Artículos Relacionados con Verification

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AI Agents Can Be Verified, But Who Protects Their Privacy?

As AI Agents evolve from automated tools into active participants in on-chain economies, a critical challenge emerges: establishing trust while preserving privacy. While standards like ERC-8004 aim to provide verifiable identity and reputation for agents, their public nature could expose sensitive operational strategies, user preferences, and business relationships in fields like DeFi, governance, and prediction markets. The proposed ACTA (Anonymous Credentials for Trustless Agents) framework addresses this by adding a privacy layer. It allows agents to cryptographically prove they meet certain criteria (e.g., having passed an audit or possessing sufficient reputation) without revealing the underlying sensitive data, using zero-knowledge proofs. This shifts trust from "public identity" to "policy-based proof." This shift is crucial because agents act dynamically on behalf of users, making their behavior a potential proxy for user intent. ACTA would enable verification of an agent's legitimacy or authorization without creating a permanent, public map of all its activities and relationships. ACTA remains a research direction with open challenges, including scalability, decentralization of credential issuers, and implementation costs. However, it highlights a fundamental need: a robust Agent economy requires not just mechanisms for verification, but also for protecting the privacy of agents, their users, and the protocols they interact with.

marsbitHace 2 días 01:27

AI Agents Can Be Verified, But Who Protects Their Privacy?

marsbitHace 2 días 01:27

From KYC to KYA, Is It Time to Give AI Agents Their Own 'ID Cards'?

Titled "From KYC to KYA: Is It Time to Issue 'Identity Cards' for AI Agents?", this article discusses the emerging concept of Know Your Agent (KYA) as AI agents become increasingly autonomous. In Agent-to-Agent (A2A) scenarios, where agents execute contracts, payments, and trades without human intervention, the lack of a shared identity standard creates risks like unauthorized transactions, fraud, and accountability gaps. KYA acts as a trust layer to verify an agent's origin, authority, and accountability. The need for KYA is most critical outside centralized platforms (like Google or Coinbase), such as in decentralized exchanges (DEX), A2A payments, and merchant payments. Several key players are building KYA infrastructure: - **ERC-8004**: A proposed Ethereum standard that issues a unique AgentID as an NFT, building on-chain identity, reputation, and validation systems. - **Visa TAP**: Visa's solution issues agent identity credentials, with transactions verified via triple signatures (legitimacy, delegator, payment method). - **Trulioo**: Extends its KYC/KYB compliance infrastructure using a Digital Passport for Agents (DAP), issued after verifying both the developer and user, and refreshed per transaction. - **Sumsub**: Focuses on post-issuance real-time verification, detecting agent anomalies during transactions using its existing compliance systems. Regulatory bodies are also acting. The EU AI Act mandates operator identification in logs for high-risk AI systems, the US NIST prioritizes agent identity management standards, and Singapore has released a national AI governance framework. Similar to how the 2019 FATF Travel Rule impacted crypto exchanges, possessing KYA infrastructure may determine market entry in the AI agent era. The market is expected to segment rather than produce a single winner, with success depending on integrations with merchants, payment networks, and KYC client bases.

marsbit05/10 05:45

From KYC to KYA, Is It Time to Give AI Agents Their Own 'ID Cards'?

marsbit05/10 05:45

How Blockchain Fills the Identity, Payment, and Trust Gaps for AI Agents?

AI Agents are rapidly evolving into autonomous economic participants, but they face critical gaps in identity, payment, and trust infrastructure. They currently lack standardized ways to prove who they are, what they are authorized to do, and how they should be compensated across different environments. Blockchain technology is emerging as a solution to these challenges by providing a neutral coordination layer. Public ledgers offer auditable credentials, wallets enable portable identities, and stablecoins serve as a programmable settlement layer. A key bottleneck is the absence of a universal identity standard for non-human entities—akin to "Know Your Agent" (KYA)—which would allow Agents to operate with verifiable, cryptographically signed credentials. Without this, Agents remain fragmented and face barriers to interoperability. Additionally, as AI systems take on governance roles, there is a risk that centralized control over models could undermine decentralized governance in practice. Cryptographic guarantees on training data, prompts, and behavior logs are essential to ensure Agents act in users' interests. Stablecoins and crypto-native payment rails are becoming the default for Agent-to-Agent commerce, enabling seamless, low-cost transactions for AI-native services. These systems support permissionless, programmable payments without traditional merchant onboarding. Finally, as AI scales, human oversight becomes impractical. Trust must be built into system architecture through verifiable provenance, on-chain attestations, and decentralized identity systems. The future of Agent economies depends on cryptographically enforced accountability, allowing users to delegate tasks with clearly defined constraints and transparent operation logs.

marsbit04/21 09:19

How Blockchain Fills the Identity, Payment, and Trust Gaps for AI Agents?

marsbit04/21 09:19

Agents Have Entered the Harness-Driven Era

The article discusses the significance of the leaked Claude Code from Anthropic, highlighting its revelation of advanced Agent engineering practices centered on "Harness" design. Rather than relying solely on model capabilities, modern AI systems now depend on a structured engineering framework—the Harness—to maximize performance. This framework includes six core components: multi-layered System Prompts, Tool Schema, Tool Call Loop (with Plan and Execute modes), Context Manager, Sub-Agent coordination, and Verification Hooks. The Harness enables tighter integration between training and inference, supports long-chain tool execution, and improves reliability through objective verification. It also drives six key training directions: behavior alignment via System Prompt, end-to-end tool-use training, integrated plan-execute training, memory compression, sub-agent orchestration, and multi-objective reinforcement learning. The shift to Harness-driven development reduces the emphasis on pure prompt engineering, favoring instead multidisciplinary talent with skills in AI, backend engineering, and infrastructure. The market is evolving toward more secure, private, and vertically integrated Agent deployments, with "model shell" companies needing either strong infrastructure or deep domain expertise to compete. Claude Code’s leak underscores that future AI advancements will be shaped by engineering architecture as much as by algorithmic innovation.

marsbit04/15 10:11

Agents Have Entered the Harness-Driven Era

marsbit04/15 10:11

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