Challenges and directions for crypto-based AI agents
1. Before we get started
1-1. Definition of a decentralized AI agent
A decentralized AI agent is a system that harnesses artificial intelligence for automation, learning, and reasoning, while simultaneously ensuring data sovereignty through distributed ledger technologies (blockchains) and consensus mechanisms. By doing so, these agents mitigate reliance on centralized servers or organizations, and empower individual users or communities to control the data they generate or consume. Potential applications range from automated asset management in decentralized finance (DeFi) to decision-support engines in Decentralized Autonomous Organizations (DAOs).
1-2. Why is decentralization important in AI?
🤖 Decentralized AI Agents for Trust, Security, and Next-Generation Applications
Background and Motivation
AI has become a cornerstone of modern industry, fueling innovation in areas like finance, healthcare, manufacturing, and education. However, most AI systems today are centralized, aggregating data and training resources under the jurisdiction of a few major entities. This arrangement has repeatedly raised concerns about data sovereignty, transparency, and equity.
In contrast, decentralized AI agents capitalize on distributed trust to enhance security and accountability, allowing individual stakeholders to define how, when, and to what extent their data is leveraged. By having the broader network verify data and model processes, these agents reduce reliance on traditional centralized platforms and create an ecosystem that is more horizontally structured and community-driven.
1-3. Purpose of This article
- Opportunities of decentralized AI, highlighting the ways in which decentralization can augment data sovereignty, transparency, and collaboration.
- Challenges that arise when centralization is replaced by a distributed architecture, focusing on data curation, token economics, and real-time adaptability.
- Directions for solving these issues, and how Fission aims to integrate AI-centric and blockchain-based solutions to form a viable and scalable ecosystem for Crypto-based AI agents.
2. Opportunities Provided by Decentralized AI
2-1. Data Ownership and Privacy Enhancement
The efficacy of AI models is often driven by large datasets. Yet, in a centralized paradigm, users forfeit genuine data ownership, as much of their personal or proprietary data is held by a single entity. By contrast, decentralized AIleverages blockchain to record data provenance and modifications, maintaining an immutable trail of accountability and enabling users to specify data visibility for AI training or inference tasks.
Moreover, advanced cryptographic techniques (e.g., zero-knowledge proofs, homomorphic encryption) allow for privacy-preserving computation, such that sensitive information can remain hidden while still contributing statistical insights to the learning process. This approach can be especially salient in healthcare or financial sectors, where data confidentialityis paramount. Coupled with tokenized compensation for data providers, these protocols further incentivize high-quality data submissions while respecting individual privacy preferences.
Table 1. Centralized vs. decentralized AI (from a data ownership perspective)
| Category | Centralized AI | Decentralized AI |
|---|---|---|
| Data ownership | Primarily corporate (platform) owned, limited user control | Collectively owned by individual users or DAO communities |
| Privacy | Personal information is centralized on a centralized server, potentially at risk of leakage | Blockchain and cryptographic techniques protect data, minimize exposure |
| Accessibility | Access may be restricted by the platform's policies | Anyone can join the network, with less regional and national boundaries |
- Key Benefit: Users decide how much and what portion of their data is accessible for AI inference, aligning with data sovereigntyand privacy-first practices.
2-2. Trust and Transparency
A fundamental property of blockchain is immutability, which secures every transaction or record against retroactive tampering. Transferring this principle to the AI domain enables Auditable AI. Rather than being relegated to closed-server logs, the training process and inference rationale are stored on a public or consortium ledger, facilitating systematic review and verification by external auditors or community participants.
When integrated with a DAO governance framework, AI agents can record critical decisions or model outputs on-chain, thereby enhancing community trust. This transparency not only reduces data manipulation or malicious misreporting but also galvanizes user engagement, as the community can directly witness and validate AI-driven recommendations.
2-3. Global Collaboration and Open Innovation
Decentralized AI agents, once deployed, can be accessed by anyone with an internet connection, drastically lowering geographic and cultural barriers. This fosters broader innovation via community-driven research and development. Large tasks such as data labeling, complex translations, or algorithmic improvements that once required centralized oversight can now be executed by a global, decentralized workforce, each node(or something acts as such) contributing verifiable work and being compensated accordingly.
Unlike conventional platform-centric crowdsourcing, this decentralized model emphasizes community ownership and collaboration, potentially leading to more sustainable “collective intelligence”. Moreover, the DAO mechanism can extend democratic governance to these AI-driven processes, ensuring that multinational or multi-stakeholder inputs are considered, and fueling continuous innovationloops.
3. Challenges Encountered by Crypto-Based AI Agents
3-1. Human-Dependent Labeling Structures
The quality of an AI model heavily depends on accurate data labeling. Centralized teams or specialized experts may provide high-quality annotations but may also face scalability bottlenecks. Human labor can be time-consuming and costly, especially when cross-border teams are involved, requiring standardization of training processes and sustained motivation. In large-scale scenarios, “expert verification” can become overly resource-intensive, while operational overhead grows due to language, cultural, and logistical differences.
3-2. The challenge of decentralized data labeling
The approach of leveraging blockchain to implement decentralized labeling and distributing incentives to participants through a token reward model has the advantage of facilitating community-driven, autonomous work, but the complexity of the verification (quality control) process and the instability of maintaining the value of tokens can lead to uncertainty in label accuracy and project sustainability. There is also a lack of proven practices at scale, and the ability of Tokenomics designs to work well requires constant observation and refinement.
Decentralized labeling must address quality control:
- Verification protocols can become intricate and costly as double- or triple-check consensus is needed to ensure label consistency.
- Token volatility may discourage long-term participant engagement and hamper dataset sustainability.
- Real-world success stories at scale remain limited, often requiring iterative refinements to tokenomics and community governance.
3-3. Limited Real-Time Adaptability in AI Agents
While all of the projects related to AI agents have addressed the problem of data labelingat some level, it is difficult to say that they have essentially addressed the overall requirements from an AI agent perspective.
Many AI agent systems rely on static or periodically updated datasets, which proves insufficient in domains—like DeFi or fast-evolving governance structures—where on-chain data changes daily or even hourly. In such contexts, if an AI agent cannot dynamically ingest new information and retrain or fine-tune promptly, its decisions or predictions risk quickly becoming outdated.
- Lack of automated fine-tuning with real-time retrieval data can impair reliability.
- Agents may produce inaccurate or hallucinated outputs if the system fails to reflect the latest on-chain states or changing protocol rules.
3-4. Inadequate Evaluation Frameworks for Agents
While many solutions brand themselves as AI “agents”, frameworks for systematically assessing these agents’ accuracy, reliability, adaptability, and hallucination rates are scarce. Traditional performance metrics like F1 scores or simple accuracy do not capture the complexity of multi-step decision-making, particularly in crypto-based contexts (e.g., managing DAO proposals, overseeing investment strategies, analyzing real-time blockchain metrics). Without robust evaluation methods, user or investor trust can be compromised, and continuous improvement remains elusive.
4. Directions for Overcoming Key Challenges
4-1. Hybrid Labeling and Distributed Quality Assurance
A balanced approach to labeling—combining human expertise with semi-automated or AI-assisted annotation—can mitigate the bottlenecks associated with purely manual labor. Adopting hierarchical verification structures (e.g., multi-layer consensus or specialized “expert groups”) ensures robust data accuracy.
- Token incentives must be designed to reward not merely quantity but label precision and long-term consistency, aligning participants’ interests with high-quality dataset creation.
4-2. RAG (Retrieval-Augmented Generation) and Real-Time Updates
In crypto-based contexts, retrieving up-to-date on-chain/off-chain data is paramount. Naïve RAG models with simplistic keyword searches often fail in dynamic environments. Instead, advanced retrieval methods—combining sparse (keyword-based) and dense(semantic-based) indexing—can effectively handle multi-modal data.
- RAG must interface with real-time or frequent data feeds, allowing AI agents to incorporate the latest DeFi or DAO changes.
- Fine-tuning processes should be automated (e.g., RAG-based fine-tuning or RAFT) so that incremental data improvements are systematically integrated into model parameters.
4-3. Comprehensive Agent Evaluation Frameworks
To build user trust and ensure agent reliability, decentralized AI systems need methodical evaluation covering:
- Task Completion Rate: Real-world success in completing user-defined objectives.
- Contextual Consistency: Multi-turn interactions or domain-specific tasks that demand coherence.
- Hallucination Detection: Mechanisms to identify when the agent’s retrieval or generative output diverges from factual data.
- DAO Governance Integration: Indicators of how effectively the agent interfaces with on-chain proposals, community votes, or resource distributions.
Such evaluations serve as feedback loops that drive iterative improvements, forging AI agents robust enough for the rapidly evolvingcrypto ecosystem.
4-4. Stable Tokenomics and Transparent Governance
Token-based projects often suffer from value fluctuations that undermine user confidence. A multi-dimensional token economy—accounting for data validity, user contributions, and domain improvements—can strike a balance between collective intelligence and financial incentives. Moreover, the synergy between tokens and DAO governance fosters community-level checks, so that the entire system remains fair and equitable over time.
5. Fission's Approach:
An AI Agent-Centric Data Layer
5-1. Hybrid Data Labeling with On-Chain Tracking
Fission adopts a collaborative labeling strategy:
- AI performs a preliminary annotation
- Human validators refine or correct this output
- On-chain records track each contributor’s edits and reliability
This not only maximizes efficiency and consistency but also maintains full transparency and ownership traceability, essential for an equitable tokenized reward model.
5-2. Upgraded Retrieval (Hybrid RAG) and RAFT
To tackle crypto’s rapid data flux, Fission implements hybrid retrieval (sparse, dense, and re-ranking). Agents can thus glean accurate and context-rich information from distributed ledgers and off-chain data repositories. Further, Fission’s RAFT (RAG-based Fine-Tuning) module continuously updates model parameters with the latest feedback and newly verified data, enabling real-time adaptability crucial for DeFi, DAO governance, and beyond.
5-3. Integrated Tokenomics for Quality and Participation
Fission addresses the bottleneck of token instability by designing reward metrics that factor in:
- Annotation accuracy and consistency
- Community engagement (proposals, discussions, feedback)
- Agent performance improvements (reductions in error rates or hallucinations)
This multi-dimensional approach fosters long-term ecosystem growth, mitigating the pitfalls of “one-off” labeling sprees or purely volume-driven tasks.
5-4. Agent Evaluation with G-Eval and Hallucination Metrics
Leveraging frameworks like G-Eval (Generalized Evaluation) and specialized Hallucination + RAG assessments, Fission ensures that each agent’s performance is measured from multiple perspectives:
- Technical metrics (retrieval accuracy, inference latency)
- Governance alignment (DAO-proposal interpretation, synergy with community votes)
- Domain-specific success (DeFi risk analysis, NFT curation, etc.)
This holistic evaluation guarantees that Fission’s AI agents remain reliable, transparent, and scalable, aligned with the evolving needs of crypto-based users.
5-5. Designing tokenomics and ensuring long-term sustainability
Fission utilizes a multi-dimensional reward metric to ensure that community engagement is not a one-off, but a long-term ecosystem contribution. Rather than simply rewarding the “quantity” of labels, Fission distributes tokens based on a combination of accuracy, meaningful feedback (objections, contributions to discussions), and contributions to improving agent learning performance. By doing so, we aim to mitigate the dichotomy of “rising labor costs vs. declining quality” that other projects have faced, and to stabilize the “token value volatility” issue in the long term.
6. Conclusion
Crypto-based AI agents hold tremendous potential: they can enhance transparency, distribute data ownership, and drive community-led innovation. Yet they simultaneously face intricate challenges, including data labeling bottlenecks, tokenomics volatility, real-time adaptation, and evaluation complexities. Many existing attempts in the field underscore these pitfalls, revealing how purely human-dependent workflows, naive retrieval, or simplistic token models can falter under practical constraints.
Fission proposes an integrated approach that reimagines how data is labeled, how retrieval is performed (Hybrid RAG + RAFT), how participants are rewarded, and how agent performance is rigorously evaluated. By centering on a robust AI Agent-Centric Data Layer, Fission aims to deliver sustainable solutions that unify technical innovation, token-based governance, and community participation.
In the long term, the success of decentralized AI agents in crypto-based ecosystems hinges on transparent governance, secure data pipelines, adaptive learning architectures, and holistic evaluation frameworks. Fission’s model aspires to chart a path that resonates with the core philosophy of decentralization—empowering individuals and communities—to realize truly decentralized intelligence in a constantly evolving Web3 environment.