Your AI Agent Works in a Demo. Here's What Breaks in Production.

Prompt Engineering Is Only the Beginning of Building Reliable AI Systems

You've built an AI agent.

It understands natural-language requests.

It calls tools correctly.

It updates application data.

Your demo works perfectly.

Then real users arrive.

Someone sends the same request twice.

Another user loses their internet connection.

Two operations run at the same time.

And suddenly, your biggest problems have nothing to do with the LLM.

Problem 1: Duplicate Requests

Imagine a user asks an AI scheduler:

"Plan my tasks for tomorrow."

The server processes the request successfully.

But the network connection drops before the client receives the response.

The client retries.

Without protection, the application might process the same scheduling operation twice.

ByDesign's documented scheduling architecture includes idempotency, allowing repeated requests to reuse a stored response.

Problem 2: Concurrent Operations

Now imagine two requests arriving close together:

"Move my Tuesday tasks to Wednesday."

"Clear Wednesday afternoon."

If both operate on the same scheduling state simultaneously, their changes can conflict.

ByDesign's architecture includes session locking to prevent overlapping scheduling turns.

This helps maintain a consistent planning process.

Problem 3: Invalid AI Output

Even a carefully prompted LLM can generate invalid scheduling decisions.

For example, it might choose a time slot that overlaps with an existing commitment.

ByDesign's scheduling design includes deterministic verification and repair rounds.

If the proposed schedule violates a constraint, the system can identify the violation and attempt correction.

Changes that remain invalid are excluded.

Problem 4: Applying Changes Too Early

Even a technically valid schedule may not match what the user intended.

That's why ByDesign separates schedule generation from final application.

The user reviews proposed changes before selecting Accept & Schedule.

The Real Challenge of Production AI

Building an AI agent isn't just about selecting a model and writing a prompt.

It's about building reliable systems around the model.

That means thinking about retries, concurrency, validation, state consistency, and user approval.

A good demo proves your AI can work. A reliable architecture helps it keep working when things go wrong.

Explore ByDesign.