AI
AI is progressing at a rate unseen in previous technology transitions. Think about it. A month ago the OpenAI Agents SDK did not have a realtime agent. Up until 2 weeks ago Google was releasing updates to Google ADK weekly. Every part of the AI stack is rapidly evolving. Tomorrow? Your carefully chosen tech stack might be holding you back from capabilities that don't even exist yet.
This isn't the gradual evolution we're used to in traditional software. This is a revolution on a quarterly, monthly, weekly and sometimes daily basis. It's important to architect your AI applications for this reality.
Traditional software taught us to build for the long term. Choose your database, pick your framework, and optimize for stability. That playbook is dead in AI development.
Consider what's happening right now. OpenAI's latest models require their specific SDK for real-time capabilities. Google's voice models perform best with the Google ADK. Want deep integration with AWS authentication then go with Strands Agents (from Amazon). Each advancement comes with its own requirements, its own optimal implementation path.
It's now common for AI applications to be built using models from different providers and to use multiple models from each provider.
Smart AI applications now run multiple models. Not as a backup plan, but as the primary architecture. Here's what this looks like in practice:
Your customer service bot might use GPT-4 for complex reasoning, Claude for nuanced writing, and a specialized local model for data privacy compliance. When OpenAI releases a model with better reasoning, you swap it in. When Anthropic improves their safety features, you route sensitive queries there. When an open-source model matches commercial performance at a reduced cost, you migrate.
The secret to surviving rapid AI evolution? Build abstraction layers that separate your core business logic from the framework.
Think of it like building with Legos. Your business logic, the unique value you provide, should snap onto whatever AI framework delivers the best results today. When that framework becomes obsolete (not if, when), you migrate to a new one.
This approach initially feels like over engineering until the first time you need to switch between frameworks or models.
When switching AI frameworks do not overlook data migration.
Every AI framework stores session data, user contexts, and learned patterns differently. When you switch frameworks, you need to migrate this accumulated intelligence. To keep up with the rapid pace of AI progress you also need to have the ability to execute data migrations at a record pace.
Successful AI teams treat data migration as a core feature, not an afterthought. This means:
Companies that master data portability can switch frameworks in days, not months. Those that don't are held hostage by their technical debt, watching competitors leverage new capabilities while they plan multi-quarter migrations.
Building AI applications while planning for constant change requires following best practices of loose coupling and tight cohesion. Business logic, tools, prompts, etc. should be portable.
Start with these architectural principles:
Treat AI models as cattle, not pets. They're replaceable resources.
Build switching costs into your roadmap. Budget time and resources for quarterly/monthly framework and model evaluations and potential migrations.
Instrument everything. You can't optimize what you can't measure, and you'll need data to justify switching technologies.
Tests and evals. Tests and evals should be as portable as possible.
Document patterns. Primatives are similar. So understanding these makes changes to a technology easier.
Your AI tech stack will become obsolete at an extremely rapid pace. The key is to plan for rapide changes and to architect accordingly. Audit your current architecture for framework dependencies. Identify where you're locked in and begin building abstraction layers. Your future self will thank you.