2 posts tagged with "ai-programming"

Our most important practical finding today was simple:

The shorter and clearer the prompt, the more effectively the coding agent works.

That does not mean removing constraints. It means moving detailed constraints out of prose and into an executable harness: the TeaQL Generation Service, generated typed APIs, model-aware assist, compiler feedback, and tests.

Today we rebuilt TeaQL Agent Kit around that finding. The agent creates a typed domain contract, the Generation Service evaluates it, generated guidance constrains the implementation, and human review happens in parallel.

The smaller, clearer workflow is now distributed as a standard Agent Skill named build-teaql-app.

The Architecture We Actually Want​

Java remains a strong choice for workflow-heavy services such as merchant onboarding, KYC, approvals, accounting, and integration with an established enterprise platform. Rust is attractive for customer-facing services where resource use, predictable latency, and fast process startup matter.

Using both languages does not mean that two services should share one database or one persistence model. That would weaken their bounded contexts. The goal is more precise:

• each service owns its model, database, and deployment lifecycle;
• reviewed metadata generates the domain API for the language used by that service;
• both stacks follow the same query-intent and mutation-audit conventions;
• communication happens through an explicit, versioned integration contract.

We built a runnable Java–Rust payment reference project to test that design instead of treating it as an architecture diagram.