AgenticAI

Solidus Ai Tech is proud to announce its strategic partnership with fetch ai, marking a major leap forward in AI + Web3 integration. The collaboration brings ASI-One, fetch ai's proprietary Large Language Model (LLM), directly to the AITECH AI Marketplace, now live and accessible to developers, researchers, and enterprises.

ASI-One is no ordinary model, it’s built on the principles of agentic AI, allowing it to autonomously think, reason, and act in dynamic, goal-oriented environments. With advanced capabilities like multi-step task execution, contextual memory, and API-based integration, ASI-One empowers users to create intelligent agents with real-world functionality and Web3-native infrastructure.

This partnership perfectly blends fetch ai advanced LLM technology with AITECH’s robust marketplace and compute infrastructure, creating a powerful ecosystem for building and deploying AI at scale.

Key benefits of ASI-One:

Agentic Reasoning

Human-like Natural Language Understanding

Context-Aware, Multi-Step Task Handling

Seamless API Integration

With over 999,000 access slots and token-based pricing, users can explore the power of ASI-One starting for FREE using AITECH tokens.

This is just the beginning. Solidus Ai Tech and Fetch ai plan deeper collaboration to accelerate innovation across decentralized AI ecosystems.
#AITECH #Fetchai #AgenticAI
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#MindNetwork全同态加密FHE重塑AI未来
AI is not just a tool; it is becoming an actor. To make it trustworthy, it relies not on trust but on the fact that it does not see your data from beginning to end.

FHE is not an accessory to AI; it is its lifeline: Mind Network is rewriting the rules of the game.

Imagine a future where your AI assistant not only schedules appointments and organizes emails but also helps you file taxes, run DAOs, and even execute on-chain transactions. It knows your assets, preferences, style, and even simulates you to help you 'make decisions.'
This is the era of 'Agentic AI.' But the question is: do you dare to share this data?

The interview with Todd Ruoff - #Autonomys CEO lays out an enticing vision for ethical, transparent, decentralized AI. Three overriding themes seem to prevail: the need for open source and on-chain transparency; #AutonomysNetwork ' agentic framework for #AI operationalization, accountability, and memory; and how decentralization of control over AI really matters in the real world.

As a social miner on @DAO Labs #SocialMining Galaxy, I will take you on a tour through his insights.

#AgenticAI $AI3

Open-Source and On-Chain Transparency: Foundations for Ethical AI
For Ruoff, the force behind Autonomys’ ethical AI approach is an unwavering support for open-source development. He argues that AI made in open source forces consumer trust that technology is free from hidden bias, and to emphasize, the code and training data are open for any kind of auditing. Such transparency is certainly absent in a closed-source system, which in reality is akin to a "black box" that might obscure or mute unethical behavior. Thus, Ruoff made sure that everything under Autonomys was open-source by recording AI interactions on-chain, embracing that every decision and process is visible and also immutable and verifiable. In a way, this type of transparency could be the very glue for public trust and holding the AI systems to the highest ethical standards.

Autonomys’ Agentic Framework: Accountability and Memory for AI Agents
Another standout theme is the innovative agentic framework developed by Autonomys to directly tackle the problems of AI accountability and memory. Ruoff explains that the AI agents developed by them, such as 0xArgu-mint, have an entire memory and reasoning process recorded on-chain. Thus, every interaction, every decision, and even the internal logic of the agent are open for review forever. This framework, in practice, allows for what Ruoff calls a "digital, immutable autopsy" upon the agent's behavior: the highest level of transparency and ability to investigate and learn from the behavior of AI in cases, especially when things go wrong. By providing AI agents with self-sovereign identities and permanent, auditable histories, Autonomys has set a standard for responsible AI to surpass.

Decentralizing Control: Safeguarding AI as a Public Good
Finally, Ruoff sought decentralization in addressing one of the AI industry's most pressing risks: concentration of power. According to him, the way things are now, only a few corporations call the shots in determining the direction and design of AI technologies. Autonomys arose against this backdrop with the alternative promise of being distributed and decentralized, so that no single entity (Autonomys themselves included) may ever assume unilateral control over the application of AI. Apart from granting access to AI, this approach also mitigates the likelihood of abuses of power, serving instead as a fertile ground for inclusiveness and friendly innovation. In Ruoff's own words, AI "should be a public good, not a corporate asset"—a vision that finds itself in many minds in the ever-climbing concern for digital sovereignty and privacy.

Conclusion
Ruoff's insights have illuminated a way forward, calling for the unfolding of open-source transparency, strong agentic architectures, and decentralization, which are no longer just technical choices but must be the ethical compass. His governance at Autonomys instills confidence that it must be possible to build AI systems that respond to safety, accountability, and serve the public interest.