How KRAFTON Built PUBG Ally, a Co-Playable Character Powered by NVIDIA ACE

What Is a Multi-Agent System?

Hệ thống đa tác nhân (MAS) gồm nhiều AI agent tự chủ phối hợp giải quyết nhiệm vụ phức tạp, với các thành phần chính như worker agents, orchestrator agents, môi trường thực thi, bộ nhớ chia sẻ, giao thức (MCP, A2A) và chính sách quản trị. Các ứng dụng thực tế bao gồm quản lý hạ tầng của NTT Data, nền tảng nghiên cứu dược phẩm của Madrigal Pharmaceuticals (dựa trên LangChain/LangSmith) và hệ thống chăm sóc sức khỏe của Fujitsu. Gartner ghi nhận sự quan tâm từ doanh nghiệp tăng 1.445% nhờ khả năng tự động hóa quy trình phức tạp ở quy mô lớn.

Lập trình viên nên đọc bài này để hiểu cách xây dựng hệ thống tự động hóa công việc phức tạp bằng cách kết hợp nhiều agent độc lập, từ đó tối ưu hóa hiệu suất và linh hoạt cho các ứng dụng doanh nghiệp tương lai.

How to integrate OpenShift AI and PG Airman MCP Server

A deep-dive into the Data Governance Copilot architecture (Part 3), covering container-level design with SvelteKit frontend, FastAPI backend, and an agentic loop integrating OpenShift AI with the PG Airman MCP server. Explains two deployment modes: Red Hat Integrated Llama Stack versus lower-level MCP-direct using the OpenAI SDK and MCP Python library. Details how inbound LLM messages use OpenAI API format converted via Jinja tokenizer templates in vLLM, and how outbound tool calls are parsed — including a custom client-side parser needed for NVIDIA Nemotron Nano 9B due to its lack of streaming support in the native vLLM plugin. Compares Nemotron's XML-tagged TOOLCALL format against Qwen3's standard hermes parser output.

The CPU's growing role in agentic AI infrastructure

Arm-sponsored content arguing that CPUs play a critical but underappreciated role in agentic AI infrastructure. While accelerators handle model performance, CPUs act as the control plane — managing data movement, workload scheduling, and secure isolation. Arm's Neoverse platform underpins custom silicon from AWS (Graviton), Google (Axion), Microsoft (Azure Cobalt), and NVIDIA (Grace Hopper/Blackwell), all reflecting a shift toward purpose-built Arm-based processors in cloud and AI datacenters. The piece introduces the Arm AGI CPU, built with Meta, targeting rack-level density for agentic AI deployments.

Two Six Technologies Unveils Sentr, the Command Center for the Information Environment

Two Six Technologies has launched Sentr, an AI-powered command center platform for information operations targeting U.S. government and allied partners. Built on Two Six's Helix agentic orchestration layer, Sentr unifies collection, research, planning, and messaging into a single chat interface. It uses AI agents to process large datasets, identify narrative opportunities, generate action plans, and deploy targeted content globally. Early users report 85% time savings. The platform is already deployed with Department of Defense, Department of State, and Intelligence Community customers, and includes audit trails, doctrine-based AI output scoring, and secure LLM interactions.

Databricks positioned highest in execution and furthest in vision for the second consecutive year in Gartner Magic Quadrant

Databricks announces it has been named a Leader in the 2026 Gartner Magic Quadrant for AI Platforms for Data Science and Machine Learning, holding the highest position in Ability to Execute and furthest in Completeness of Vision for the second consecutive year. The post highlights Databricks' unified platform philosophy combining lakehouse, Lakebase, Agent Bricks, and Unity Catalog to deliver governed, production-grade agentic applications. Key capabilities include centralized governance via Unity AI Gateway, support for frontier and open-source models, and tools for both developers and business users to build AI agents grounded in enterprise data.

Log management for AI workloads: How to bring your logs and telemetry plan into the AI-first century

AI workloads are overwhelming traditional log management systems, exposing limits in how teams capture, store, and correlate telemetry. A five-point action plan covers: unifying all telemetry signals on a single platform, correlating logs with traces for causation, controlling costs by eliminating rigid schemas and rehydration overhead, standardizing instrumentation at ingest, and enabling preventive operations through real-time contextual analytics. Key stats from a 2026 survey of 450 IT leaders highlight that 85% of organizations struggle to ingest logs at AI scale, 74% cite indexing and rehydration costs as barriers, and 84% say customer trust in AI depends on predictive log analytics.

Build Trust in Agentic AI: From POC to Production

A four-step framework for moving agentic AI systems from proof-of-concept to production-grade enterprise deployments. The framework covers: (1) a foundation layer using RAG, operational data, and agent memory to reduce hallucinations; (2) a verification layer with Agent Confidence Score (ACS) and Business Risk Score (BRS); (3) a governance layer using an Agent Decision Score formula (ADS = ACS × (1 - BRS)) with a traffic-light routing system for human-in-the-loop escalation; and (4) an outcomes layer with executive dashboards tracking ROI, cost-per-task, and systemic risk. A customer refund workflow is used throughout as a concrete example. MongoDB is positioned as the unified data platform for agent memory, vector search, and execution traces.