How to Engineer a Multi-Agent Pipeline for Production Reliability

The useful part

We built a nine-agent pipeline that turns a natural language use case into working code, an interactive preview, and an implementation guide in under 4 minutes. It serves roughly 800 to 1,000 users per day, but its first production version consumed about 30,000 tokens and made 15 to 20 Pro-tier model calls per request. Putting all of those responsibilities into one prompt would have created an oversized context, less predictable output, and no clean place to isolate failures.

How it works

• Each stage can be tested, tuned, and assigned a model based on the work it performs.
• Decompose work around failure boundaries The system needed to analyze a requested use case, find relevant APIs, generate visual configuration, write code, inspect that code, repair defects, and produce...
• The architecture was appropriate because the workflow already contained...
• A root agent manages session state, routing, and runtime instruction composition.
• Specialized agents handle analysis, styling, code generation, evaluation, refinement, and documentation.

What to take from it

That separation matters because it creates operational boundaries. Each receives a narrow responsibility, an explicit input, and an expected output. Every boundary introduces orchestration, state transfer, and another potential failure point.

Details worth keeping

A style configuration can be validated without rerunning code generation. An evaluator can reject unsafe code without rewriting unrelated sections.