The gap between "what software actually does" and "what users believe it does" — this paper tackles that root problem with one unifying measure: explicit meaning 📐 Title: Making Software Meaningful URL: 📐 Overview The paper advocates adopting "explicit meaning" as a unifying measure to improve software usability, modularity, and accountability. It describes software behavior as a shared representation grounded in domain-specific language, not technical jargon. ❓ Challenges Solved Software engineering has a fundamental gap. ・"What the software actually does" diverges from "what users believe it does" ・That divergence breeds poor usability, bugs, and distrust The thesis: quality improves when developers and users hold a single, explicit understanding of what the software does and why. 💡 Methodology & Proposed Approach It organizes domain-specific phenomena (individuals, actions, and resulting facts) into concepts as a shared vocabulary, shown through three applications. ・Usability design: align user and designer perspectives via shared meaning ・Modular code generation: use LLMs to map conceptual units of meaning to code units, boosting modularity and legibility ・Agent accountability: implement codes of conduct based on explicitly defined intended behaviors 🌍 Use Cases It applies to improving modularity and legibility in LLM-assisted development, and to AI governance by making an autonomous agent's intended behavior explicit and accountable. It argues for putting "meaning" at the center as humans and AI build software together. #SoftwareEngineering# #AIGovernance#

Logging alone won't prevent incidents. Meet runtime governance that blocks problematic requests before they reach the LLM 🛡️ Title: LangSmith LLM Gateway: runtime governance built into the agent lifecycle URL: 🛡️ Overview A runtime governance layer that sits between agents and their model providers. As an enforcement point inside the LangSmith platform, it aims to stop problems at the source rather than just logging them after the fact. ❓ Challenges Solved Observability (logging) alone can't prevent problems. Logging an incident after it happens is too late — problematic requests should be blocked before they reach external LLM providers. 💡 How It Works ・Spend controls: hard caps at org, workspace, user, or API-key level; returns 402 when exceeded ・Cost visibility: real-time spend tracking across org units ・Data protection: auto-redaction of PII and secrets before they reach the model ・Trace integration: gateway-proxied calls appear in the same workspace ・Audit logging and layered policies Setup is minimal: point base_url at the Gateway, store provider keys in workspace secrets, and define policies in the LangSmith UI. 🌍 Use Cases ・Preventing runaway agent spend from retry loops ・Stopping sensitive data (SSNs, PII) from leaking into provider logs ・Establishing org-wide cost governance and compliance auditing #LLMOps# #AIGovernance#

The EU AI Act deadline is August 2, 2026, with penalties up to €15M. Here's a practical guide mapping abstract articles to concrete features ⚖️ Title: How LangSmith and LangChain OSS Help You Meet EU AI Act Requirements URL: ⚖️ Overview This post explains how to meet the EU AI Act's requirements for high-risk systems using LangSmith and LangChain OSS features, mapping the articles to implementations like tracing, evaluation, and human oversight. ❓ Challenges Solved The EU AI Act imposes strict requirements on high-risk AI. The deadline is August 2, 2026, with penalties up to €15M or 3% of global revenue. The hard part is knowing which capabilities actually satisfy the abstract articles. 💡 How It Works (articles → features) ・Observability and tracing (Article 12): end-to-end traces of LLM calls, tools, and reasoning steps; retention of 14 days (base) / 400 days (extended); EU data residency options ・Quality and safety (Article 15): online evaluators continuously score production traffic, with prebuilt evaluators for toxicity, hallucination, PII leakage, prompt injection, and more ・Human oversight (Article 14): LangGraph's interrupt for human-in-the-loop, with resume-from-exact-point recovery ・Risk management (Article 9): custom dashboards track risk scores and trigger alerts 🌍 Practical Starting Point Build in this order — tracing → production evaluations → human-in-the-loop — and choose EU, self-hosted, or BYOC deployment based on data residency needs. #EUAIAct# #AIGovernance#

Battling spiraling infrastructure costs and Shadow AI risks? The problem isn't technology; it's governance. Join our webinar for expert AI governance insights.

Vietnam is taking a proactive approach to AI governance, becoming the first in Southeast Asia to implement a comprehensive law. Clear rules can guide innovation responsibly while building trust and a stable environment for AI.

"All countries should take a people-centered approach and develop AI for the positive and for good. We should ensure that AI is an important driver for shared prosperity and common security." — Keynote Address by President Xi Jinping at the Opening Ceremony of the 2026 World AI Conference and High-Level Meeting on Global AI Governance #2026WAIC#

President Xi Jinping attended the opening ceremony of the 2026 World AI Conference and High-Level Meeting on Global AI Governance in Shanghai and delivered the keynote address. President Xi noted that today, the world has entered an unprecedented period of active innovation on AI technologies, carrying within it great opportunities as well as challenges to governance. In China’s view, all countries should take a people-centered approach and develop AI for the positive and for good. We should ensure that AI is an important driver for shared prosperity and common security. We should join hands to build a just and equitable system for global AI governance.

A meaningful and impactful #GenevaDigitalWeek# — all about collaborating to unlock #AI#'s potential to serve humanity! ✨ We made a new #Partner2Connect# pledge, while #ZTE#'s experts shared insights on AI infrastructure, AI governance, women’s leadership, and more — strengthening our role as a global collaborator and responsible innovator. 🤝 Together, we're driving sustainable, inclusive, and trustworthy AI for all. #AIforGood# @UN | @ITU | @AIforGood