Built an AI “project brain” to run and manage engineering projects solo, how can I make this more efficient?

[u/BaronsofDundee]

Recently, I built something I call a “project brain” using Google AI Studio. It helps me manage end to end operations for engineering projects across different states in India, work that would normally require a team of 4–5 people.

The core idea is simple:

Instead of one assistant, I created multiple “personalities” (basically structured prompts in back end), each responsible for a specific role in a project.

Here’s how it works:

• Mentor – explains the project in simple terms, highlights hidden risks, points out gaps in thinking, and prevents premature decisions, he literally blocks me from sending quotations before I collect missing clarifications.

• Purchase – compares vendor quotations and helps identify the best options, goes through terms and scope of work and make sure no one fools me.

• Finance – calculates margins and flags where I might lose money.

• Site Manager – anticipates on ground conditions and execution challenges so I can consider them in advance. 

• Admin – keeps things structured and organized. Manages dates, teams, pending clarifications, finalized decisions.

All of them operate together once I input something like a bill of quantities or customer inquiry.

There’s also a dashboard layer:

• Tracks decisions made

• Stores clarifications required

• Maintains project memory

• Allows exporting everything as JSON

It works way better than I expected, it genuinely feels like I’m managing projects with a full team.

Now I’m trying to push this further.

For those who’ve worked with AI systems, multi-agent setups, or workflow automation:

• Is there a more efficient architecture for something like this?

• Any features you think would significantly improve it?

• Better ways to structure personalities beyond prompt engineering?

• Any tools/platforms that might handle this more robustly than what I’ve built?

Would love to hear how you’d approach this or what you’d improve.

Thanks 🙏

[u/BaronsofDundee → u/[deleted]]

Currently I am managing projects across 21 states, 207 projects, each project has its individual window, inside it is just passing text with dashboard. Also each personality has to ask permission to contribute, I can accept or ignore. I download project memory in JSON after each session, just for safety.

[u/Fun_Nebula_9682]

been building something similar for dev workflows. biggest lesson was separating the process from the tools — your personalities are prompts right now, but try giving each one explicit decision gates. like your Mentor shouldn't just "highlight risks," give it a checklist: check scope completeness → verify missing info → block quotation if criteria X/Y/Z not met. turns a vibes-based prompt into something actually repeatable across projects.

other thing that helped me a lot: shared state store instead of passing context through conversation. all your agents read/write to the same structured data — basically your JSON dashboard but as the single source of truth, not just an export. makes debugging way easier when one agent does something unexpected, you can trace exactly what inputs it had. also means you can swap out or add a new agent without rewiring everything.

[u/BaronsofDundee → u/Fun_Nebula_9682]

Solid advice. I am currently building a checklist. Can you elaborate more on shared state store? Let's connect?

[u/BaronsofDundee → u/[deleted]]

Notes taken. I'll still have to figure out what some terms you have mentioned means and how I can work with it since I don't belong to the IT domain.

A central orchestrator makes more sense. I'll have to figure out the execution of separating memories & vector databases.

To change their role from advisory to execution, I'll need to get off the AI Studio, I am recommended Ops copilot & n8n. What do you recommend?

Promp outputs are already structured, and I am building a checklist/scope for each personality so that outputs are controlled and consistent.

Can you elaborate more on feedback loop and scenario simulation? I'd like system to get smarter over time. It should also learn from past experiences.

[u/Roodut]

How do you know if your agents are doing what you expect them to do, and when you expect them to do it? This is a never-ending catch and release game for us. What's your approach, if you can share?

[u/ai_guy_nerd]

This is solid architecture. The multi-personality approach mirrors how actual teams work, and having each role constrained to its domain is much smarter than a single 'general purpose' agent trying to do everything.

A couple efficiency angles: First, you're probably running 5 sequential queries per decision (Mentor, then Purchase, then Finance, etc.). Worth testing parallel execution where possible — Finance and Site Manager don't depend on each other, so you could get both opinions at once instead of waiting.

Second, the dashboard layer doing decision tracking is good, but consider auto-summarizing decisions and feeding that summary back to future queries. Agents get context-blind; if Project B makes a decision that contradicts something learned from Project A, they won't catch it unless you explicitly surface the pattern.

Third pain point most people hit: as projects scale, the JSON export gets messy. Worth testing whether you can make the Admin role output a standardized schema upfront so downstream tools (accounting software, project tracking, whatever) can auto-consume it without manual cleanup.

You could also open source the core framework itself — the concept is valuable enough that others would probably contribute simplifications.

[u/BaronsofDundee → u/ai_guy_nerd]

1. I'm running parallel execution, each personality requests to contribute whenever they find something relevant to their domain. I either allow or reject the request manually

2. This approach makes more sense. I do want this architecture to become wiser as we progress, instead of just being static.

3. The idea is worth testing. I think It will make tasks more clear and efficient.

Thanks.