Agentic AI is redefining banking across transactions, risk control, and decision-making. The shift demands more agile, resilient, and secure infrastructure. Join the Huawei Intelligent Finance Summit 2026 in Shanghai, May 20–21 to examine the next phase of agentic banking.

Agentic coding is a huge boon for software developers that want to get far more done, great for IT people to build vastly more custom systems internally, great for domain experts that want to automate workflows or wire systems together, and absolutely fantastic for anyone curious to learn how to start coding. What it’s less great for is casually building complex software that you have to maintain on an ongoing basis and take on all the risk for. Upgrades, maintenance, keeping up to date with latest security issues, and so on, are taxes most knowledge workers aren’t familiar with or prepared for. Net net: we’re going to get 100X more software and vastly more software developers in the future. But that’s different from *everyone* rolling their own.

Agentic Payment的最真实的信息，信息降噪 StableHunterAI日报 ━━━━━━━━━━━━━━━━━━━━━━ 2026-04-10 🔥 #1｜How# agents, digital wallets, and trust are rewriting checkout The post analyzes global checkout trends with a focus on digital wallets and AI agents, which aligns with the AI×Web3×Payment intersection, but it lacks deep technical specifics or major protocol-level changes. → 查看原文 ( 🤖 #2｜AlphaTON# Capital looks to raise $43 million to bolster Telegram’s Cocoon AI infrastructure This item directly addresses a major funding initiative for AI infrastructure within a leading Web3 messaging platform, signaling strategic capital deployment at the intersection of AI, Web3, and Payments via Telegram's ecosystem. → 查看原文 ( 🤖 #3｜CoreWeave’s# $8.5B loan shows how AI is replacing crypto mining finance This item highlights a significant capital shift from crypto mining to AI infrastructure, directly impacting Web3 finance and AI hardware investment trends. → 查看原文 ( 🤖 #4｜Why# AI Agents Hit Snags Onchain It directly addresses AI agents encountering blockchain infrastructure friction, squarely at the intersection of AI and Web3, though it's more of an analysis than a major industry shift. → 查看原文 ( ⛓ #5｜Stablecoin# FX nears ‘institutional-grade’ parity with bank rails in LATAM and East Africa: report This report highlights a significant milestone where stablecoin FX is achieving institutional-grade parity with traditional banking in key emerging markets, directly impacting the Web3 and Payments intersection by demonstrating real-world adoption and competitive infrastructure. → 查看原文 ( ⛓ #6｜Stablecoin# volumes to reach $719T by 2035 as generational wealth shift speeds up crypto adoption It directly addresses the intersection of Web3 (stablecoins, crypto adoption) and Payments (challenging Visa/Mastercard, payment volumes) with a forward-looking industry shift driven by generational wealth transfer. → 查看原文 ( ⛓ #7｜Webinar# Recap: Corporate Treasury Onchain — 24/7 Global Liquidity This content directly addresses the intersection of Web3 and Payments by showcasing how enterprises use Solana and Fireblocks for on-chain treasury management, offering 24/7 liquidity and instant payouts, which signals a tangible shift in corporate financial operations. → 查看原文 ( ⛓ #8｜Webinar# Recap: Payments on Solana - A Production-Ready Ecosystem This webinar recap directly addresses the intersection of Web3 and payments by highlighting major payment players building on Solana for real-world use cases, though it lacks detailed technical or product maturity specifics. → 查看原文 ( ⛓ #9｜From# Zero to a Global Pricing Hub: Binance TradFi’s First 90 Days This item highlights a significant Web3-Payment infrastructure milestone with multi-billion-dollar scaling in TradFi derivatives, but lacks direct AI relevance and deep technical specifics. → 查看原文 (

Agentic blockchain infra is still a WIP. Any progression in the following directions was and will be significant to evolution of agents. agent to agent - eg. Moltbook agent to crypto - eg. x402 agent to recources - eg. web 4.0 agent to human - eg. openclaw agent to tasks - eg. yet to take off but would like to see agentic bounty hunters Still so much to be done.

Announcing agentic performance benchmarking for Speech to Speech models on Artificial Analysis. We use 𝜏-Voice to measure tool calling and customer interaction voice agent capabilities in realistic customer service scenarios Even the strongest Speech to Speech (S2S) models today resolve only about half of realistic customer service scenarios end-to-end - a meaningful gap relative to frontier text-based agents on the same tasks. Voice channels introduce significant complexity: challenging accents, background noise, and packet loss, all while requiring fast responses, consistency across long multi-turn conversations, and reliable tool use. Performance also varies considerably by audio condition: in clean audio some models perform notably better, but realistic conditions continue to pose a challenge. Conversation duration also varies meaningfully across models, with implications for both customer experience and operational cost. About 𝜏-Voice: Our Agentic Performance benchmark is based on 𝜏-Voice (Ray, Dhandhania, Barres & Narasimhan, 2026), which extends 𝜏²-bench into the voice modality to evaluate S2S models on realistic customer service tasks. It measures multi-turn instruction following, support of a simulated customer through a complete interaction, and tool use against simulated customer service systems. The simulated user combines an LLM-driven decision model with realistic audio synthesis: diverse accents, background noise, and packet loss modelled on real network conditions. This complements our Big Bench Audio benchmark measuring intelligence and Conversational Dynamics (Full Duplex Bench subset) benchmark measuring conversational naturalness. Scores are the average of three independent pass@1 trials. We evaluate under realistic audio conditions using the 𝜏²-bench base task split across three domains: ➤ Airline (50 scenarios): e.g., changing a flight, rebooking under policy constraints ➤ Retail (114 scenarios): e.g., disputing a charge, processing a return ➤ Telecom (114 scenarios): e.g., resolving a billing issue, troubleshooting a service problem Task success is determined by deterministic checks against expected actions and final database state, consistent with the 𝜏²-bench evaluator. Key results: xAI's Grok Voice Think Fast 1.0 is the clear leader at 52.1%, averaging 5.6 minutes per conversation, the second-longest overall. OpenAI's GPT-Realtime-2 (High) (39.8%, 3.0 min) and GPT-Realtime-1.5 (38.8%, 4.8 min) follow, with Gemini 3.1 Flash Live Preview - High close behind at 37.7% (3.8 min). Speech to Speech is a fast evolving modality and we expect movement in rankings as we continue to add new models with these capabilities, and model robustness improves. Congratulations @xAI @elonmusk! See below for further detail ⬇️

3/ Agentic Commerce 🤖 The most exciting takeaway? AI Agents need native currency. $U is being architected as the programmable blood for the $3T-$5T Agentic Economy while EIP3009 enabled. M2M (Machine-to-Machine) payments are the next frontier.

Most agentic stacks run into the same problems pretty quickly: reasoning and tool parsing drift across turns, KV cache reuse falls apart, or tools fire too late. We’ve been hardening Dynamo’s harness-facing path so @Claudeai Code, @OpenClaw, and @openai Codex-style agent patterns behave reliably on custom stacks and inference endpoints: • Stable prompts for KV reuse and lower TTFT • Interleaved reasoning + tool calls preserved across turns • Streaming tool dispatch instead of end-of-turn buffering • Harness behavior aligned with real multi-turn agent runtimes If you’re building your own agent stack or serving endpoint, this blog goes through the infrastructure issues that tend to show up in practice and the patterns we’ve been using to fix them. Tech blog ➡️

The agentic CPU, as a specific product, will evolve over this year. It's possible much of what we know now will be out of date in six months. That said... This was a stab at detailing the market and TAM size from mid-March.

The biggest barrier to agentic DeFi isn't liquidity, fees, or speed. It's the onboarding flow. CAPTCHAs. Wallet popups. Email verification. Every step assumes a human is reading the screen. Platforms that treat human verification as immovable won't be attacked. They just won't be chosen.

Our new multi-model agentic security system brings together more than 100 specialized agents across frontier and custom models to find exploitable bugs, delivering top performance on the CyberGym benchmark. We used it ahead of Patch Tuesday to help find and fix 16 vulnerabilities. Today we’re announcing that customers can sign up to test it in private preview.