Sentient In-Depth Research Report: Secures $85 Million in Funding to Build a Decentralized AGI New Paradigm

Original Article Title: "AI L1 Deep Research Report Series: Sentient: Building an Open AI Platform with $85 Million"

Original Article Authors: 0xjacobzhao, Biteye

1. Project Introduction:

Sentient is dedicated to building a decentralized artificial intelligence economy open-source protocol platform. Its core objective is to establish ownership structures for AI models, provide on-chain invocation mechanisms, and build a composable, revenue-sharing AI Agent network. Through the "OML" framework (Open, Monetizable, Loyal) and model fingerprinting technology, Sentient addresses the fundamental issues of "unclear model ownership, untraceable invocation, and unfair value distribution" in the current centralized LLM market.

The project is driven by the Sentient Foundation, focusing on open-source AGI and protocol incentive mechanism construction. The advocated "Loyal AI" refers to an open AI model ecosystem that serves the community, practices fair governance, and can evolve autonomously in the long term.

Figure 1: The architecture of the Sentient Protocol consists of two core components: the blockchain system and the AI pipeline

The AI Pipeline is the foundation for developing and training "Loyal AI" artifacts, consisting of two core processes:​

· Data Curation:​ A community-driven data selection process for model alignment.​

· Loyalty Training:​ Ensuring the model undergoes training consistent with the community's intent.

The blockchain system provides transparency and decentralized control to the protocol, ensuring ownership and governance of AI artifacts, with key modules including:​

· Governance:​ Controlled and decided by a decentralized autonomous organization (DAO).​

· Ownership:​ Representing AI artifact ownership through tokenization.​

· Decentralized Finance (DeFi):​ Providing financial tools that support open, decentralized, fair governance, and rewards.​

2. Technical Architecture and Model Resentment Mechanism:

1. OML Model Framework

The Sentient: Loyal AI Whitepaper proposes the OML framework Open, Monetizable, and Loyal AI, which starts with model resentment and first systematically proposes the concept of "AI-native Cryptography," aiming to provide encryption-level ownership protection mechanisms for open-source models.

· Open: The model must be open source, with transparent code and data structures that support community replication, auditing, and forking;

· Monetizable: Each model invocation triggers revenue flow, which is allocated to trainers, deployers, and validators through on-chain contracts;

· Loyal: The model does not belong to a company but to the contributor community, with the model upgrade direction and governance determined by a DAO. Model ownership is verifiable, modifications are restricted, and usage is controlled.

OML, through on-chain mechanisms and cryptographic means, ensures that open-source models maintain openness while having economic and governance sovereignty. It constructs an AI-native protocol layer of usage rights and revenue rights to ensure model transparency, clear ownership, economic incentives, and behavioral governance.

Core Concept: AI-native Cryptography

AI-native cryptography leverages the continuity of AI models, low-dimensional manifold structure, and model differentiability to develop a "verifiable but non-removable" lightweight security mechanism. Its core technologies are:

· Fingerprint Embedding: Inserting a set of hidden query-response key-value pairs during training to form a unique model signature;

· Ownership Verification Protocol: Verifying whether the fingerprint is retained in query form by a third-party detector (Prover);

· Permissioned Invocation Mechanism: Prior to invocation, obtaining a "permission credential" issued by the model owner to authorize the system to decode the input and return the correct answer.

This approach can achieve "behavior-based authorization call + ownership verification" at no additional encryption cost.

Sentient currently adopts Melange hybrid security: combining fingerprint resentment, TEE execution, and on-chain contract revenue sharing. The fingerprint methodology is the mainstream implementation of OML 1.0, emphasizing the "Optimistic Security" concept, where default compliance is assumed, and violations are detectable and punishable.

OML and Sentient Protocol Protocol Architecture

The final chapter of the paper proposes a complete on-chain protocol (Sentient Protocol) to support OML:

· Storage Layer: Stores model weights and fingerprint registration information;

· Execution Layer: Authorization contract controls model invocation entry points;

· Access Layer: Verifies user authorization through proof of permission;

· Incentive Layer: Profit routing contract allocates payment for each invocation to trainers, deployers, and validators.

2. Fingerprinting and Model Ownership Mechanism

GitHub: https://github.com/sentient-agi/oml-1.0-fingerprinting

This repository is the first implementation of the Sentient fingerprinting mechanism, providing a fingerprint injection and verification interface that can be embedded in the training process. Its purpose is to ensure verifiability of model ownership and traceability of usage behavior, preventing unauthorized copying and commercialization. This is a specific engineering implementation of the OML framework.

The essence of the fingerprinting mechanism is: by fine-tuning the model, embedding a set of unique "key-response" pairs, the model owner can verify if the model belongs to them through specific queries, thus creating a "cryptographic signature" for the model.

3. Enclave TEE Computing Framework

GitHub: https://github.com/sentient-agi/Sentient-Enclaves-Framework

The Sentient Enclaves Framework is an open-source framework that leverages trusted execution environments (TEEs) such as AWS Nitro Enclaves to achieve secure deployment of model inference, fine-tuning, and proxy services. This framework emphasizes the "loyalty" of the model, ensuring that the model only responds to authorized requests, preventing unauthorized access and usage.

The TEE (Sentient Enclaves Framework) excels in high performance and cloud integration, suitable for real-time AI and sensitive data processing, but is limited by hardware dependencies and side-channel attacks. Compared to other encryption technologies, FHE provides hardware-independent and post-quantum secure strong privacy guarantees, but with significant performance overhead, making it challenging to directly replace TEE for high-performance tasks. ZK performs excellently in verifiability and decentralization scenarios and can serve as a complement to TEE (this module is planned to integrate zkML in the future).

4. Sentient Agent Framework

GitHub: https://github.com/sentient-agi/Sentient-Agent-Framework

The Sentient Agent Framework is a lightweight open-source framework that focuses on using an AI agent to control the browser for Web task automation (such as search and video playback). It combines natural language instructions to provide a concise development experience (claiming 3 lines of code). This architecture supports building an intelligent agent with a complete "perceive-plan-execute-feedback" loop. Compared to traditional AI Agent Frameworks, the Sentient Agent Framework is limited in functionality, lightweight, and concise, making it more suitable for off-chain Web tasks.

5. Sentient Social Agent

GitHub: https://github.com/sentient-agi/Sentient-Social-Agent

The Sentient Social Agent is aimed at building an AI system for automating interactions on social platforms (Twitter, Discord, and Telegram). It can understand the social context, generate content, interact with users, and engage in social exchanges through multi-agent collaboration. This system can be integrated with the Sentient Agent Framework.

6. Open Deep Search (Not Launched Yet)

On the Sentient official website, Open Deep Search is defined as a search agent that surpasses ChatGPT and Perplexity Pro. Team member Sewoong Oh revealed part of the roadmap at the EthDenver 2025 Open AGI Summit:

Open Deep Search consists of two main parts: Sensient's search functionalities (including query rephrasing, URL and document handling, etc.) and the reasoning agent. The reasoning agent leverages open-source Large Language Models (LLMs) like Llama 3.1 and DeepSeek, enhancing search quality through tools like search, calculators, and self-reflection. On the Frames Benchmark, Open Deep Search outperforms other open-source models and can even rival some closed-source models. However, as its functionality is not yet launched, we currently cannot assess its real capabilities.

3. Product Form, Implementation, and Planning

The products currently showcased on the Sentient official website are primarily Sentient Chat, a chat conversation platform, and the open-source Dobby LLMs model:

Sentient Chat:

Sentient Chat is a decentralized AI chat platform launched by the Sentient Foundation. This platform integrates open-source large language models (such as the Dobby series) with an advanced reasoning agent framework. Its core features include:

1. Open Reasoning Agent: The built-in reasoning agent in Sentient Chat can perform complex tasks, supporting tools like an Ontology-driven Search (ODS), calculator, and code execution.

2. Multi-Agent Integration: The platform supports integrating multiple AI agents, allowing users to interact with different agents as needed. This is similar to a Web3 version of POE or an open, agent-driven Perplexity alternative.

Sentient Chat is currently in the testing phase and is only accessible through invitation codes distributed via email or community activities. According to official public information, over 5,000 users have successfully obtained access to Sentient Chat, with over 100,000 user queries processed. As the author is not currently a white-listed tester, the true capabilities of its models cannot be evaluated at this time.

Dobby LLM Model Series:

1. Dobby-Unhinged Series

· Dobby-Unhinged-Llama-3.3-70B: Based on the Llama 3.3-70B-Instruct fine-tuning, emphasizing personal freedom and a stance on cryptocurrency, with a direct, humorous, and personable conversational style.

· Dobby-Mini-Unhinged-Llama-3.1-8B: An 8B parameter version suitable for resource-constrained devices.

2. Dobby-Mini-Leashed-Llama-3.1-8B: A milder tone suitable for applications requiring more robust outputs.

Since the Dobby LLM model is a fine-tuned version based on Llama 3.1 and 3.3, we believe its main applications lie in building chatbots, content generation and creation, role-playing agents, etc. Its advantages include flexible style generation, enhanced reasoning, and low resource requirements, making it suitable for quick deployment and flexible customization in resource-constrained environments. Compared to more powerful closed-source models like GPT-4, there is still a gap in Dobby LLM when handling tasks involving advanced logic, cross-domain knowledge reasoning, and deep reasoning.

IV. Ecosystem Collaboration and Real-World Scenarios

The Sentient Builder Program currently offers $1 million in funding to support developers in building an AI Agent to run within the Sentient Chat ecosystem. Developers are required to use Sentient's development kit and integrate with its Agent API.

Simultaneously, the ecosystem partners announced on the Sentient official website cover projects in various Crypto AI domains, as follows:

Figure 2: Sentient's AI Ecosystem Partners

As a leading project in the Crypto AI field, Sentient's resource integration capabilities can encompass any star startup within the industry. However, it should be noted that the prevalence of "marketing-oriented" partnerships in the Crypto sector has created an illusion of industry prosperity. The contribution and loyalty of Sentient's ecosystem partners still require our continuous observation.

The Open AGI Summit is an internationally oriented conference initiated by the Sentient team dedicated to exploring the integration of Artificial Intelligence (AI) and cryptographic technology (Crypto). I had the privilege to attend its summits during ETH Denver in 2024 and ETHcc in 2025. The Sentient team has the ability to gather top-tier institutional investors and project entrepreneurs from the industry, making it a highlight.

V. Team Structure and Research Background

The Sentient Foundation has brought together top academic experts, crypto industry entrepreneurs, and engineers worldwide, committed to building a community-driven, open-source, and verifiable AGI platform. According to the official information released, the team members are primarily:

Core Leadership Team (Steering Committee)

· Pramod Viswanath – Forrest G. Hamrick Professor at Princeton University, with long-term research in information theory and communication systems, leading Sentient's AI security and theoretical foundation development.

· Himanshu Tyagi – Professor at the Indian Institute of Science, specializing in privacy protection and decentralized learning algorithms, providing academic support for model training and privacy collaboration.

· Sandeep Nailwal – Co-Founder of Polygon, responsible for blockchain strategy and global ecosystem development, is a key figure in connecting the crypto community with AI architecture.

· Sensys Team – Web3-native product studio, leading user experience optimization and developer infrastructure development, driving the adoption of the Sentient product.

Core Engineering and Development Team: Comprised of team members from renowned tech and blockchain companies such as Meta, Coinbase, Circle, Polygon, Binance, as well as researchers from top universities such as Princeton University, University of Washington, and Indian Institutes of Technology. AI Research and Model Training Team: The research team covers AI/ML, NLP, computer vision, and reinforcement learning, with members having practical experience at institutions like Google Research, Daimon Labs, Fetch.ai, and more.

It is worth noting that Sentient was founded with the successful track record of Polygon's co-founder Sandeep Nailwal. As a key scaling solution within the Ethereum ecosystem, Matic initially relied on Plasma, a technology that was not cutting-edge but was "cheap and fast" enough, to build Polygon's moat in areas such as NFTs and social, while also integrating ZK technology into its blockchain scaling solutions through acquisitions like Mir Protocol and Hermez Network and the launch of Polygon zkEVM. Sentient, as Sandeep Nailwal's second entrepreneurial endeavor, benefits from his experience, funding, network, and market recognition, allowing it to raise significant funding in 2024 based on an imperfect project concept. However, the AI field is inherently different from Crypto, and Sentient still faces challenges in adapting to changes in the new market environment, increased competition, technological advancements, and more.

Six, Funding Status and Token Model

In 2024, Sentient raised an $85 million seed round led by Founders Fund, Pantera, and Framework Ventures. The token has not been released yet. The current Agent incentive points can be mapped to tokens in the future. The token can be used for proposal voting on model version management, staking to validate Agent outputs' authenticity, governance signaling, and more.

Figure 3: Sentient Funding Status

Sentient is a kingpin project born with a silver spoon, with its investor background, funding scale, and valuation setting the bar high for most Crypto AI projects in the market. On one hand, its strong resource endorsement can more easily integrate resources from various industries, a high funding amount can more easily recruit top talents to join its team, and a solid capital base can support the project's development through industry cycles. However, on the other hand, the current Crypto industry generally demystifies high-valued projects with VC endorsements. Furthermore, VC-backed projects tend to prioritize price appreciation through capital operations and are severely disconnected from fundamentals. Assuming that Sentient fails to deliver impactful Crypto AI products and instead chooses to issue tokens at a high valuation, it will ultimately harm the Crypto community, which is in urgent need of rebuilding trust. How the team responds to the current industry predicament is worth our continuous observation.

Seven, Competitor Analysis and Market Positioning

Most Crypto AI projects in the market mostly focus on a single area such as data, models, computation, training, or inference, or develop consumer-level applications such as AI Agents. Projects positioning themselves as AI Chains include projects focusing on the transformation of public chains with AI (such as Near and ICP) or decentralized resource sharing coordination and token incentive protocols like Bittensor, a positioning that does not fully match Sentient's. On the model training side, Sentient is more like an integration platform and has a cooperative relationship with open-source AI models on the market. On the Agent side, Sentient competes with projects like Talus, Olas, or Theoriq in terms of multi-agent systems and reasoning capability, but each project still has different core goals and application scenarios, maintaining complementarity.

Eight, Conclusion

Sentient, as a decentralized Artificial General Intelligence (AGI) protocol platform, aims to provide clear ownership structure for AI models and enable on-chain mechanism for invocation and value distribution, addressing the current ambiguity and unfairness in ownership in the centralized Large Language Model (LLM) market. The core framework OML (Open, Monetizable, Loyal) ensures ownership, transparency, and fair revenue sharing for open-source models through model fingerprinting and blockchain technology. With the support of top VCs and AI ecosystem partners under the backing of Polygon co-founder Sandeep Nailwal, Sentient, despite facing uncertainties, controversies, and competition, still aims to become one of the standard protocols for decentralized AI ownership, driving the decentralized development of AGI.

This article is a contributed piece and does not represent the views of BlockBeats.