The Hard Part Wasn’t the Model. It Was Making AI Composable
Article summary
It’s easy to drop a model into an app. It’s hard to drop one into an ecosystem. That was the realization by . LLMs could write text, summarize pages, answer questions. But in production systems, they didn’t live alone. They needed grounding, routing, fallback, and context injection. They needed to compose. We didn’t need smarter models. We needed smarter interfaces. By now, everyone could deploy a model. But what we needed was integration layers that could: Understand where the model sat in the workflow Inject relevant memory without polluting prompts Chain multiple calls with persistent reasoning Enforce security, cost, and latency constraints without blocking flow The architecture didn’t break because the model failed. It broke because the glue around it wasn’t modular enough to scale. We started treating LLMs as reasoning microservices.
Read Full Article on MediumPractical takeaway
The main idea behind The Hard Part Wasn’t the Model. It Was Making AI Composable is to help teams move from broad theory to clear, repeatable decision making. When teams apply this thinking, they reduce ambiguity and focus on improvements that deliver measurable momentum.
Example scenario
Imagine a team facing competing priorities. By applying the ideas in The Hard Part Wasn’t the Model. It Was Making AI Composable, they can map dependencies, identify risks and choose the next move that produces progress without destabilizing their system.
Common mistakes to avoid
- Trying to redesign everything instead of taking small steps.
- Ignoring real constraints like incentives, ownership or legacy systems.
- Creating documents that do not lead to any change in code or decisions.
How to apply this in real work
Start by identifying where The Hard Part Wasn’t the Model. It Was Making AI Composable already shows up in your architecture or delivery flow. Then pick one area where clarity would reduce friction. Apply the idea, measure its effect and share the learning.
Signs you are doing it correctly
- Teams make decisions faster and with fewer disagreements.
- Architectural conversations become clearer and less abstract.
- Changes land safely with fewer surprises or rework cycles.