOCAL

Our architecture follows a layered approach, separating concerns while maintaining end-to-end encryption and verifiable computation at each boundary.

Component Overview

API Gateway

RESTful and WebSocket endpoints with JWT authentication and rate limiting

Agent Manager

Lifecycle management, load balancing, and fault tolerance for agent instances

Inference Engine

Optimized runtime supporting multiple model architectures and quantization schemes

Crypto Layer

zk-SNARK proof generation, verification, and encrypted state management

03

Zero-Knowledge Protocol

At the core of our privacy guarantees lies the zero-knowledge proof system, enabling computation on encrypted data without revealing sensitive information.

// Zero-Knowledge Proof Generation
const proof = await ZKProver.generate({
  input: encryptedUserData,
  circuit: "agent-inference-v1",
  publicInputs: {
    modelHash: MODEL_HASH,
    timestamp: Date.now()
  }
});

// Verification (no plaintext data exposed)
const valid = await ZKVerifier.verify(proof);
console.assert(valid, "Proof verification failed");

Protocol Specifications

PARAMETERVALUE
Proof SystemGroth16 / PLONK
CurveBN254 / BLS12-381
Proving Time~2.3s (M1 Pro)
Verification Time~10ms
Proof Size~256 bytes
04

Multi-Agent System

Our agents operate as autonomous entities capable of complex reasoning, planning, and execution. Each agent maintains local state while coordinating through encrypted messages.

🧠

Planner Agent

Decomposes complex tasks into executable sub-tasks with dependency management

🔍

Research Agent

Gathers and synthesizes information from multiple sources

✍️

Creator Agent

Generates content, code, and creative artifacts

⚙️

Executor Agent

Performs actions, calls tools, and interacts with external systems

Communication Protocol

Agents communicate through a peer-to-peer message bus with end-to-end encryption. Messages contain encrypted payloads with capability-based access control.

05

Edge Inference Engine

Our custom inference engine enables sub-second response times on consumer hardware through aggressive optimization and hardware-aware compilation.

Optimization Techniques

1
Quantization
8-bit activation/weight quantization reducing model size by 75%
4x faster
2
Operator Fusion
Fusing multiple operations into single kernels
3x throughput
3
KV Cache
Caching key-value attention states for repeated tokens
2x faster decode
4
Speculative Decoding
Small draft model predicts tokens verified by larger model
2.5x faster
06

Security Analysis

Our security model assumes a hostile network environment where all communications may be intercepted and modified. The system maintains security through cryptographic primitives.

Threat Model

  • • Network adversary can observe all traffic
  • • Device compromise is possible but detectable via remote attestation
  • • AI provider may be compelled to disclose user data
  • • Model weights may be extracted through side channels

Defenses

  • End-to-end encryption for all agent communications
  • Local-only execution of sensitive operations
  • Continuous attestation of model integrity
  • Zero-knowledge proofs prevent data leakage
  • Decentralized identity prevents single points of compromise
07

Performance Benchmarks

All benchmarks conducted on consumer hardware. Cloud baselines require 10-100x more energy for equivalent performance.

MODELPARAMSLATENCYTHROUGHPUTMEMORY
Azokle-S1.3B120ms850 req/s2GB
Azokle-M7.2B380ms320 req/s8GB
Azokle-L24.5B1.1s95 req/s24GB
Hardware: M2 Pro, 16GB RAM
All models include 8-bit quantization
08

Roadmap

Q1 2025

Foundation

  • Core agent framework
  • ZK proof integration
  • Edge inference engine
Q2 2025

Expansion

  • Mobile deployment
  • Federated learning
  • Plugin system
Q3 2025

Ecosystem

  • Developer SDK
  • Marketplace
  • Community models
Q4 2025

Decentralization

  • P2P agent network
  • Token incentives
  • DAO governance
09

Conclusion

Azokle Intelligence demonstrates that privacy and performance are not mutually exclusive. Our zero-knowledge, edge-first architecture delivers sub-second inference while maintaining cryptographic guarantees of data sovereignty.

The future of artificial intelligence is decentralized, verifiable, and user-controlled. Azokle Intelligence is building that future today.

Join the Revolution

Be part of the future of private AI.

Document Version: 1.0

Last Updated: January 2025

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