Zero-Knowledge Agentic AI
A comprehensive technical overview of decentralized, privacy-preserving artificial intelligence architecture designed for the post-cloud era.
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.
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.
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.
Natural language input, task delegation, result visualization
Multi-agent coordination, task planning, execution monitoring
Local model execution, quantized weights, hardware acceleration
Our architecture follows a layered approach, separating concerns while maintaining end-to-end encryption and verifiable computation at each boundary.
RESTful and WebSocket endpoints with JWT authentication and rate limiting
Lifecycle management, load balancing, and fault tolerance for agent instances
Optimized runtime supporting multiple model architectures and quantization schemes
zk-SNARK proof generation, verification, and encrypted state management
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");| PARAMETER | VALUE |
|---|---|
| Proof System | Groth16 / PLONK |
| Curve | BN254 / BLS12-381 |
| Proving Time | ~2.3s (M1 Pro) |
| Verification Time | ~10ms |
| Proof Size | ~256 bytes |
Our agents operate as autonomous entities capable of complex reasoning, planning, and execution. Each agent maintains local state while coordinating through encrypted messages.
Decomposes complex tasks into executable sub-tasks with dependency management
Gathers and synthesizes information from multiple sources
Generates content, code, and creative artifacts
Performs actions, calls tools, and interacts with external systems
Agents communicate through a peer-to-peer message bus with end-to-end encryption. Messages contain encrypted payloads with capability-based access control.
Our custom inference engine enables sub-second response times on consumer hardware through aggressive optimization and hardware-aware compilation.
Our security model assumes a hostile network environment where all communications may be intercepted and modified. The system maintains security through cryptographic primitives.
All benchmarks conducted on consumer hardware. Cloud baselines require 10-100x more energy for equivalent performance.
| MODEL | PARAMS | LATENCY | THROUGHPUT | MEMORY |
|---|---|---|---|---|
| Azokle-S | 1.3B | 120ms | 850 req/s | 2GB |
| Azokle-M | 7.2B | 380ms | 320 req/s | 8GB |
| Azokle-L | 24.5B | 1.1s | 95 req/s | 24GB |
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.
Document Version: 1.0
Last Updated: January 2025
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