In 2027, will most internet traffic be agents talking to agents? If yes — who wins: 1⃣the best models 2⃣the best infrastructure 3⃣the best governance? SVP Chain purpose-built for the agentic economy are positioning to capture all three. #AIAgents# #AgenticAI# #AgenticEconomy# #SVPChain# #MultiAgent# #FutureOfInternet#

You can build any AI agent with any LLM. But when multiple agents start talking to each other, collisions are inevitable — because nothing is watching them. OpenAI agents may built them in a secret message board but it is still unmonitored. Agents only going to get wilder. That’s why putting agents on-chain with decentralized permission controls like SVPchain is what actually brings governance. #AIAgents# #MultiAgent# #AgenticAI# #OnChainAgents# #SVPchain# #AgentCollusion#

Multi-agent collaboration that exchanges text every round is expensive 🔄 The fresh idea: scale the collaboration itself through latent-space recursion. Title: Recursive Multi-Agent Systems URL: 🔄 Overview RecursiveMAS reframes multi-agent collaboration as recursive computation in a unified latent space, rather than sequential text exchange. It connects heterogeneous agents through a RecursiveLink module, enabling latent thought generation and cross-agent state transfer. ❓ Challenges Solved Multi-agent systems (MAS) typically rely on text-based communication. ・Agents conversing in natural language consume huge token counts and cost a lot of compute ・The driving question: can agent collaboration itself be scaled through recursion? 💡 Methodology & Proposed Approach ・It frames the whole system as recursive latent-space computation ・The RecursiveLink module lightly connects heterogeneous agents with gradient-based credit assignment across recursion rounds ・Optimization uses inner-outer loop learning while maintaining theoretical stability 📊 Experimental Results Across 9 benchmarks spanning math, science, medicine, search, and code generation, with 4 collaboration patterns tested. ・Average accuracy improved by 8.3% ・Inference sped up 1.2x-2.4x ・Token usage reduced by 34.6%-75.6% It raises accuracy while substantially improving speed and cost at the same time. #MultiAgent# #LLM#

Building multi-agent systems is powerful, but governing them at scale? That's the real challenge. With Agent Registry in the Gemini Enterprise Agent Platform, IT admins now have a unified catalog for managing AI agents. Learn more →

From a multi-agent system surpassing human grandmasters to terrorist groups weaponizing AI chatbots, the last two weeks highlight agentic AI is reshaping both competition and security landscapes. Here's your biweekly recap of everything that happened in the space, in the latest edition of AI Agents Pulse: 1️⃣ SpaceXAI releases Grok 4.5, an 'Opus-class model' the company says has 'twice greater token efficiency' than other leading models, positioned as a workhorse for coding, research, and knowledge work. 2️⃣ AI chip startup SambaNova raises $1 billion in a round led by General Atlantic, reaching an $11 billion valuation as investors continue to back Nvidia challengers. 3️⃣ S&P Global downgrades Oracle's credit rating to BBB-, citing OpenAI as a key credit risk due to Oracle's massive data center contractual obligations. 4️⃣ Databricks adopts Chinese open-source model GLM 5.2 as its default coding engine after it matched Anthropic's Opus 4.8 performance at a lower cost. 5️⃣ Microsoft's carbon emissions rose 25 percent in 2025 to 34 million metric tons, driven primarily by the expansion of its datacenter infrastructure, per the company's 2026 sustainability report. 6️⃣ Over 200 economists and AI researchers, including Nobel laureates, warn that the window to prepare for AI's economic impact is closing fast. 7️⃣ General Intuition, a Bezos-backed startup, raises $320 million at a $2.3 billion valuation to develop physical AI world models trained on gaming data. 8️⃣ GrandCode, a multi-agent reinforcement learning system, becomes the first AI to consistently beat all human participants in live Codeforces contests. 9️⃣ A study finds terrorist groups like Boko Haram are using major AI chatbots for attack planning and weapons development, with safety filters failing to prevent misuse.

Grok build for multi-agent research workflows. What I did: Asked Grok to deeply research industrial projects across the state of Florida. I asked Grok to deploy sub-agents to run deep dive research in each region of Florida with strong industrial activity. I pointed Grok to a folder on my PC that houses hundreds of PDFs connected to the products I sell, and asked it to focus on companies that utilize these products in their manufacturing process. I asked for a research report deliverable, with separate call sheet document. The output: Grok supervisor agent deployed 5 sub-agents in different parts of FL: Space coast, Central FL, Tampa area, and South FL. Each sub-agent delivered a report for its region, going over market signals, investment $, and individual facilities that are expanding/receiving new investment. Each agent delivered 10-15 companies to dive into, with complimentary call sheet for next steps. I’ve been running these reports using Claude dynamic workflows and not even gonna lie, Grok is delivering equally useful bangers. This would’ve been far from true not long ago. Grok Build is about to come off the bench and enter the token rotation 🥹

Build a multi-agent fleet that remembers — free & open source Your AI agents forget everything between sessions. Learn to build a fleet that doesn't. Six free, hands-on tutorials on one shared, governed memory