 ( ) Fractal Designs, Simple to Complex Source Complex systems emerge from simple parts. Bits form bytes, letters form words, and basic arithmetic builds into entire mathematical models. In software, microservices become platforms like Netflix. This pattern, small components combining into powerful wholes, is the foundation of complexity itself. The great gift to the world that technologies like Apache Kafka gave us was decoupling: it broke monoliths into independent services and teams that could scale.Kafka didn’t just change how data moved, it reshaped how we design systems: modular, loosely coupled, and easier to evolve. Multi-agent systems (MAS) offer a similar shift for AI. MAS introduces a way of structuring intelligence, breaking down complex AI tasks into simpler, focused agents that work together to solve harder problems. It’s a design shift that brings clear benefits: better testing, more reliable behavior, and scalable systems. 이 게시물에서는 MAS가 여기서 중요한 두 개의 개념, 출현, 분해와 함께 어떻게 똑똑하고 신뢰할 수있는 AI 시스템을 구축 할 수있는 더 나은 경로를 제공하는지 탐구 할 것입니다. 출처: Complexity from Simplicity  is the phenomenon where simple rules or parts combine to create unexpected, higher-order behavior. It’s how the physical world is built. Atoms form molecules, molecules form cells, and cells form living organisms. None of the individual components “know” the full picture, but their interactions create it. Emergence 긴급 The Biological Levels of Organization From an Atom to a Multicellular Organism (Source:  ) Biological Macromolecules Biological Macromolecules Biological Macromolecules We see this bottom-up complexity in countless domains. When I first studied   in college, it completely reshaped how I thought about computation. It was part of a theoretical computer science course, and the idea that a grid of binary cells, just alive or dead, could create such rich, evolving patterns was mind-bending. It’s continued to fascinate computer scientists for decades. Conway’s Game of Life Conway’s Game of Life Tetromino Patterns from Conway's Game of Life (Source: Cornell University's Math Explorer Club) Cornell University’s Math Explorer Club Cornell University’s Math Explorer Club With only four simple rules, you could simulate behaviors that looked like movement, reproduction, and even computation itself. That lesson stuck with me: you don’t need to engineer complexity directly. You can build small parts with clear behaviors and let them interact. Complexity emerges. Another example is ant colonies. Each ant follows basic instructions: lay pheromones, follow trails, pick up food. There’s no leader, no central coordination, yet the colony exhibits intelligent, adaptive behavior. It organizes itself. Ant Colony Behavior (Source:  ) Insect Lore, Ant Colony Behaviors Insect Lore, Ant Colony Behaviors Insect Lore, Ant Colony Behaviors 지능은 항상 가장 똑똑한 부분에 관한 것이 아니라 가장 똑똑한 시스템에 관한 것입니다.이것은 우리가 AI에 대해 생각해야하는 방식이며 이것은 멀티 에이전트 시스템이 AI에 가져 오는 원칙입니다. Instead of building one all-knowing model, MAS encourages you to design multiple simple agents, each focused on a narrow task. Like ants or Conway’s gliders, they interact to form a system that can solve problems much larger than any one part could handle alone. The Trouble with Generative AI The Trouble with Generative AI Generative AI is powerful, but it can also be chaotic. The same foundation model that can summarize a document can also write code, explain a joke, or draft an email. That flexibility is impressive, and it’s in part what drives the interest but it also introduces a problem: we’ve packed too much complexity into a single prompt-shaped interface. This is the opposite of how purpose-built AI models are designed. Traditional models are scoped: fraud detection, recommendation, and churn prediction. The inputs and outputs are known, the evaluation metrics are clear, and testing is straightforward. They lack generality, but that’s exactly what makes them reliable, testable, and deployable at scale. 세대는 이것을 흔들어줍니다. It's like giving an employee fifteen unrelated tasks at once, with no prioritization, no clear inputs, and no defined outputs. It’s hard to evaluate their work, not because they’re bad at it, but because we’ve defined the problem in a way that resists structure. From a systems perspective, this is the opposite of emergence. Instead of building complexity through interaction between simple components, we’re injecting complexity all at once and hoping for coherence. It’s brittle, hard to debug, and nearly impossible to test reliably. Generative AI systems suffer from: 
 
 
 
 테스트 어려움 – 입력 공간은 무한하고 출력 공간은 개방적입니다. 무엇이든 말할 수 있는 시스템에서 정확성을 어떻게 주장합니까?  – Small changes to prompts can produce wildly different results. You don’t know what you're going to get. This makes designing and testing these systems more challenging than their conventional alternatives. Poor predictability of outputs  –  Complex overloaded prompts don’t scale well organizationally. Everything becomes entangled in the prompt and the model gets confused. Scalability limits These challenges aren’t just inconvenient, they make generative AI systems hard to engineer, scale, and trust in production. But there’s another way to approach the problem. A way that embraces complexity   structure, not despite it. One that draws from emergence, modularity, and clear system boundaries. through 이것이 어디에  come in. multi-agent systems From Models to Agents From Models to Agents So how do we bring structure back into AI systems? We start thinking in terms of agents. An   is more than just a model. It’s a goal-directed entity with memory, tools, and autonomy. Instead of asking a model to respond to a prompt, you give an agent a task and let it decide how to achieve it. It can plan, call APIs, retrieve information, interact with other agents, and adapt based on context. agent Agent Architecture (Inspired by  ) https://arxiv.org/pdf/2304.03442 https://arxiv.org/pdf/2304.03442 https://arxiv.org/pdf/2304.03442 One of the most powerful things agents can do is execute deterministic processes, like querying a database, running a script, or triggering a workflow. Foundation models provide a natural language interface to those actions, removing the friction that used to require specialized knowledge. The deep database expertise of a senior analyst, how to structure the right query, filter by quarter, join across systems, can now be surfaced and reused by a non-technical executive who simply wants to know  . what’s happening with the revenue forecast This is where agents become more than just clever wrappers around LLMs. They can connect probabilistic reasoning (language) with deterministic systems (code and data), a fusion that gives language models real-world leverage. Even more powerful is when you connect multiple agents together. That’s the idea behind multi-agent systems: a framework where each agent is designed with a narrow focus, but collectively they coordinate to solve complex problems. Planning, routing, executing, refining, all handled by different agents working in concert. AS , agents offer a clean separation of concerns.   explored in this article explored in this article You get modular components with defined responsibilities, interfaces, and scopes. That makes them easier to test, scale, and evolve independently, just like well-designed microservices in software architecture. 그리고 개미 식민지나 Conway의 Game of Life와 마찬가지로, 지능은 하나의 대규모 단체에 내장되어 있지 않습니다. 그것은 소규모, 목적 지향 단위의 상호 작용에서 나옵니다. 이것은 MAS가 자연을 반영하는 곳입니다 : 간단한 규칙을 따르는 작은 에이전트가 집단적으로 정교하고 신뢰할 수있는 행동을 생성 할 수 있습니다. In the next section, we’ll dig into the advantages of this approach and why it might be the most practical path to building complex, trustworthy AI systems. Why Multi-Agent Systems Work One of the key conceptual shifts MAS enables is moving from open-world to closed-world problem framing. A general-purpose foundation model operates in an open world, it can be asked anything, in any way, and is expected to respond appropriately. That’s powerful, but it also makes the system unbounded and unpredictable. In contrast, an agent operates in a closed world: it has a specific goal, access to known tools, and clear success criteria. This boundedness makes agents testable, observable, and composable, exactly what’s needed for real-world reliability. 1. Testability When each agent has a narrow scope, you can test it like any traditional software component. A retrieval agent can be validated on recall and precision. A summarizer can be benchmarked against expected outputs. You no longer have to treat the system like a black box with infinitely many valid responses, you can probe and improve each piece in isolation. 2. Observability In a monolithic LLM interaction, you get input, output, and not much else. With MAS, every agent step is visible: what data was fetched, what reasoning was applied, what decision was made. That makes debugging tractable, auditing possible, and failure modes easier to understand and fix. 3. Composability Agents are building blocks. Once you’ve built a reliable planner or a summarizer, you can reuse them across different workflows. This aligns perfectly with how modern software is built, through reusable, composable services, and opens the door to faster iteration and lower operational overhead. 4. Scalability Different agents can be developed and maintained by different teams. Each can be versioned, monitored, and deployed independently. That’s crucial in a growing organization or product where responsibilities need to be distributed. You scale the system by scaling the team without increasing the complexity for any single contributor. 5.Real-World Restrictions에 대한 조정 Real-world tasks are not free-form language problems, they’re structured workflows with known inputs, outputs, constraints, and goals. MAS allows us to encode that structure into the system itself. You can insert guardrails, fallback logic, human-in-the-loop checks—all within a coherent architecture. MAS doesn’t eliminate complexity. But it organizes it. It gives you the ability to engineer AI systems with the same rigor and reliability we expect from modern software. MAS doesn’t eliminate complexity. But it organizes it. It gives you the ability to engineer AI systems with the same rigor and reliability we expect from modern software. An Example: Revenue Forecasting with MAS An Example: Revenue Forecasting with MAS Imagine a company wants to forecast revenue across regions and product lines. Doing this well requires data retrieval, trend analysis, anomaly detection, and narrative reporting. Trying to prompt a single foundation model to do all of that in one shot is brittle and opaque, hard to test, hard to trust, and hard to scale. With MAS, we decompose the problem: 
 
 
 
 
 
 리트리버 에이전트는 내부 시스템에서 판매 및 재무 데이터를 추출합니다. 분석 에이전트는 결정적인 논리를 적용하거나 예측 모델을 호출합니다.An analysis agent applies deterministic logic or invokes a predictive model. A   checks for missing data, outliers, or violations of business rules. QA agent A   summarizes the results in natural language. reporting agent 플래너 에이전트는 작업 흐름을 조정하고 오류 또는 리트리를 처리합니다.A planner agent orchestrates the workflow and handles errors or retries. Each agent is scoped, testable, and reusable. As a system, it produces a more reliable and explainable result. And because the interface is natural language, a non-technical user can ask a high-level question like  , and get an answer grounded in structured data and deterministic logic. “How are we tracking against Q2 targets?” 이 접근 방식은 비즈니스가 작동하는 방식과 AI를 일치시킵니다 : 모듈 형 시스템, 명확한 소유권 및 검증 가능한 결과. Closing Thoughts Closing Thoughts Building intelligent systems isn’t just about having powerful models, it’s about how we structure and compose them. Complex behavior doesn’t require complex parts. It requires well-designed, simple parts working together, guided by clear boundaries and interactions. That’s the essence of emergence and the core design principle behind multi-agent systems. By breaking down monolithic prompts into smaller, testable agents, we regain control. We shift AI from an experimental black box into an engineered system, observable, scalable, and aligned with real business workflows. Foundation models gave us a powerful interface. MAS gives us the architectural pattern to make it useful, dependable, and extensible. The future of AI isn't just bigger models, it's better system design. Good ol’ practical computer science.  Sean Falconer Written by  Sean Falconer Written by Written by Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Sean also hosts the popular engineering podcasts Software Engineering Daily and Software Huddle. You can connect with Sean on  . 링크

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What Conway, Ants, and Apache Kafka Can Teach Us About AI System Design

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