숲 해설을 위한 영어 공부
영어로 I think you. 라는 말은 성립하지 않는 문장이다. 반드시 I think of/about you. 로 써야 하는데, 이는 think라는 동사가 ‘사람’ 자체를 직접 대상으로 삼는 것이 아니라, 그 사람과 관련된 ‘내용이나 개념’을 대상으로 삼는 성질을 가진 동사이기 때문이다. Think라는 동사는 기본적으로 머릿속에서 어떤 생각을 떠올리는 것인데, 이는 실체적인 물체를 직접 조작하거나 움직이는 “물리 동작”이 아니라, 뇌 속에서 생성되는 “비물질적 정보 처리 행위”에 가깝다. 즉 개념적 대상, 기억, 감정, 판단 같은 ‘내용’을 취급하는 동사이기 때문에, 목적어도 그에 맞추어 “개념화된 대상”을 요구하게…
Now Silicon Valley is in the middle of an artificial intelligence boomthat bears some obvious resemblances to the dot-com boom. Much of the rhetoric about a glorious world to come is the same. For all the similarities, however, there are many differences that could lead to a distinctly different outcome. The main one is that A.I. is being financed and controlled by multitrillion-dollar companies like Microsoft, Google and Meta that are in no danger of going kaput, unlike the dot-com start-ups that were…
The specification is organized into three distinct layers that work together to provide a complete protocol definition: Layer 1: Canonical Data Model defines the core data structures and message formats that all A2A implementations must understand. These are protocol agnostic definitions expressed as Protocol Buffer messages. Layer 2: Abstract Operations describes the fundamental capabilities and behaviors that A2A agents must support, independent of how they are exposed over specific protocols. Layer 3: Protocol Bindings provides concrete mappings of the abstract operations and data…
In the A2A protocol, when a client sends a message to an agent, the agent might determine that fulfilling the request requires a stateful operation (e.g., “generate a report,” “book a flight,” “summarize a document”). This is where the Task Object comes into play. It is the fundamental, stateful unit of work processed by the A2A Server (remote agent) for an A2A Client, encapsulating the entire interaction related to achieving a specific goal or request. Key characteristics of a Task include: By defining interactions within…
No ‘the” when it’s on activity : I’m going to bed now We don’t use “the” when we describe everyday places and activities Use “the” when it’s a place or physical location : The school is on the corner. I’m in class now (activity) The class when on a trip (group of students) musical instruments : I’m playing the piano: I bought a piano Don’t use “the” with lakes : Lake Tahoe, Lake Michigan Use “the” with rivers : The…
If traditional prompting is like writing a static cheat sheet before an exam, Agentic Context Engineering(ACE) is like maintaining a Github repository for prompts – one that evolves, branches, and merges over time, Instead of freezing a model’s context in place, ACE treats it as a living playbook that grows smarter with every experience. Think of context here not as a paragraph of instructions, but as a dynamic memory system – a continuously evolving notebook of strategies, mistakes, and best…
AI Agents typically focus on executing predefined tasks efficiently, automating repetitive workflows. On the other hand, Agentic AI interprets goals, plans across tools, makes independent decisions, and adapts in real time – bridging the gap between automation and true intelligence. This difference shapes how business can reduce costs, boost efficiency, and create experiences that feel less like command and more like collaboration.
When developing AI agents, understanding prompt engineering is essential. Prompt engineering is the key to effectively working with LLMs. It involves crafting inputs that guide these sophisticated AI systems to generate accurate and relevant responses. Mstering skill ensures that developers can leverage the full potential of LLMs, making interactions with AI more productive and precise. There is a. lot of hype surrounding prompt engineering, with numerous videos, blogs, and articles claiming to reveal its “secrets.” However, it’s important to be…
Encapsulating Thoughts and Intent Action blocks represent a model’s decision at a given point in its reasoning loop. Rather than producing only a final answer, the model under MCP generates intermediate actions that reflect its plan. Each action block contains structured metadata describing what the model intends to do next. This can include calling a tool, representing clarification, storing an observation, or generating final output. For example, an agent tasked with summarizing documents might produce this action block: This tells…
Memory: Persistent Context Across Reasoning Cycles Memory in MCP serves as the persistent, structured storage that an agent can use to reference previous interactions, maintain state, or build context over time. Unlike traditional LLM memory, which is simulated by stuffing previous outputs into the next prompt, MCP memory is external, addressable, and queryable. Memory can include: Each memory object can be assigned an identifier and passed to the model as part of the context. The model can then reason over…