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에이전틱 AI는 자동화와 의사결정을 위한 차세대 키워드로 주목받으며 큰 기대를 모으고 있습니다. 하지만 이러한 열기 이면에는 다소 조심스러운 전망이 자리 잡고 있습니다. 가트너(Gartner)는 비용 급증, 불분명한 비즈니스 가치, 부적절한 리스크 관리 등의 이유로 에이전틱 AI 프로젝트의 40% 이상이 2027년 말까지 중단될 것이라고 경고합니다. *이 글은 SAS 글로벌 공공 부문 전략 […] The post AI 에이전트와 […]
AI 에이전트와 에이전틱 AI 활용 가이드 Read More »
Meta has acquired Moltbook, the Reddit-esque simulated social network made up of AI agents that went viral a few weeks ago. The company will hire Moltbook creator Matt Schlicht and his business partner, Ben Parr, to work within Meta Superintelligence Labs. The terms of the deal have not been disclosed. As for what interested Meta
Meta acquires Moltbook, the AI agent social network Read More »
In keeping with its recently accelerated release cadence, OpenAI has shipped GPT-5.4 (including GPT-5.4 Thinking and GPT-5.4 Pro). This update comes at a critical time, as recent events have led some vocal users to abandon ship for competing products and models from Anthropic and Google. GPT-5.4 is another model update focused on usefulness for agentic
OpenAI introduces GPT-5.4 with more knowledge-work capability Read More »
Perplexity has introduced “Computer,” a new tool that allows users to assign tasks and see them carried out by a system that coordinates multiple agents running various models. The company claims that Computer, currently available to Perplexity Max subscribers, is “a system that creates and executes entire workflows” and “capable of running for hours or
Perplexity announces “Computer,” an AI agent that assigns work to other AI agents Read More »
US Defense Secretary Pete Hegseth has threatened to cut Anthropic from his department’s supply chain unless it agrees to sign off on its technology being used in all lawful military applications by Friday. The threat is the latest escalation in a feud between Anthropic and the department, triggered by the AI group’s refusal to give
Pete Hegseth tells Anthropic to fall in line with DoD desires, or else Read More »
What adversarial prompt generation means Adversarial prompt generation is the practice of designing inputs that intentionally try to make an AI system misbehave—for example, bypass a policy, leak data, or produce unsafe guidance. It’s the “crash test” mindset applied to language interfaces. A Simple Analogy (that sticks) Think of an LLM like a highly capable
Adversarial Prompt Generation: Safer LLMs with HITL Read More »
If you only look at automated scores, most LLMs seem great—until they write something subtly wrong, risky, or off-tone. That’s the gap between what static benchmarks measure and what your users actually need. In this guide, we show how to blend human judgment (HITL) with automation so your LLM benchmarking reflects truthfulness, safety, and domain
LLM Benchmarking, Reimagined: Put Human Judgment Back In Read More »
Virtual assistants are progressing beyond simple question-and-answer formats to solving complex queries. Today, AI-driven virtual assistants communicate in multiple languages easily, and large language models, or LLMs, power this transformation. Now you can ask your device for restaurant recommendations in English and get an answer in Spanish. That’s what LLMs have made possible in recent
Role of Large Language Models in Powering Multilingual AI Virtual Assistants Read More »
Imagine hiring a new employee. One candidate is a “jack of all trades”—knows a little bit about everything, but not in depth. The other has 10 years of experience in your exact industry. Who do you trust with your critical business decisions? That’s the difference between general-purpose large language models (LLMs) and domain-specific LLMs. While
Building Domain-Specific LLMs: Precision AI for Every Industry Read More »
When most people think of large language models (LLMs), they imagine chatbots that answer questions or write text instantly. But beneath the surface lies a deeper challenge: reasoning. Can these models truly “think,” or are they simply parroting patterns from vast amounts of data? Understanding this distinction is critical — for businesses building AI solutions,
Understanding Reasoning in Large Language Models Read More »
