Decentralized Infrastructure for
Multi-Agent Generative
AI Systems
Building a trustless, high-fidelity platform for unbiased, privacy-focused AI inference across decentralized LLM ecosystems.
Core Architecture
Swarm Manager
An intent-sensitive, multi-agent orchestrator that dynamically selects and manages agents using adaptive routing algorithms. This orchestrator optimizes agent-task mapping, leveraging real-time utility functions and decentralized task scheduling for efficient swarm operations within the Seraphnet Playground.
(Multi-Agent Pods)
These modular, self-sufficient agents function as specialized applications in a coordinated swarm. Utilizing Monte Carlo Tree Search (MCTS) and ensemble-based calibration, each pod independently contributes to task resolution across both open-source and commercial LLMs, forming a collaborative agent ecosystem.
(Forge SDK)
Forge serves as a multi-agent SDK, enabling model deployment, inference, and training in a plug-in-supported containerized environment. It includes task-splitting protocols, automated hyperparameter tuning, and interpretability tools, all accessible via real-time orchestration APIs for streamlined model adaptation and testing.
Key Features
Optimized for System Engineers
Seraphnet’s LLMOps and SDK empower developers to continuously fine-tune and specialize generative agents in a decentralized mesh, supporting robust deployment within dynamic environments.
Bias Mitigation and Normalization
A bias-neutrality layer leverages cross-agent variance and latent factor analysis to minimize bias across outputs, ensuring objective response fidelity through a Maximum Likelihood Estimation recalibration routine.
Privacy by Design
Fully Homomorphic Encryption (FHE) ensures all inference outputs remain encrypted throughout the computational pipeline, securing data integrity and confidentiality without adding latency.
Onchain-Offchain Data Fusion
Hybrid data integration merges blockchain data immutability with offchain databases, using Bayesian data fusion to synthesize verified insights, enhancing accuracy and transparency across the multi-agent network.
Incentivized, Resilient Ecosystem
An incentivized, open-source framework that rewards contributions through agent-tiered scoring and reputation, fostering inter-agent learning and enhancing model resilience through specialization.
Techstack
