<h3 style="font-size: 22px; font-weight: normal;">From the perspective of building actual products, the LLM is just a 'Brain'</h3>There is a common misconception when building AI products: "Just hook up an LLM API." But when you actually build a product, you quickly realize that while the LLM can think, it cannot act.For example, when a user says, "Create a business trip itinerary," the LLM can generate text, but it cannot perform the following tasks:<ul class="editorBulletList"><li class="editor_editorListItem__FKzSL">Searching for flights</li><li class="editor_editorListItem__FKzSL">Comparing hotel prices</li><li class="editor_editorListItem__FKzSL">Creating an itinerary</li><li class="editor_editorListItem__FKzSL">Registering it on a calendar</li></ul>In other words, the LLM is merely a reasoning engine. To build a functional product, you need a system that can execute actions. This structure is what we call AI Agent Architecture.<h3 style="font-size: 22px; font-weight: normal;">Actual Agent Architecture</h3>The most basic agent structure used in real-world services is as follows:<code>User</code> → <code>Orchestrator</code> → <code>LLM (Reasoning)</code> → <code>Tool Router</code> → <code>External Tools</code> → <code>Memory</code>Each component has a clear role:<img src="https://d20jzv1ghtxgkk.cloudfront.net/images_temp/1/image-1777019322743.png" width="90%" draggable="true" style="max-width: 100%; height: auto; cursor: pointer;"><h4 style="font-size: 22px; font-weight: normal;">1. Orchestrator</h4>The layer that manages the entire agent execution.<ul class="editorBulletList"><li class="editor_editorListItem__FKzSL">Tasks: Request analysis, execution loop management, tool call control.</li><li class="editor_editorListItem__FKzSL">Implementation: Typically implemented as a <code>Python Agent loop</code>, <code>LangGraph</code>, or <code>Temporal workflow</code>. This loop is the Agent Execution Engine.</li></ul><h4 style="font-size: 22px; font-weight: normal;">2. Planner Implementation</h4>The Planner decomposes goals into steps. If a user's request is, "Create a business trip itinerary," the output would be:<ol class="editorOrderedList"><li class="editor_editorListItem__FKzSL">Search flight</li><li class="editor_editorListItem__FKzSL">Search hotel</li><li class="editor_editorListItem__FKzSL">Create itinerary</li></ol><img src="https://d20jzv1ghtxgkk.cloudfront.net/images_temp/1/image-1777019348213.png" width="90%" draggable="true" style="max-width: 100%; height: auto; cursor: pointer;">The Planner is usually implemented via LLM prompts:<ul class="editorBulletList"><li class="editor_editorListItem__FKzSL">Example Prompt:</li></ul><pre style="display: block; line-height: 1.42857; color: white; word-break: break-all; overflow-wrap: break-word; background-color: black; border: 1px solid rgb(204, 204, 204); border-radius: 4px; max-width: 90%; overflow: auto; padding: 9.5px !important; margin: 0px 0px 10px !important; font-size: 14px !important;"><code>You are a planning AI.
Break the user request into steps.
Return JSON.

User request:
Create a business trip itinerary</code></pre><ul class="editorBulletList"><li class="editor_editorListItem__FKzSL">Output:</li></ul><pre style="display: block; line-height: 1.42857; color: white; word-break: break-all; overflow-wrap: break-word; background-color: black; border: 1px solid rgb(204, 204, 204); border-radius: 4px; max-width: 90%; overflow: auto; padding: 9.5px !important; margin: 0px 0px 10px !important; font-size: 14px !important;"><code>{
 "steps": [
 "search flight",
 "search hotel",
 "create itinerary"
 ]
}
</code></pre>This result becomes the task queue for agent execution.<h4 style="font-size: 22px; font-weight: normal;">3. Tool Layer Design</h4>The most critical part of an agent system is the Tool Layer. Tools are actual functions the AI can use.<ul class="editorBulletList"><li class="editor_editorListItem__FKzSL">Example: <code>search_flight(),search_hotel(),Calendar(),send_email()</code></li></ul>Tools are typically defined as follows:<pre style="display: block; line-height: 1.42857; color: white; word-break: break-all; overflow-wrap: break-word; background-color: black; border: 1px solid rgb(204, 204, 204); border-radius: 4px; max-width: 90%; overflow: auto; padding: 9.5px !important; margin: 0px 0px 10px !important; font-size: 14px !important;"><code>class Tool:
 name = "search_flight"
 description = "Search flight tickets"
 
 def run(self, query):
 return flight_api(query)
</code></pre>Then, you have the LLM select the tool:<ul class="editorBulletList"><li class="editor_editorListItem__FKzSL">Available tools: <code>search_flight,search_hotel,Calendar</code></li><li class="editor_editorListItem__FKzSL">LLM Output:</li></ul><pre style="display: block; line-height: 1.42857; color: white; word-break: break-all; overflow-wrap: break-word; background-color: black; border: 1px solid rgb(204, 204, 204); border-radius: 4px; max-width: 90%; overflow: auto; padding: 9.5px !important; margin: 0px 0px 10px !important; font-size: 14px !important;"><code>{
 "tool": "search_flight",
 "arguments": {
 "from": "Seoul",
 "to": "Tokyo"
 }
}
</code></pre>This structure is known as Function Calling / Tool Calling.<h4 style="font-size: 22px; font-weight: normal;">4. Agent Execution Loop</h4>Agents operate in a loop:<ol class="editorOrderedList"><li class="editor_editorListItem__FKzSL">User request</li><li class="editor_editorListItem__FKzSL">Plan generation</li><li class="editor_editorListItem__FKzSL">Tool selection</li><li class="editor_editorListItem__FKzSL">Tool execution</li><li class="editor_editorListItem__FKzSL">Evaluation of results</li><li class="editor_editorListItem__FKzSL">Next action decision This loop is the Agent Reasoning Cycle.</li></ol><h4 style="font-size: 22px; font-weight: normal;">5. Memory Design</h4>Memory is usually divided into three layers:<ol class="editorOrderedList"><li class="editor_editorListItem__FKzSL">Short-term memory: Current task context, conversation history, current task, tool results (usually managed via the <code>context window</code></li><li class="editor_editorListItem__FKzSL">Long-term memory: Compressed user information and past activity (usually stored in a separate <code>Vector DB</code> like Pinecone, Weaviate, or pgvector).</li><li class="editor_editorListItem__FKzSL">Working memory: Agent task state (e.g., current plan, task state, intermediate results), usually stored in memory-based <code>Redis or State Store</code></li></ol><h3 style="font-size: 22px; font-weight: normal;">Actual Agent Architecture (Industry-Standard Structure)</h3>A real-world service usually looks like this:<code>Frontend</code> → <code>API Gateway</code> → <code>Agent Service</code> (Next.js / FastAPI Agent) → <code>LLM Service</code> (OpenAI API) → <code>Tool Service</code> (Postgres / External tools) → <code>Memory Store</code> (Redis)<h3 style="font-size: 22px; font-weight: normal;">The Real Difficulties of Agent Systems</h3>Many people think implementing an agent is hard, but the real challenges lie elsewhere:<ol class="editorOrderedList"><li class="editor_editorListItem__FKzSL">Tool reliability: AI calls tools incorrectly.</li><li class="editor_editorListItem__FKzSL">Hallucination: Calling non-existent APIs.</li><li class="editor_editorListItem__FKzSL">Loop problems: The agent getting stuck in an infinite loop.</li><li class="editor_editorListItem__FKzSL">Cost problems: Excessive tool + LLM calls.</li></ol>To mitigate these, real-world services add tool validation, step limits, cost guardrails, and fallback logic.<h3 style="font-size: 22px; font-weight: normal;">Summary</h3>An AI Agent is not just a concept. When viewed as a real system, it requires the combination of these five elements:<ul class="editorBulletList"><li class="editor_editorListItem__FKzSL">LLM → Reasoning</li><li class="editor_editorListItem__FKzSL">Planner → Task decomposition</li><li class="editor_editorListItem__FKzSL">Tools → Action</li><li class="editor_editorListItem__FKzSL">Memory → Context</li><li class="editor_editorListItem__FKzSL">Orchestrator → Execution loop</li></ul>Only when these five are integrated can you create an AI system that actually performs work.

Understanding AI Agent Architecture and Specific Design Methods

#Agent #Design #Orchestration #Planning #ToolDesign #API #Memory #Iteration #Evaluation #AIAgentArchitecture

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