lucas (@lucas_flatwhite)
2026년 현재 코딩 중심 개발에서 에이전트 엔지니어링으로 패러다임이 이동하는 전환기라는 점을 강조한다. Andrej Karpathy의 Sequoia Ascent 2026 발언을 통해 개발자의 역할 재정의 필요성을 제시한다.
lucas (@lucas_flatwhite)
2026년 현재 코딩 중심 개발에서 에이전트 엔지니어링으로 패러다임이 이동하는 전환기라는 점을 강조한다. Andrej Karpathy의 Sequoia Ascent 2026 발언을 통해 개발자의 역할 재정의 필요성을 제시한다.
Title: P2: I was preparing for interview for Agentic AI Engineer. [2025-05-15 Thu]
interpretable outputs (e.g., natural language
explanations) to facilitate human-in-the-loop oversight.
standards:
- FIPA Standards/specifications (e.g., FIPA-ACL)
- IEEE 1471 (ISO/IEC 42010)
I thing, first step of planning multi-agent system is to
measure what intellectual capabilities we can accure
and manage. #dailyreport #multiagent #agents #agentoriented #agentengineering
Title: P1: I was preparing for interview for Agentic AI Engineer. [2025-05-15 Thu]
- Event-Driven Communication: (e.g., Apache Kafka, RabbitMQ)
- Standardized Communication Protocols: like FIPA
(Foundation for Intelligent Physical Agents) for agent
communication, ensuring interoperability and structured message
passing (e.g., ACL - Agent Communication Language).
- Explainability in Communication: Ensure agents provide #dailyreport #multiagent #agents #agentoriented #agentengineering
Title: P0: I was preparing for interview for Agentic AI Engineer. [2025-05-15 Thu]
Main steps in Agent-Oriented Software Engineering:
1) analyzing requirements - what to do and how fast
2) defining system scope and limits - where to run and
how much money we have
3) identifying agent roles
4) selecting architectures
5) choosing tools
best practices:
- microservices REST or gRPC #dailyreport #multiagent #agents #agentoriented #agentengineering
I like the word harness 🤗
The parallels between leading organisations and building agents are a bit … uncanny
Akshay (@akshay_pachaar)
AI 에이전트의 진화가 모델 자체의 성능보다 주변 환경과 하네스 엔지니어링 개선에 더 크게 좌우됐다는 관점을 제시합니다. 2022~2026년 사이 에이전트 기술이 weights, context, harness engineering 중심으로 발전해 왔다는 흐름을 정리한 분석입니다.

from weights → context → harness engineering (evolution of agent landscape from 2022-26) the biggest shift in AI agents had nothing to do with making models smarter. it was about making the environment around them smarter. here's how agent engineering evolved in just 4
Everyone’s excited about AI agents. But a harder question remains: what would need to be true for them to actually stay aligned with the human job-to-be-done over time? Not just at the prompt, but across decisions, context, and collaboration. Curious how others think about this.
#AgentArchitecture #AgentEngineering #AgentMemory #RuntimeEvaluation #IntentModeling #LLMSystems #AgenticAI #AI #ArtificialIntelligence #JTBD
tonyy (@ttonydev)
@simonw의 블로그 글(에이전트 공학 패턴)에 영감을 받아 작성한 글로, 작성자가 BDD(행동 주도 개발)를 활용해 여러 에이전트를 조정(coordinate)하는 방법을 정리했다는 내용입니다. 에이전트 설계·운영 관점의 실무적 패턴 공유입니다.
Agent Engineering is the future of AI.
It's an iterative blend of product thinking, engineering, and data science that transforms unpredictable LLMs into reliable, production-ready agents.
Ship fast,
observe deeply,
refine constantly.