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…
The MessagesPlaceHolder in LangChain.js plays a crucial role in enabling dynamic and flexible construction of chat prompts by acting as a placeholder for a list of messages within a ChatPromptTemplate. Its primary functions are Dynamic Message Insertion It allows you to inject a list of BaseMessage objects (like HumanMessage, AIMessage, SystemMessage, etc) into a specific position within your chat prompt template during runtime. This is particularly useful for managing chat history, where the list of past messages needs to be…
Many AI coding assistants, including Claude Code, charge based on token count – essentially the amount of text processed and generated. This creates what economists would call a “perverse incentives(삐뚤어진 유인책)” – an incentive that produces behavior contrary to what’s actually desired. Let’s break down how this works: It might be difficult for AI companies to prioritize code conciseness when their revenue depends on token count. There’s clearly something going on where the more verse the LLM is, the better…
2022년 11월 ChatGPT가 본격적인 A.I.의 서막을 알렸으니 A.I. 붐이 일어난지 아직 3년이 채 못 된 시점이다. 비록 짧은 기간이지만 근래 우리 피부에 와닿는 이 기술의 파급력은 어마어마하다. 초기 그렇게 덩치가 컸던 A.I. 모델도 3년도 안돼 휴대폰 속까지 들어와버렸으니 A.I.를 모르는 사람은 거의 없을 지경이다. 이렇게 전 세계적으로 파급력도 크고 전파력도 강한 A.I.가 일상생활 속에서 느끼는 체감이 이정도인데 이 업계에 몸담고 있는 나로서는 오죽하겠는가! A.I.가 만들어 내는 결과물을 접하는 이용자의 시각과 A.I.를 활용해 결과물을 만들어 내는 개발자의 입장에서 바라보는 관점은 사뭇…
Edges define how the logic is routed and how the graph decides to stop. This is a big part of how your agents work and how different nodes communicate with each other. There are a few key types of edges: A node can have MULTIPLE outgoing edges. If a node has multiple out-going nodes, all of those destination nodes will be executed in parallel as a part of the next superstep.
“A little piece of advice. You see an agent, you do what we do. Run!” – Cypher to Neo in the movie, “The Matrix” When it comes to LLM-based agents, there is no universally accepted definition, but we can extend the philosophical definition to say that an agent is an intelligent(?) entity that leverages LLMs to solve complex tasks by interacting with the environment via a set of tools. Since LLMs possess good understanding, reasoning & generation capabilities, it makes…
The company also introduced a new tool that helps computer programmers use chatbots when writing code. In September(2024), OpenAI introduced A.I. technology that could “reason” through tasks involving math, coding and science. Now, this technology can tackle similar tasks that involve images, including sketches, posters, diagrams and graphs. On Wednesday(2025.4.16), the company unveiled two new versions of its reasoning technology called OpenAI o3 and OpenAI o4-mini. Each can handle tasks that involve both images and text. These systems can “manipulate, crop and…