Fetch.ai said it has added two new pieces to its ASI:One ecosystem: an official Skills Library announcement on ASI:One and a Card Playground for building interactive agent cards. The company described the updates as tools for creating, previewing and activating agent-facing experiences more quickly.

In posts on X, Fetch.ai said the ASI:One Card Playground gives users three connected panels for editing card metadata and payloads. The company said the playground supports carousel, detail, form, review and custom card formats, and that users can also describe an interface in plain language or upload a reference image to generate a starting payload with AI.

Many people chat with ASI:One. But they’re only scratching the surface.

(1) Use ‘Projects’ on https://t.co/zIYAcepyTr to folder your activity
(2) Set ‘Recurring Tasks’ so your AI completes tasks while you sleep.
(3) Try ‘Multi-User Chat’ to have you, your friends and all your…

— ASI:One by Fetch.ai (@asi1_official) July 26, 2026

Fetch.ai also said the playground uses the same validation as ASI:One in production, suggesting that cards rendered in the tool should behave the same way for end users. The company framed the feature as a way to “design, test, inspect and connect” interactive cards before writing agent code.

Separately, Fetch.ai announced community-created Skills inside the ASI:One skills library on asi1.ai. According to the post, users can activate abilities with one tap from a right-side panel. The official blog link confirms the Skills Library launch.

The update appears focused on developer and user-interface tooling rather than a broader protocol or token change.

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NEAR Protocol has issued a terminology clarification to better define the scope of its AI infrastructure, distinguishing between the broad concept of an “agent economy” and the narrower function of “agentic payments.”https://twitter.com/NEARProtocol/status/2079235069818945745

The protocol’s core message, shared via official social channels, suggests that the “agent economy” represents a shift in market structure that is significantly larger in scale than the mere transactional layer of automated payments.

According to NEAR, while agentic payments focus on the ability of AI to send and receive value, the agent economy refers to the total orchestration of commercial relationships by autonomous software. The protocol argues that most modern commercial interactions are essentially “transactions waiting for an agent” to manage them.

The clarification identifies three primary pillars that define this emerging category:

• Machine-negotiated services: Autonomous software units determining the terms of service delivery without direct human intervention.
• Agent-to-agent contracting: The creation of digital agreements where AI agents hire and pay other agents to fulfill specific tasks within a value chain.
• Continuous micro-settlement: The high-frequency exchange of value in increments too small or frequent for traditional financial systems to process efficiently.

Infrastructure for AI Agents

The distinction comes as NEAR positions its sharded blockchain architecture, known as Nightshade, as a foundation for “user-owned AI.” By integrating features like the NEAR Agent Market, the protocol aims to provide an environment where AI agents can operate with cryptographic identity and verifiable authority.

Under this framework, blockchain provides the “financial guardrails” necessary for agents to transact safely. This includes establishing reputation systems where agents can build portable transaction histories, which other software counterparts use to evaluate risk or refuse interactions.

NEAR suggests that this model will compress existing value chains by removing human-centric delays. Rather than merely making current payment volumes cheaper, the protocol anticipates that the shift toward machine commerce will lead to a transaction volume that dwarfs existing consumer systems, driven by agents consuming both compute and settlement resources simultaneously.

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Ritual has introduced a framework aimed at running verifiable AI inference and autonomous agents directly on blockchain networks, according to an official update from the project. The announcement outlines a development stack designed to allow developers to deploy machine-learning models for tasks such as protocol risk monitoring, portfolio management, and decentralized autonomous organization (DAO) governance, while maintaining transparent on-chain records for model outputs.

According to the official introduction from Ritual, the architecture centers on a system the project calls Infernet. Rather than relying on opaque, off-chain model calls, the infrastructure is built to generate on-chain verification records for each inference. This approach is intended to address a recurring constraint in Web3 AI workflows: the difficulty of auditing why an autonomous agent executed a specific transaction or governance action. By anchoring inference results to the chain, the project states that decisions made by these agents can be traced back to specific data inputs and model configurations.

The announced use cases focus on areas where automated decision-making intersects with capital allocation or protocol authority. For portfolio management, the system could support agents that monitor market conditions and execute adjustments based on predefined rules, with each action carrying a verifiable execution record. In DAO environments, the framework could enable agents that assist with proposal analysis or automated voting, where the underlying reasoning remains publicly auditable. The project also highlighted protocol risk monitoring as a primary deployment target.

Ritual positions the framework as an infrastructure layer rather than a standalone consumer application. The system is intended to provide developers with the tools required to integrate verifiable inference into existing smart contracts or custom agent workflows. The available source material does not specify network coverage, deployment fees, or the current scope of live integrations, and broader ecosystem adoption remains unverified. The update establishes a technical pathway for verifiable AI agent activity, but exact deployment timelines and developer accessibility parameters require additional confirmation.

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