At AIF 2024, Genesislab CEO Says Future Competitiveness Will Come from AI Agents

Genesislab CEO Lee Young-bok used his AIF 2024 keynote to argue that future enterprise competitiveness will come from generative AI agents. He traced the arc from AI interviewer through training, assessment, promotion, and offboarding — the full HR cycle on agents.

Share
At AIF 2024, Genesislab CEO Says Future Competitiveness Will Come from AI Agents

Source: Sisa Journal e (시사저널e) — [AIF 2024] Future Enterprise Competitiveness Will Come from Generative AI Agents Original in Korean

Sisa Journal e covered the 10th AI International Forum (AIF 2024) on September 12 at the Grand Hyatt Seoul, where Genesislab CEO Lee Young-bok gave a talk on “Enterprise Decision-Making in a World of Generative AI Agents.”

Lee’s core message was singular: “Future enterprise competitiveness will come from generative AI agents.” He projected the AI market would reach around KRW 200 trillion by 2027, and added: “Only the companies that move fast will stay competitive.”

The talk walked through the technical anatomy of an AI agent. LLMs provide broad language capability but come with gaps in factual accuracy and contextual grounding; RAG fills those gaps by connecting the model to external knowledge bases. On top of that, AI agents automate complex work by cycling through four steps: understanding the question, planning, using tools, and iterating.

For Genesislab’s first deployment, Lee pointed to HR. “We’re building an AI interviewer agent in HR.” The path extends beyond hiring — training assessment, promotion selection, and offboarding — using video-based analysis of non-verbal behavior and competency signals. LG, Hyundai Motor, Samsung, and government and municipal bodies were named as current customers.

The technical deep-dive and the rest of the forum’s sessions are covered in the original article.

Read more

단일 LLM의 한계를 넘어서: Multi-Agent System은 왜 필요한가

단일 LLM의 한계를 넘어서: Multi-Agent System은 왜 필요한가

단일 LLM으로 복잡한 비즈니스 문제를 해결하는 접근은 현실에서 쉽게 한계에 부딪힌다. 이 글에서는 단일 프롬프트부터 멀티 에이전트 시스템에 이르기까지 AI 아키텍처의 발전 단계를 분석하고, 각 구조가 왜 실패하거나 부족했는지 그 이유를 짚는다. 그리고 그 흐름 속에서 도출되는 멀티 에이전트 스케일링 법칙이 B2B 플랫폼 설계에 어떤 시사점을 주는지 살펴본다.