AI 에이전트를 위한 컨텍스트 엔지니어링 실전 가이드: 메모리, RAG, 도구 설계부터 프로덕션까지
프롬프트만으로는 부족합니다. AI 에이전트의 성패를 좌우하는 컨텍스트 엔지니어링의 6가지 핵심 원칙과 Python 구현 코드를 다루는 실전 가이드입니다.
Marcus has been gluing systems together for twelve years - first as an integrations engineer at Tray.io, then four years at MuleSoft (post-Salesforce acquisition) leading a team that built connectors for regulated-industry customers. He moved full-time into LLM orchestration in 2023 after a side project - an n8n workflow that triaged his consulting firm's intake email - replaced an actual headcount. He focuses on the boring middle layer: idempotent webhook receivers, dead-letter queues for tool-call failures, and getting Temporal to play nicely with OpenAI's Assistants API. He's published two open-source n8n community nodes (one for Pinecone hybrid search, one for Anthropic prompt caching) and contributed retry-backoff improvements to the LangChain JS repo. Lives in Atlanta. Writes about what actually breaks in production agents, not what looks good in a demo.
프롬프트만으로는 부족합니다. AI 에이전트의 성패를 좌우하는 컨텍스트 엔지니어링의 6가지 핵심 원칙과 Python 구현 코드를 다루는 실전 가이드입니다.