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

Fractal Designs, Simple to Complex (سرچینه)

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.

د نړۍ لپاره د ټیکنالوژۍ چې د Apache Kafka په څیر موږ ته وړاندیز کړی دی، د انفرادي خدماتو او ټیمونو ته د انفرادي خدماتو او ټیمونو ته وښيي. Kafka نه یوازې د ډاټا حرکت کولو څرنګه بدل کړ، دا د سیستمونو ډیزاین کولو څرنګه رامینځته کړ: ماډولر، خوندي کڅوړه، او د پرمختګ لپاره آسانه.

Multi-agent systems (MAS) offer a similar shift for AI.

MAS د اندیښنې جوړولو یوه لاره رامینځته کوي، د پیچلي AI کارونو په ساده، تمرکز شوي ایجنټونو کې وده ورکوي چې د سختو ستونزو حل کولو لپاره په ګډه کار کوي. دا د ډیزاین بدلون دی چې واضح ګټې وړاندې کوي: ښه ازموینې، د اعتماد وړ رفتار، او د سیسټمونو کثافاتو.

Generative AI often suffers from the opposite problem: too much complexity packed into a single prompt. In this post, we’ll explore how MAS -- along with two other concepts that are important here, emergence, decomposition -- offer a better path for building intelligent, reliable AI systems.

Emergence: Complexity from Simplicity

Emergence 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.

د بیولوژیکي سازمانونو کچه د اتم څخه د ډیرو سیلوري بدن ته (د سرچینې: د بیولوژیکي میکروولیکولونو)

We see this bottom-up complexity in countless domains.

When I first studied Conway’s Game of Life 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.

Tetromino Patterns from Conway’s Game of Life (Source: 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)

This is the essence of emergence. Intelligence isn’t always about the smartest part, it’s about the smartest system. This is how we need to think about AI and this is the principle that multi-agent systems bring to AI.

د يو ټولو معلوماتو د ماډل جوړولو په ځای کې، MAS تاسو ته د څو ساده اګانو ډیزاین کولو ته وده ورکوي، هر یو په یوه ضخامت کې تمرکز کوي. لکه د مور او یا Conway ګلاډرونه، دوی د یو سیسټم جوړوي چې کولی شي د ستونزو حل کړي چې ډیر لوی وي چې هر یو برخه یوازې کولی شي حل کړي.

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.

Generative AI flips this.

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.

د سیسټمونو نظر څخه، دا د نمونې مخالف دی.

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:

• Testing difficulties – The input space is infinite, and the output space is open-ended. How do you assert correctness in a system that can say anything?
• Poor predictability of outputs – 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.
• د کثافاتو محدودیتونه - د پیچلي پرچون پروپیلنونه په سازماني توګه ښه کثافې نلري. هر څه په پروپیلن کې مخنیوی کیږي او د نمونوي به مخنیوی وي.

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 through structure, not despite it. One that draws from emergence, modularity, and clear system boundaries.

That’s where multi-agent systems come in.

From Models to Agents

So how do we bring structure back into AI systems? We start thinking in terms of agents.

An agentد نمونوي په پرتله ډیر دی. دا د یادښت، وسایلو، او اتومات سره یو هدف ته اړتيا لري. د نمونوي ته د پاملرنې ته ځواب ورکولو په ځای کې، تاسو یو اګانې ورکړئ او اجازه ورکړئ چې دا څنګه ترسره شي. دا کولی شي د پروګرام، APIs، معلوماتو ترلاسه کړي، د نورو اګانې سره اړیکه ونیسئ، او د کنکټور په اساس adapts.

Agent Architecture (Inspired by 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.

دا هغه ځای دی چې اټکلونکي د LLMs په اړه د سمارټ پوښونو څخه ډیر دي. دوی کولی شي د احتمالي منطق (لګښت) سره د تشخیصي سیستمونو (کډ او ډاټا) سره اړیکه ونیسئ، کوم چې د ژور ماډلونه د واقعي نړۍ ګټه ورکوي.

نور قوي ده کله چې تاسو د ډیری ایجنټونو سره اړیکه ونیسئ.

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 explored in this article, agents offer a clean separation of concerns.

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.

And just like in ant colonies or Conway’s Game of Life, the intelligence isn’t embedded in one massive entity. It emerges from the interaction of smaller, purpose-driven units. This is where MAS mirrors nature: small agents following simple rules can collectively generate sophisticated, reliable behavior.

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

د مسلکي بدلونونو کې یو د مسلکي بدلون کې د مسلکي بدلون کې د مسلکي بدلون څخه د مسلکي بدلون څخه د مسلکي بدلون څخه د مسلکي بدلون ته راشي.

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. Alignment with Real-World Constraints

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

تصور وکړئ چې یو شرکت د سيمو او محصول کرښې په پراخه کچه د خرڅلاو پیشې کولو ته اړتيا لري. د دې لپاره ډاټا ترلاسه کولو، ټینډینټ تحلیل، anomaly detection، او narrative راپور ورکولو ته اړتيا لري. هڅه کول چې یو واحد بنسټ ماډل ته د دې ټولې لپاره په یوه ټوټه کې کار وکړي د بریښنا او غیر شفاف، سخت د ازمايښت، سخت د اعتماد، او سخت اندازه کولو ده.

With MAS, we decompose the problem:

• A retriever agent pulls sales and financial data from internal systems.
• د تحلیل ایجنټ د deterministic منطق په کارولو سره یا د پیژندنې ماډل په کارولو سره کاروي.
• A QA agent checks for missing data, outliers, or violations of business rules.
• A reporting agent summarizes the results in natural language.
• A planner agent orchestrates the workflow and handles errors or retries.

هر اګانې په سمه توګه، ازموينه او تکرار کیدی شي.

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 “How are we tracking against Q2 targets?”, and get an answer grounded in structured data and deterministic logic.

This approach aligns AI with how businesses operate: modular systems, clear ownership, and verifiable outcomes.

Closing Thoughts

د سمارټ سیسټمونو جوړولو یوازې د قوي ماډلونو په اړه نه ده، دا د دوی جوړولو او جوړولو په اړه ده. پیچلي چلند د پیچلي برخو ته اړتیا نلري. دا د ډیزاین شوي، ساده برخو ته اړتیا لري چې په ګډه کار کوي، د واضح حدودو او تعاملونو لخوا لارښوونه کیږي. دا د نوښت او د ډیری ایجنټ سیسټمونو پیژندنې اصلي اصول دی.

موږ د AI څخه د تجربې سیلیکون بکس څخه د انجینري سیسټم ته بدلون کوو، د نښلیدو، کثافاتو او د واقعي سوداګرۍ کار فورمه سره سمون لري.

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.

Written by Sean Falconer

Sean په Confluent کې د AI کارپوه کارپوه دی چې هغه د AI ستراتیژۍ او تفکر لارښوونې په اړه کار کوي. Sean دی د زده کونکي، د پیل کولو جوړونکي، او Googler. هغه کارونه خپور کړی چې د AI څخه د کوانتوم کمپیوټرونو پورې په پراخه کچه موضوعاتو پوښښ. Sean هم د مشهور انجنيرۍ podcasts Software Engineering Daily او Software Huddle میزبان. تاسو کولی شئ د Sean سره اړیکه ونیسئLinkedIn.