Agentic ID (ERC-7857): This is the "passport" for the AI world. As AI agents start buying things or accessing data on your behalf, they need a way to prove who they are and what they’re allowed to do. Agentic ID provides a standardized on-chain identity for these agents, acting as the security and permission layer.
Zero Studio: Think of this as the "Pro" version of the 0G App. It’s a specialized environment for developers to fine-tune AI models and deploy autonomous agents. It leverages the 0G stack to make model training significantly cheaper and faster than traditional cloud providers.
Company in a Box: This is designed for "AI Founders." It bundles everything needed to launch an AI startup on-chain: legal frameworks, token issuance tools, and instant deployment (one-click to Vercel). It’s meant to lower the barrier to starting a decentralized AI company to just a few clicks.
0G Pay: To make an "Agentic Economy" work, agents need to be able to pay each other. 0G Pay is the high-speed settlement layer that allows AI agents to buy compute power, data, or services from one another instantly with minimal fees.
Ghast AI (Beta): Launched as a Web3-native assistant, its "killer feature" is Memory as an Asset. Instead of a central company like OpenAI owning your chat history, Ghast encrypts your conversations and stores them on-chain. You can actually trade or port this "memory" as a digital asset, allowing your AI's learned context to follow you across different platforms.
0G App: This is the flagship consumer platform. It uses natural language (plain English) to let you build AI applications. It's essentially "ChatGPT for app development" but with a Web3 twist: the apps you build run on decentralized compute and use Trusted Execution Environments (TEEs) to ensure your data stays private and the AI’s output is verifiable.
ERC-8004: Trustless Agents
Discover agents and establish trust through reputation and validation
https://eips.ethereum.org/EIPS/eip-8004
This protocol proposes to use blockchains to discover, choose, and interact with agents across organizational boundaries without pre-existing trust, thus enabling open-ended agent economies.
Trust models are pluggable and tiered, with security proportional to value at risk, from low-stake tasks like ordering pizza to high-stake tasks like medical diagnosis. Developers can choose from different trust models: reputation systems using client feedback, validation via stake-secured re-execution, zero-knowledge machine learning (zkML) proofs, or trusted execution environment (TEE) oracles.
https://www.x402.org/
The x402 Payment Protocol (Tech/Web3)
Developed by Coinbase and supported by companies like Cloudflare, x402 is an open-standard payment protocol designed specifically for AI agents and machine-to-machine transactions.
How it works: It revives the "forgotten" HTTP 402 Payment Required status code. When an AI agent tries to access a paid API or service, the server sends back a 402 error with payment instructions. The agent can then pay instantly using stablecoins (like USDC) to get the data.
Why it matters: It eliminates the need for human-managed subscriptions, API keys, or credit card forms. It allows AI models to "buy" their own compute power or data in real-time.
Speed: Transactions can settle in milliseconds, making micropayments (e.g., $0.001 per request) economically viable.
x402 is an open, neutral standard for internet-native payments.
Know Your Agent (KYA) is a security framework that provides AI agents with a digital passport, verifying their identity, owner, and capabilities to ensure trust in autonomous, agent-to-agent transactions. It acts like "SSL for agent commerce," preventing malicious activity by linking automated actions to verified human/business entities.
It uses zero-knowledge proofs to verify identities without exposing sensitive personal data.
zkAgent: Verifiable Agent Execution via One-Shot Complete LLM Inference Proof
Source: https://eprint.iacr.org/2026/199.pdf
Recent advances in large language models (LLMs) have enabled LLM-based agents to move beyond simple text generation toward long-term execution that involves tool use, multi-step interactions, and autonomous decision-making. However, if an agent provider is compromised, it may produce malicious or incorrect outputs. As these agents increasingly handle sensitive data and financial assets, such misbehavior could lead to severe real-world harm.
To mitigate this risk, recent research has explored using zero-knowledge proofs (ZKPs) to verify the correctness of LLM inference. These methods ensure that a provider can only return outputs consistent with a claimed model. However, prior approaches are restricted to standalone transformer computations and do not support full agent executions.
We introduce zkAgent, an efficient SNARK system for verifiable agent execution. Unlike prior Transformer-only proof systems, zkAgent verifies the entire execution process, covering both end-to-end LLM inference and tool interactions. It achieves scalable proof generation by producing a single, one-shot inference proof across multiple interaction steps, removing the need to verify each intermediate token generation. To our knowledge, zkAgent is the first system to deliver practical, verifiable agent execution that attests simultaneously to complete LLM inference and external tool usage.
Evaluation results:
• For a 512-token GPT-2 agent inference, zkAgent achieves an amortized proving time of 1.05 s/token, representing a 294× speedup over the previous state of the art, zkGPT (USENIX Security ’25), which required 309 s/token using step-by-step generation.
• Verification time is reduced by 9,690× (0.45 s vs. 4,361.09 s).
• In end-to-end agent tasks—such as weather forecasting and code generation—zkAgent completes proving in 240 seconds and verification in 0.5 seconds, with a proof size of 42 MB.
These results demonstrate that zkAgent makes verifiable agent execution practical for real-world deployments.