Building intelligent platforms requires careful architectural decisions that balance capability with scalability. At SMITE LABS, we've developed design principles that enable our core agents—AI Scientist, AI Tutor, and AI User Modeler—to work together seamlessly while remaining independently scalable.
Why this matters
Poor architectural decisions early on can limit what your AI systems can achieve. Getting the foundations right enables continuous improvement and the addition of new capabilities without rebuilding from scratch.
Key takeaways
- Modular agent architecture enables independent development and scaling
- Event-driven communication keeps systems loosely coupled
- Shared knowledge representations enable cross-agent learning
- Real-time personalisation requires careful latency management
Conclusion
The architecture of intelligent platforms is as important as the AI models themselves. By investing in solid foundations, we create systems that can evolve and improve over time, delivering increasing value to users.
