Agent Infrastructure

TL;DR for operators When hundreds of agents span internal teams, vendors, and partner domains, discovery has to answer more than which agent can do the task. Operators also need to know who registered it and what evidence justifies trusting its identity. Agent Trust Infrastructure (ATI) addresses those questions by separating trusted-registry governance, per-agent publication, and resolver-side discovery rather than collapsing them into one directory.1 ...

TL;DR for operators A better plan can become a worse agent when the environment keeps moving while the system thinks. STAR makes that reversal unusually clear: Kimi-K2-Thinking leads the unlimited-deliberation evaluation with a rating of 1206.1 and a 1.00 win rate, then falls to 842.6 and a 0.210 win rate in real-time play, where GLM-4.6 leads at 1180.8.1 ...

Memory is cheap until it has to remember correctly. A chatbot can remember a paragraph for a few minutes. An enterprise agent is asked to remember a customer’s old address, current address, account owner, exception approval, product issue, refund promise, and the reason the promise changed last month. Then it must answer without mixing the past with the present. This is where “just add more context” begins to look less like strategy and more like buying a bigger drawer for unsorted receipts. ...

Memory fails in a very ordinary way. A customer asks, “Can we use the same approval condition as before?” A research agent says, “Yes.” A procurement assistant retrieves the old vendor quote. A planning copilot remembers a hotel price from yesterday’s itinerary. Everything looks semantically relevant. The words match. The entities match. The embedding score smiles politely. ...

TL;DR for operators The useful reading of this paper is not “AI agents are coming to crypto.” That is already obvious, and in some corners of the market, painfully over-branded. The sharper point is that Web3-AI agents are forming a stack. At the bottom are infrastructure and trust layers: protocols, DePIN systems, verification mechanisms, execution environments, and agent-development platforms. On top sit the applications: DeFi agents, portfolio tools, market-intelligence systems, governance assistants, security auditors, creative agents, and RWA managers. The paper’s dataset of 133 projects shows this stack is not evenly valued. Infrastructure accounts for 67.8% of the analysed $6.92 billion market capitalisation, even though incubation platforms show the most project activity.1 ...

TL;DR for operators If your organisation is building one chatbot, DNS is not your problem. If your organisation expects thousands of autonomous agents to discover one another, verify capabilities, rotate endpoints, respect privacy boundaries, and revoke trust quickly, then DNS starts looking like a filing cabinet in a drone factory. The paper behind NANDA proposes a layered discovery architecture for the “Internet of AI agents”: a lean signed index record called AgentAddr, richer verified metadata called AgentFacts, and optional adaptive resolvers for live endpoint selection.1 The important idea is not that NANDA is “DNS for agents”. That is the tempting headline and, naturally, the least useful one. The paper is really about separating stable identity from dynamic operational metadata and from runtime routing. ...