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  • Sakana AI and MUFG Deploy AI Agent for Banking Lending
    Sakana AI and MUFG Deploy AI Agent for Banking Lending

    銀行業務にAIエージェントを実装する https://
    sakana.ai/mufg-ai-lendin
    g-interview/
    … 先日、Sakana AIと三菱UFJ銀行の「AI融資エキスパート」が、実案件での検証フェーズへと舵を切りました。プロジェクトの中心メンバー2名が、インタビュー形式でその技術的背景や取り組みの概要を語りました。

    → View original post on X — @sakanaailabs

  • AI Agents in Practice: Design, Implement, and Scale Autonomous Systems
    AI Agents in Practice: Design, Implement, and Scale Autonomous Systems

    Another great book by @AltoValentina
    , from @PacktDataML at http://
    amzn.to/4p98LYl "AI Agents in Practice — Design, Implement, and Scale Autonomous #AI Systems for Production" Book Description (from Amazon): As AI agents evolve to take on complex tasks and operate

    → View original post on X — @kirkdborne

  • DeepSeek LLM Guide: Fine-tuning, Distillation, Agents, Prompting
    DeepSeek LLM Guide: Fine-tuning, Distillation, Agents, Prompting

    "DeepSeek in Practice: From basics to fine-tuning, distillation, agent design, and prompt engineering of open source LLM" via @PacktDataML at http://
    amzn.to/4imbryH Discover DeepSeek's unique traits in the LLM landscape
    Compare DeepSeek's multimodal features with leading

    → View original post on X — @kirkdborne

  • New Book: Agentic Patterns for Multi-Agent AI Systems
    New Book: Agentic Patterns for Multi-Agent AI Systems

    New release from @PacktDataML at http://
    amzn.to/3MaHy8T "Agentic Architectural Patterns for Building Multi-Agent Systems: Proven design patterns and practices for GenAI, agents, RAG, LLMOps, and enterprise-scale AI systems" Contents:
    GenAI in the Enterprise: Landscape,

    → View original post on X — @kirkdborne

  • New Multi-Agent AI Systems Book Released by Packt Data ML
    New Multi-Agent AI Systems Book Released by Packt Data ML

    New release from @PacktDataML available at: http://
    amzn.to/40Sp4O9 "Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Python-based Agentic AI Framework with tool use, memory, and multi-agent workflows" Table of Contents:
    Introduction to Generative AI and AI

    → View original post on X — @kirkdborne

  • MintMCP Workshops: Secure Data Access, Agent Identity, Interoperability

    Secured data access, agent identity, and interoperability are three key areas we solve for Enterprise customers @MintMCP_AI ; we're running workshop sessions now for people interested to learn more about this topic. Email founders@mintmcp.com if you're interested to get on one.

    → View original post on X — @jiquanngiam, 2026-03-19 04:41 UTC

  • Enterprise AI Adoption: Agents, Governance, and Multi-Platform Strategy

    Had meetings and a dinner with 20+ enterprise AI and IT leaders today. Lots of interesting conversations around the state of AI in large enterprises, especially regulated businesses. Here are some of general trends: * Agents are clearly the big thing. Enterprises moving from talking about chatbots to agents, though we’re still very early. Coding is still the dominant agentic use-case being adopted thus far, with other categories of across knowledge work starting to emerge. Lots of agentic work moving from pilots and PoCs into production, and some enterprises had lots of active live use-cases. * Agentic use-cases span every part of a business, from back office operations to client facing experiences from sales to customer onboarding workflows. General feeling is that agentic workflows will hit every part of an organization, often with biggest focus on delivering better for customers, getting better insights and intelligence from data and documents, speeding up high ROI workflows with agents, and so on. Very limited discussion on pure cost cutting. * Data and AI governance still remain core challenges. Getting data and content into a spot that agents can securely and easily operate on remains a huge task for more organizations. Years of data management fragmentation that wasn’t a problem now is an issue for enterprises looking to adopt agents. And governing what agents can do with data in a workflow still a major topic. * Identity emerging as a big topic. Can the agent have access to everything you have? In a world of dozens of agents working on behalf, potentially too much data exposure and scope for the agents. How do we manage agents with partitioned level of access to your information? * Lots of emerging questions on how we will budget for tokens across use-cases and teams. Companies don’t want to constrain use-cases, but equally need to be mindful of ultimate token budgets. This is going to become a bigger part of OpEx over time, and probably won’t make sense to be considered an IT budget anymore. Likely needs to be factored into the rest of operating expenses. * Interoperability is key. Every enterprise is deploying multiple AI systems right now, and it’s unlikely that there’s going to be a single platform to rule them all. Customers are getting savvier on how to handle agent interoperability, and this will be one of the biggest drivers of an AI stack going forward. Lots more takeaways than just this, but needless to say the momentum is building but equally enterprises are acutely aware of the change management and work ahead. Lots of opportunity right now.

    → View original post on X — @jiquanngiam, 2026-03-19 04:16 UTC

  • AI connects to company knowledge base, learns tone and methodology
    AI connects to company knowledge base, learns tone and methodology

    it also connects to your company's knowledge base and learns your tone and methodology over time. built by ex-McKinsey, Big Four, and ex-Apple AI.

    → View original post on X — @aibreakfast

  • H2O.ai Showcases Flood Intelligence Blueprint at NVIDIA GTC
    H2O.ai Showcases Flood Intelligence Blueprint at NVIDIA GTC

    Today at #NVIDIAGTC, http://
    H2O.ai’s Flood Intelligence Blueprint was showcased at the @nvidia booth — a real example of how #AIforGood is being applied to support mission-critical infrastructure and public safety. Built with NVIDIA NeMo Agent Toolkit, Nemotron

    → View original post on X — @h2oai

  • Why Macs Fall Short for Agentic LLM Workloads
    Why Macs Fall Short for Agentic LLM Workloads

    this is exactly why I tell people to NOT get a Mac anything for LLMs useless for concurrency and agents, basically a single chat interface that doesn’t scale up neither with sessions nor with kvcache you want to be ready for the agentic world you Buy a GPU

    → View original post on X — @theahmadosman