TL;DR

When building Paradigm Shifting - AI agents or using LLMs, don’t ask them to solve big problems all at once. Break the task into modular, minimally responsible components—just like you’d do when structuring real-world engineering teams.

Think of yourself as a maze architect—designing a path for the LLM to navigate from a clear entry point to multiple potential exits (solutions), step by step.

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Template for Building Agents: Making Mazes Great Again

When Margaret Hamilton wrote the Apollo Guidance Software—the mission-critical system for the lunar landing—it took over 6 years and a team of 350 engineers, mathematicians, and analysts at its peak. Margaret didn’t just lead the team; she pioneered the term “Software Engineering,” bringing with it concepts we now take for granted: rigorous testing, fault tolerance, task scheduling, and so much more.

Honestly, the more you learn about her, the more inspiring she becomes.

If you look at the (somewhat simplified) system requirements her team had to tackle, they break down as follows:

1. Sensor Integration and Data Fusion – Read and sanitize inputs from gyroscopes, accelerometers, etc.
2. Actuator Control – Send precise commands to thrusters and throttlers, while maintaining a feedback loop.
3. Fault Tolerance and Reliability – Gracefully handle hardware faults and ensure critical modules can restart safely.
4. User Interface with Astronauts – Display system health, key metrics, and allow manual overrides.
5. Task Prioritization – Automatically prioritize high-stakes tasks like descent control over background processes.

If you asked a top-tier MIT lab to build the same system in 2020 (pre-LLMs), they could probably do it with a team of 10 engineers in a couple of years—thanks to decades of evolution in hardware and tooling. Ask that question today, and with the help of LLMs, it might just be doable in two months.

Why? Because Margaret and her team were manually creating a new paradigm of software development. And with every major paradigm shift since then, developer velocity has soared.

Here are a few of those leaps:

• Object-Oriented Programming – Enabled code reusability through encapsulation and inheritance.
• Relational Databases – Made data retrieval and storage near-trivial.
• Cloud Infrastructure as Code – Gave us on-demand environments with minimal hardware investment.
• Machine Learning Platforms – Abstracted logic via pattern recognition, replacing a lot of manual rules.

But what do all these have in common? What made these paradigms work?

It comes down to clarity and minimalism in responsibility. We’ve all heard it before—classes, modules, teams, orgs… all should have clear, minimal, and separate responsibilities. Why? Because this structure leads to cleaner integration. When components are well-defined and self-contained, connecting them becomes trivial (well, mostly—let’s park the tech debt conversation for another day).

So What Does This Mean for AI Agents?

Here’s my thesis: If we apply this same mindset to how we utilize LLMs, especially for complex systems like spinning up a global social networking platform, we’ll get much better outcomes.

Instead of throwing an LLM at an ambiguous prompt like:

“Help me build the backend for a social networking site”

Break it down.

Just like building a rocket guidance system, decompose the problem into minimal, interlocking tasks. For example:

1. Help me choose a cloud provider (GCP vs AWS)
2. Which orchestration platform fits better? (Kubernetes vs ECS)
3. Design a user service (signup, login, sessions)
4. Build an auth system (OAuth2, JWT, sessions)
5. Implement a feed service (CRUD posts, timelines)
6. Build a social graph (follows, blocks, mutes)
7. Set up a media pipeline (uploads, thumbnails, transcodes)

Now, each prompt given to the LLM is scoped, clear, and constrained. Which means the LLM’s output is far more accurate and actionable.

Sound familiar? That’s how real teams are structured too. Every team owns a narrow slice of responsibility—but together, they build a world-changing product.

For Agent Builders: Make Mazes, Not Messes

So, if you’re working on bootstrapping AI Agents, don’t just throw the whole project at them. Architect the maze. Break your agent’s responsibilities down into clearly defined, minimal sub-problems, and think through how each one ties to the next.

If a task has more than two possible execution paths, break it down further.

Design the maze—and make the LLM walk through it.

That’s how you get not just an Agent that “kind of works,” but one that’s maintainable, reliable, and surprisingly smart.

Let’s call this approach:

“Making Mazes Great Again.”

And if Margaret were building Agents today? She’d probably be mapping out mazes too.

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Drafted and cleaned up by ChatGPT