Help please, trying to build an AI architect agent

[smashmongo2]

All right, so I should probably just start out by saying that this is where openAI support told me to look for information on this topic. Hopefully someone on here doesn’t mind answering questions, or dealing with complicated problems.

I’m working on Hephaestus AI, an initiative designed to help non-technical users build custom, task-specific AI systems. The central idea is to automate the development process for specialized AI solutions. For example, if multiple restaurants want to automate inventory tasks (like counting boxes), they could pool their resources, split the cost of development, and each retain full control of their own data.

However, I’ve been encountering a significant challenge I call “drift.” When an AI agent attempts to build another AI, small inaccuracies (hallucinations) tend to compound over time, leading the final product to deviate from the original vision. Metaphorically, you start with plans for a human skeleton but end up with a hybrid that’s part-human and part-gorilla. This becomes especially problematic when large amounts of code are generated, as it’s difficult to maintain a consistent structure all the way through.

Another hurdle is merging multiple task-specific AIs into a single, cohesive system. If a business creates two or three specialized AI models, it would be ideal to combine them seamlessly into one larger model without having to rip apart each block of code and reassemble everything piece by piece. I’m looking for an approach that remains flexible yet rigid enough to avoid drift, handle large-scale code generation, and allow for straightforward integration of multiple AI services.

I’m open to the possibility of open-sourcing the initial phase of this project, especially since so much will rely on API calls anyway. Ultimately, the goal is to democratize AI development so that organizations and individuals can build—and jointly fund—tools they otherwise would struggle to create on their own.

Given these challenges, I’d love insight into how best to structure an AI architect system to mitigate drift and simplify combining multiple AI modules. Any advice or resources you can suggest would be incredibly helpful.

Thank you in advance for your time and expertise!

[smashmongo2]

Why I’m Building This: The AI Architect Agent for the Eco-Power House (EPH)

Hello all,

I want to take a moment to explain why I’m working on this project and why I’ve been so determined to get help here—even if it’s been a bit of an uphill battle.

I’m not just trying to build an AI agent for the sake of novelty. I’m building it as part of a much larger initiative called the Eco-Power House (EPH)—a decentralized, energy-positive housing model that integrates renewable energy, smart infrastructure, and autonomous systems. The goal is simple, but ambitious:

Each EPH generates 100% or more of its own power and runs a portion of the local grid in reverse—selling surplus electricity back into the system, ultimately subsidizing housing costs.

But here’s where it gets really interesting—and where AI becomes indispensable.

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Why I Need an AI Architect Agent

Managing these systems manually is slow, expensive, and inaccessible to smaller teams. The EPH architecture requires coordination across:

• Renewable energy systems (solar, wind, battery storage)
• HVAC and load balancing algorithms
• Real-time energy market analytics
• Localized data ingestion and optimization

I’m building an AI Architect Agent to automate much of this complexity—handling infrastructure configuration, deployment, and even small-scale AI design workflows. Eventually, I want it to empower others—especially startups and underserved communities—to build their own microgrid-ready systems without hiring a full dev team.

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Edge AI and Decentralized Infrastructure

Each EPH unit includes a local server cabinet—what I call a “closet node.” It functions as a secure, always-on compute layer for edge AI. If you imagine thousands of these nodes networked across a city or nation, you get the foundation for a decentralized AI mesh.

This could:

• Reduce load on centralized cloud providers
• Improve latency and data sovereignty for AI inference
• Create a distributed computing grid that also serves as resilient energy infrastructure

In essence, EPH isn’t just about energy or housing—it’s about rebuilding critical infrastructure from the edge inward, rather than from the cloud downward.

Why I’m Posting Here

I know some folks in this community have deep technical knowledge. I respect that, and I’d love to tap into it if you’re willing. I’m not a trained software developer—my background is in electrical engineering, which tends to be far more binary: either it works or it doesn’t. Software is a different world, but I’m trying to bridge both.

If you’re interested in helping me push this further—or just want to poke around—please take a look at the repo (or zip, depending on how I package it). Feedback, criticism, contributions—everything is welcome.

Let’s build something that doesn’t just scale tech—it reshapes how we live and power the world.

Thank you,
mongo smash - Army nickname

[smashmongo2]

Elevator pitch

I appreciate that there is a considerable amount of information to cover, and I would like to present a detailed yet cohesive overview of the project I am developing. The project consists of six primary components, with the first four constituting the minimum viable product (MVP): Insulated Concrete Forms (ICF), geothermal HV/AC systems, microgrid technology, and solar energy storage.

The first two components, ICF and geothermal HV/AC, operate in tandem to provide a highly efficient passive heating and cooling solution for residential applications. By integrating these technologies, we establish a meticulously engineered system that significantly reduces energy consumption. When combined with microgrid technology, this system enables a home to function independently of the traditional grid, sustaining power indefinitely through battery backup. Unlike Tesla’s systems—which are designed for temporary power outages—our approach envisions a solution capable of powering a home indefinitely on battery storage.

This system features a 5-megawatt-hour battery pack paired with approximately 650 kilowatt-hours of solar panels, offering substantial energy storage and generation capacity. As technological advancements occur, our goal is to reduce the number of required solar panels by half, further enhancing overall efficiency.

What is particularly noteworthy is the seamless integration of these four components. For example, the geothermal system complements the ICF structure exceptionally well. ICF provides a flexible framework that can be adapted to incorporate advanced features such as earthquake-resistant designs (as implemented in Japan) or hurricane-resistant attributes suitable for regions like Florida. This adaptability allows the system to be customized for various environmental conditions, thereby enhancing both safety and sustainability.

Moving into more technical territory, consider the integration of the solar and geothermal systems. By connecting geothermal cooling with the solar panels, we employ a strategy similar to thermal management in computer hardware. This approach reduces the operating temperature of the solar panels, thereby increasing their efficiency and lowering the risk of fire. Moreover, any excess heat—or ampacity—generated by the solar panels is not wasted; instead, it is captured and reintroduced into the system. For instance, a high-temperature fluid such as oil, heated to between 300 to 350 degrees, can be utilized to generate electricity through a Stirling engine. This engine operates by exploiting the temperature differential between the heated fluid from the solar panels and the cooling provided by the geothermal system, converting lost thermal energy into useful mechanical work.

A similar concept applies to battery operation. As batteries discharge, they generate heat—a phenomenon that we can capture and repurpose. One of the primary challenges with current battery technology is that most systems prioritize energy density over energy retention. To address this, we are collaborating with institutions such as Harvard iLab to develop batteries optimized for long-term energy storage rather than merely transporting energy. This innovation is critical, as it promises extended cycle life and improved durability, particularly for systems where energy is stored in a fixed location.

Our approach to microgrid technology is equally innovative. Rather than simply channeling as much energy onto the grid as possible, our strategy involves carefully pacing the energy flow to extend the longevity of stored energy. The decentralized microgrid network we are constructing is designed to store surplus energy during periods of overproduction and release it during shortfalls. This not only stabilizes the grid but also helps to prevent issues such as the rolling blackouts witnessed during the Enron crisis in California, effectively smoothing out the peaks and valleys of energy production and consumption.

The implications of a large-scale, decentralized microgrid network are extensive. One of the most significant advantages is its ability to facilitate the transition to renewable energy by accelerating the development of the necessary infrastructure. Additionally, the network offers the capacity to reroute power based on regional needs and weather patterns. For example, if a storm is anticipated in Florida, energy can be preemptively redirected from other areas to fill local storage capacities, ensuring that affected regions have adequate power to weather the storm.

While the technical details are complex, this overview is intended to convey a clear picture of the integrated system we are developing. Ultimately, when all six components are fully integrated, the technology will not only revolutionize terrestrial housing but also pave the way for its adaptation in space—subsidizing the construction of habitats beyond our planet.

[smashmongo2]

Why I’m Building an AI That Builds the AI

By now, I hope it’s clear that I’m not just building a tool—I’m building the tool that builds the tools. This is a recursive AI framework, purpose-built to simulate and scale something as ambitious as the Eco-Power House (EPH) network.

To understand why this is necessary, consider this: only 35% of electricity in the U.S. currently goes to powering homes. That means a staggering 65% is consumed by everything else—commercial infrastructure, industrial systems, government operations, data centers, and more. The modern grid is no longer residential at its core. It was originally conceived to power homes, back when Nikola Tesla envisioned an electrified society. But the world has shifted.

Now we’re adding electric vehicles, electrified HVAC, and smart devices to every household. We’re asking the same grid to carry twice the load it was built for—without updating the architecture to match. That’s not just a scalability problem—it’s an existential one.

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Why We Need Autonomous AI for This Transition

To scale EPH across 20,000+ decentralized locations, each producing 3× the power they consume, we need to understand the real impact on the grid. How much surplus energy can be redistributed? How would that change regional power flow? Could this shift actually stabilize volatile areas of the grid—or even relieve existing infrastructure?

Layer on weather forecasting: Can we predictively charge storage systems before severe conditions hit, improving resilience and minimizing blackout risk? Probably. But by how much? Is it marginal or transformative?

These are the types of complex, multi-variable questions that require AI to answer—quickly, reliably, and in real time.

But here’s the deeper reason I’m doing this:
I’m not just building this AI for myself. I’m building it so that others—small businesses, startups, nonprofits—can deploy, customize, and benefit from it too.

Imagine a restaurant that wants to monitor its solar storage, HVAC loads, and perishable inventory risks all in one place. They can’t afford a team of software engineers. But they could deploy a version of this AI with a few configuration changes. That’s the long-term vision: make infrastructure-grade intelligence accessible.

Modeling to Make the Vision Real

Right now, I’m building the modeling tools that I need to show people what’s possible. These simulations are what will let investors, governments, and communities visualize what decentralized, energy-positive housing can look like at scale.

The AI I want to build doesn’t just simulate—it builds, deploys, and maintains itself. It’s an architectural assistant, a systems optimizer, and a deployment engine all in one.

[smashmongo2]

Eco-Power House: Artistic rendering not proportional.

This is a visual aid for those that don’t conceptually understand what it will look like! it’s not to scale, and there are some other corrections that need to be made, but it still gives people a visual representation of what EPH will look like roughly. Again, this is just what the minimum viable product looks like, this is not what a final fully integrated product will look like.

[smashmongo2]

ArchitectAI/
├─ architect/
│ ├─ init.py
│ └─ architect_ai.py
├─ training/
│ ├─ init.py
│ └─ train.py
├─ evaluation/
│ ├─ init.py
│ └─ evaluate.py
├─ data/
├─ models/
├─ logs/
├─ run.py
├─ requirements.txt
└─ README.md

[smashmongo2]

Here’s a Microsoft Word document link containing raw, unedited Python code written for Visual Studio Code. It’s incomplete, but it clearly demonstrates the concept of drift—how code gradually diverges from its original intent as it grows larger and more complex. Around the 10,000-line mark, this drift becomes very noticeable, and I think you’ll see exactly what I mean.

As automation makes it easier to create larger programs, managing this drift will become increasingly important. I’ve rewritten sections of this code about ten times, and I have to mention how incredibly helpful OpenAI’s plug-in for Visual Studio Code on Mac has been. I initially thought Microsoft would restrict the plug-in to Windows, but thankfully they’ve made it accessible on Apple as well. Honestly, editing code with OpenAI’s integration feels even more intuitive and convenient than using GitHub Copilot, even though they’re closely related.

Here’s the link to the document:
https://1drv.ms/w/c/c72da81ef7ad8a6a/EQHib89gZZBMpi4ZltalBmMBwIrNmBInIDhxtI6yYXNk7w

If you do take a look, I hope you find it interesting!

[Diet]

That’s a pretty common theme with this “agents” type hype that’s going on right now.

This topic might be of interest to you: Agent builder platforms misguiding organizations

There’s still no free lunch here

[smashmongo2]

Thanks for sharing this thread—I really appreciate it! The “gold rush” analogy you mentioned is spot on and captures the situation perfectly.

I completely agree that setting realistic expectations and understanding compound probabilities is key. Playing around with these tools definitely helps highlight both the opportunities and challenges involved.

You’re right about needing clearer, practical solutions rather than just recognizing the problem. I think the more precisely we define the issue, the closer we’ll get to finding those answers.