web3ai

Autonomous AI agents are no longer confined to the role of digital assistants. They are rapidly evolving into independent, on-chain decision-makers capable of operating with minimal human supervision. From executing trades and managing DeFi positions to analyzing complex datasets and participating in governance, these agents are becoming first-class actors within Web3 systems.
This shift marks the rise of agentic AI—a paradigm where intelligence is not only automated, but also accountable, transparent, and natively integrated into decentralized infrastructure.
The Rise of Agentic AI in Web3
Agentic AI frameworks enable software agents to:
Make autonomous decisions
Interact directly with smart contracts
Adapt to changing conditions in real time
Operate transparently on-chain
Unlike traditional AI systems that rely on centralized execution and opaque data pipelines, agentic AI in Web3 embeds automation into environments where actions are verifiable and auditable by design.
Several early projects already demonstrate this shift:
Virtuals Protocol, where tokenized AI agents act on behalf of users
AI16Z, exploring AI-driven DAO structures and autonomous investment strategies
AIxbt, market-focused agents capable of real-time pattern analysis and insight generation
Together, these systems point toward a future where decentralized AI optimizes decision-making across finance, gaming, governance, and data analysis—without requiring constant human oversight.
Why Data Quality Is the Bottleneck
As powerful as agentic AI can be, its effectiveness is constrained by one fundamental factor: data quality.
Autonomous agents operating in real-world environments must learn from data that is:
Accurate
Representative
Up to date
Aligned with human intent
Without this, AI agents risk becoming brittle, biased, or misaligned—particularly in high-stakes domains like finance and governance.
This is where OORT plays a critical role.
OORT’s Role in the DeAI Stack
OORT provides the data infrastructure required to make agentic AI reliable and accountable.
Through its DataHub, OORT supplies human-verified datasets, ensuring that training data reflects real-world conditions rather than synthetic or low-quality inputs. Complementing this is OORT’s Reinforcement Learning from Human Feedback (RLHF) framework, which allows AI agents to learn preferences, behaviors, and constraints directly from verified human signals.
These components form a foundation for training autonomous agents that can operate safely in decentralized environments.
Enabling Verifiable and Adaptive AI Workflows
OORT’s infrastructure supports a wide range of AI workflows, including:
Training models on financial candlestick and market data
Decentralized RLHF tasks to refine agent behavior
Preference learning for governance and decision systems
Continuous adaptation to evolving market conditions
By decentralizing data contribution and verification, OORT ensures that learning processes remain transparent, auditable, and aligned with human expectations.
This is particularly important for agentic AI systems, which must balance autonomy with accountability. With OORT, developers can create agents that make confident decisions while remaining traceable and explainable.
Agentic AI as a Core Layer of Web3
As adoption accelerates, agentic AI systems will play an increasingly central role in Web3 infrastructure. They can:
Autonomously manage portfolios and liquidity
Interact with smart contracts on behalf of users
Optimize network resources
Participate in DAO governance
Analyze data streams in real time
Crucially, when combined with OORT’s RLHF services, these agents can be continuously refined to recognize patterns, avoid known pitfalls, and respond intelligently to changing environments—without sacrificing security or decentralization.
A New Paradigm for Decentralized Intelligence
Understanding agentic AI requires more than familiarity with models or tokens. It demands insight into:
How autonomous agents operate on-chain
How datasets are sourced, verified, and updated
How RLHF aligns machine behavior with human values
Together, these components define a new paradigm: AI that acts independently, learns from high-quality human feedback, and remains fully verifiable within decentralized systems.
Conclusion: The Future Is Already Here
Agentic AI and OORT’s ecosystem demonstrate that the next generation of autonomous on-chain agents is not theoretical—it is already emerging.
By aligning intelligent agents with verified data and decentralized infrastructure, OORT helps enable a future where AI systems are not only powerful, but trustworthy, transparent, and accountable.
As DeAI continues to evolve, these foundations will drive innovation, efficiency, and adoption across the Web3 landscape—transforming autonomous agents from experimental tools into reliable participants in decentralized economies.
#DeAI #OORT #RLHF #Web3AI #XPL $XPL

Проблема с доработкой ИИ

Индустрия блокчейна спешит добавить функции ИИ. Большинство проектов прикрепляют возможности ИИ к существующей инфраструктуре, создавая фрагментированные системы, которые лишены согласованной архитектуры. Этот подход в корне неверно понимает, что требует инфраструктура, основанная на ИИ.

Настоящие ИИ-системы требуют четырех основных возможностей на уровне протокола: постоянной памяти, родного рассуждения, автономной автоматизации и доверительного расчет. Метрики скорости, такие как TPS, являются стандартом. Теперь важно, может ли инфраструктура поддерживать интеллектуальные операции с самого начала, а не как после мысли.

Искусственный интеллект все больше внедряется в то, как современные системы принимают решения — от финансового прогнозирования и моделирования рисков до автоматизации и оптимизации. Несмотря на это растущее влияние, большинство современных ИИ-систем остаются закрытыми, централизованными и контролируемыми небольшим числом организаций. Эти силосы ограничивают прозрачность, адаптивность и подотчетность.
Allora (ALLO) представляет собой принципиально новую модель: децентрализованная сеть, где интеллект создается совместно, координируется с помощью криптографических стимулов и постоянно уточняется на основе измеримых результатов.