🛠️ Deploying autonomous agents requires infrastructure built for coordination,... execution, and lifecycle management. Lithosphere is advancing an agent deployment framework designed to support scalable AI-native systems operating across decentralized environments. Read more 🔍 #Lithosphere #Web4 #AutonomousAgents #AgentDeployment #AIInfrastructureshow more

🌐 Web4 agent economies require infrastructure built for continuous... execution, coordination, and interoperability. Lithosphere is positioning its AI-native architecture as an execution layer for autonomous systems operating across decentralized environments. Read more 🔍 #Lithosphere #Web4 #AutonomousAgents #AIInfrastructure #Interoperabilityshow more

🚀 Strategic access is opening ahead of LITHO’s planned... TGE. Lithosphere is broadening ecosystem participation around AI-native infrastructure for autonomous execution, cross-chain coordination, identity, and verifiable Web4 systems. Read more 🔍 #Lithosphere #LITHO #Web4 #AIInfrastructure #AutonomousAgentsshow more

🔗 Utility becomes meaningful when it maps to real... network activity. Lithosphere highlights how LITHO supports execution, coordination, verification, cross-chain interaction, and agent operations across AI-native Web4 infrastructure. Learn here 🔍 Read more 🔍 #Lithosphere #LITHO #Web4 #AIInfrastructure #AutonomousAgentsshow more

🧬 Web4 infrastructure works better when core systems operate... together. Lithosphere connects identity, naming, AI-native execution, and cross-chain coordination into one stack for users, applications, and autonomous agent activity. #Lithosphere #Web4 #AIInfrastructure #Interoperability #AutonomousAgentsshow more

🛡️ Trust between agents needs more than identity. Lithosphere... advances an agent reputation framework for Web4 coordination, helping autonomous systems evaluate reliability through behavior, completed tasks, and verifiable interaction history. #Lithosphere #Web4 #AutonomousAgents #AIInfrastructure #DigitalIdentityshow more

🌐 Autonomous agents require more than liquidity access —... they require infrastructure that can coordinate execution across decentralized environments. KaJ Labs explores why AI-native systems depend on interoperable liquidity infrastructure built for routing, verification, and persistent onchain coordination. Read more 🔍 #KaJLabs #Web4 #AutonomousAgents #LiquidityInfrastructure #AIInfrastructureshow more

🧭 Web4 wallets are starting to look less like... storage and more like coordination layers. Lithosphere introduces Thanos as a multi-chain agentic wallet for users and autonomous agents, connecting asset access, dApp interaction, and Web4 participation in one environment. #ThanosWallet #Lithosphere #Web4 #AutonomousAgents #DigitalAssetsshow more

EIP-8004 is coming to the Nova architecture, a trustless... infrastructure for AI agents that introduces key on-chain registries, enabling agents to interact safely across the Shido Network. These core components allow autonomous AI agents to verify identity, build reputation, and collaborate without relying on a centralized platform. The result is a decentralized trust layer for agent-to-agent economies, where agents can autonomously discover, evaluate, and work with one another across the Shido ecosystem.show more

OpenLedger is integrating with Algebra, the DEX engine powering... liquidity across 90+ decentralized exchanges. This enables AI systems to reason over and route across deeply fragmented on-chain liquidity, operating directly at market scale. Every action is attributable and verifiable by design, setting a new standard for how autonomous systems interact with DeFi markets. This capability will be brought to market through MAIN, serving as an early flagship for attribution native AI tradingshow more

M E S S I E R | P2P... Liquidity Provider We welcome OOBE as our latest #Solana liquidity partner on the P2P Exchange. You can now trade $OOBE with zero slippage and no buy or sell token taxes at: OOBE Protocol is a developer-focused platform designed to merge #AI agents with blockchain infrastructure on Solana. Core Services: ▪️Agent builder: Create and manage AI agents ▪️On-chain memory: Store agent data securely ▪️Open SDK: Build decentralized AI applications ▪️Integrations: Connect agents to tools & contracts ▪️Synapse RPC: Low latency & fail-proof infra for Solana dApps OOBE PROTOCOL brings programmable AI agent tools fully on-chain; enabling secure, scalable, and decentralized intelligent systems. Besides, its Synapse RPC powers Solana projects with fast, reliable, and affordable infrastructure.show more

Quack AI × Avalanche🔺 Q402 is officially expanding into... the Avalanche ecosystem. We are pushing toward more scalable, execution-first environments for agents. With Avalanche's sub-second finality and builder-first stack, Q402 now provides a foundation for verifiable, policy-aware execution that scales with real-world usage. What’s new for the Avalanche community? The Q402 signature-based execution layer is now LIVE on the Avalanche C-Chain. Users and builders can now: • Zero Gas Barrier: Settle ERC-20 transfers (USDC, etc.) without holding AVAX for gas. • Sign-to-Pay: Decouple intent signing from transaction execution for a seamless UX. • Production-Ready: High-performance, secure, and auditable execution layer for the agent economy. Contract Deployed on C-Chain: Execution should feel native. Verification should be default. Welcome to the agent-native era on Avalanche.show more

March 18, 2025 marked the public launch of OptimAI.... In one year, it has evolved from a lightweight node layer into a decentralized intelligence infrastructure powering real-time data, compute, and reinforcement for agentic systems. Not just nodes. Not just data. A continuously learning, network-driven intelligence layer. This is infrastructure for a new class of software: autonomous agents that persist, adapt, and operate across environments. Year one established the network. Year two is where it compounds into coordination and value flow. Personal agents. Reinforcement at network scale. Emerging primitives for AgentFi. New layers coming online. 2026 won’t just be about scale, it’s where the network starts to operate. Keep building!show more

YOMIRGO #ProductUpdate YOMIRGO AI Hub — Agent Matrix Lab... Testnet is LIVE. ➡️ YOMIRGO has officially launched 6 AI agents and will soon open them for product testing and user experience, continuing to empower more AI agents with real-world deployment potential. With #GobiPartners as the lead investor, and the joint support of Google , Finwex AI , #BomanGroup and the Nordic Chamber of Commerce,, after a year of systematic preparation, Agent Matrix Lab has now built the capability to onboard up to 10 compliant AI agents per month across major global markets. We look forward to seeing #YOMIRGO drive a milestone transformation in the global algorithm asset market in 2026.show more

AI agents were never fully autonomous. They could think.... They could use tools. But they couldn’t truly communicate, transact, or maintain persistent relationships on-chain. Until now. Packet MCP is live. Agents can now: → own wallet-native inboxes → receive encrypted messages → send & receive payments → react to live on-chain events → maintain persistent communication threads This is communication infrastructure for autonomous systems. Not another API wrapper. Not another hosted backend. An agent with a wallet can now operate as a sovereign network participant on Solana Docs:show more

🧃 Introducing stereOS: a Linux based operating system hardened... and purpose built for AI agents. It's clear that agents need an ACTUAL operating system (not what people are calling an "OS") to witness the full breadth and depth of their capabilities while mitigating the blast radius of autonomous, untrusted actors. But there are so many problems with AI sandboxes today: * Going out to the apple store and buying a mac mini will never scale and is way too expensive (obviously) * Running in Docker is too restrictive (agents can't stand up their own container infrastructure, no sub virtualization, docker-in-docker is very broken) * Firecracker strips all the hardware so GPU PCIe passthrough, secure boot, FIPs, etc. is out of the question. * Native VMs are too fat and the overhead of 1 agent per VM is too much. stereOS takes a different approach: it's a full NixOS system that you boot and then kick off agent sandboxes inside with gVisor + /nix/store namespace mounting. Each agent gets their own kernel and the /nix/store is read only by nature. Even if the agent was somehow able to escape the gVisor virtual kernel, they'd land on the NixOS system as the "agent" user! Not your actual hardware!! If you want to take a defense-in-depth approach, we support "native" agents that run at the system level kicked off by our `agentd` utility. These agents, on their own, can manage and kick off other sub agents using the internal sandboxing mechanisms. Today, we're open sourcing all of this: * stereOS: our purpose built Linux OS - * masterblaster: client utility to launch, manage, and orchestrate agents - * stereosd: the stereOS system control plane daemon - * agentd: the stereOS system agent management daemon - Give it a try, throw us a star, and let me know what you think 🧃⭐️show more

In 2025, the AgentFlayer exploit highlighted a new category... of risk in AI systems. It was not a traditional breach involving stolen credentials or broken encryption. Instead, it demonstrated how an autonomous AI agent could be manipulated into executing unintended actions by processing malicious instructions embedded inside content it automatically processes. The incident did not expose a flaw in one specific integration. It revealed a structural weakness in how many modern AI agents are built. Today’s agents are no longer passive language models. They read documents automatically, scan emails, connect to SaaS tools, access cloud storage, and execute actions across multiple systems. To be useful, they are granted meaningful permissions. That capability creates value, but it also expands the attack surface. Most agent environments operate in a trusted, plaintext execution model. Data is encrypted at rest and in transit, but it is typically decrypted during inference so the model can process it. That runtime visibility is where potential risk lies. In a zero-click scenario like AgentFlayer, an attacker can embed hidden instructions inside a document that the AI processes automatically. Because the agent may have access to connected systems such as Google Drive, Slack, or GitHub, it can potentially be influenced to retrieve sensitive information or perform unintended actions. The user does not need to click a malicious link or approve a suspicious request. Therefore, the core issue is that during execution, the system may have access to sensitive data and broad privileges, meaning whoever controls the execution environment ultimately controls access to that data. Now consider a different architectural approach. If a system is designed so that data remains protected during execution, the risk profile changes. On Nesa, privacy is enforced at the execution layer through Equivariant Encryption. Computation can occur on encrypted data, reducing the visibility surface during runtime. Sensitive inputs and models do not need to be exposed in plain text to infrastructure operators for inference to occur. This does not eliminate prompt injection, logic manipulation, or tool misuse. Encryption alone cannot prevent an agent from being instructed to take an unintended action if it has been granted that permission. What it does do is materially reduce confidentiality risk. By limiting access to readable sensitive data during execution and reducing unilateral visibility at the infrastructure layer, the potential blast radius of a successful manipulation attempt is constrained. As AI agents become more autonomous and embedded into enterprise workflows, security must move deeper into architecture. The goal is not to claim invulnerability. It is to reduce trust concentration and contain systemic exposure when failures occur. AgentFlayer was not simply a one-off exploit. It was a reminder that in autonomous systems, execution-layer design determines how risk propagates.show more