Why AI agents need engineering methodology
Fast code generation without quality gates is a recipe for technical debt. Here's why AI agents need real engineering methodology.
Speed vs Quality
Most AI coding tools optimize for one thing: speed. How fast can we generate code from a prompt? But speed without quality creates problems faster than it solves them.
What real engineering looks like
The FL methodology built into Daco Work follows a structured pipeline:
1. **Research** — Understand the domain, existing patterns, constraints
2. **Plan** — Design the solution, identify dependencies, define acceptance criteria
3. **Execute** — Write code with tests, atomic commits, documentation
4. **Quality Gate** — Anti-placeholder scans, test coverage, visual verification
5. **Validate** — Stakeholder confirmation that it delivers business value
Why this matters
A component generated in 5 seconds that breaks the existing test suite costs more than a component built in 5 minutes that integrates cleanly.
AI agents are powerful enough to follow real engineering practices. The question is whether we choose to build them that way.
The Daco approach
Every task Daco executes — whether delegated to a worker via RabbitMQ or executed directly — goes through this pipeline. Tests are not optional. Quality gates are not skippable. Visual proof is required.
This is what separates a tool from a co-worker.