Most AI assistant demos look impressive until you try to actually ship them inside a product. That’s where things usually break. Not because of the model, but because of the infrastructure around it: managing state across steps, handling tool calls, and dealing with retries.
The "Demo-to-Production" Gap
After experimenting with different approaches, we kept running into the same problem: systems were either too abstract and hard to control, or too manual and impossible to scale. We decided to try something different - keeping everything in code. No visual builders, no hidden layers. Just a TypeScript-based workflow that defines how the assistant behaves.
Why Code-First over No-Code?
Surprisingly, this made things much simpler. Instead of "prompt engineering," it started to feel more like actual software engineering:
Explicit state: No more guessing what the agent remembers.
Predictable execution: You control the flow, not a black-box framework.
Easier debugging: Standard logs and traces instead of visual spaghetti.
We ended up with a pattern where a functional in-product assistant is implemented in around 90 lines of code. This isn't a toy example; it's a blueprint for something you’d actually embed in a B2B SaaS.
Join our Live Build session
If you're working on AI features or trying to move your agents from demo to production, we're running a live session to walk through this process step-by-step. We'll cover:
Defining assistant behavior directly in TypeScript.
Handling tool-calling and multi-step flows without the mess.
Real-time observability and debugging.
Curious to hear how others here are approaching agentic infrastructure. Are you sticking with frameworks, or building custom runtimes?
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