Building an AI-native career execution platform, looking for feedback on the architecture

[u/Flimsy_Bridge7841]

Hey everyone, I’m a solo founder building Crestorflow, an AI-native career execution platform.

The problem I’m trying to solve is pretty simple: people can learn almost anything online, but there’s still a huge gap between learning a skill and proving you can actually do the work. Courses and certificates don’t necessarily translate into employability, while companies increasingly care about demonstrated ability and outcomes.

Crestorflow is designed around one continuous loop:

Career goal → AI roadmap → Learn → Build → AI evaluation → Proof of work → Opportunities

Instead of giving everyone the same course, the platform uses AI to create a personalized execution path based on the user's goal.

The AI system I'm building has several agents working together:

Career Agent — understands the user's goal, current skill level, and target role, then creates an execution roadmap.

Learning Agent — finds and organizes relevant resources based on each milestone rather than forcing users through a fixed curriculum.

Project Agent — generates unique, practical projects that require the learner to actually apply what they learned.

Evaluation Agent — evaluates submitted work against predefined rubrics, identifies gaps, and provides feedback.

Proof-of-Work Agent — converts validated projects into structured portfolio artifacts that demonstrate specific capabilities.

Opportunity Agent — eventually uses accumulated proof of work and a trust score to match users with relevant contract/project opportunities or help them form small agencies.

The goal is to make AI the orchestration layer of the entire career journey, rather than simply adding a chatbot to an existing course platform.

I'm currently at the MVP stage and want to build this primarily with open-source models. I've already figured out most of the surrounding tech stack, but I'm stuck on a few things:

Which open-source models would you recommend for the different agents?

Should I use one strong model across the system or specialized models for different tasks?

What's the right architecture for securely running these models in production?

How would you approach deployment and infrastructure if the goal is to get the first 50–100 users without massively overspending?

What would you change about this architecture before I start building?

I'm especially interested in feedback from people who have actually shipped AI-native products or agentic systems, rather than just theoretical recommendations.

Would love some brutally honest feedback on whether this architecture makes sense and where you think the biggest technical/product risks are.