# Spec-Native Development for AI Coding Agents

> fabriqa.ai uses spec-native and spec-driven development to turn intent into requirements, tasks, acceptance criteria, agent execution, and review.

Agentic coding gets messy when every task starts from a one-off prompt. fabriqa.ai makes the spec native to planning, routing, execution, and review. Agents execute with context. Humans review with confidence.

## The Spec-Native Loop

### Capture intent

Turn product goals into requirements, constraints, and acceptance criteria a person and an agent can both read.

### Decompose into tasks

Break the spec into implementation, testing, review, and documentation work with explicit boundaries and outputs.

### Route to the right agent

Each task goes to the agent best suited for it, with shared context and provider history preserved across handoffs.

### Review against the spec

Diffs are evaluated against acceptance criteria, not against the last prompt. Anything that drifts gets routed back.

## Without Specs Versus With fabriqa.ai

Without specs, agents generate plausible work that can miss the goal, skip edge cases, ignore acceptance criteria, and optimize for the last prompt instead of the real product intent.

With fabriqa.ai, the spec is native to planning, execution, and review. Each agent has a clear task, and every change can be checked against the same source of truth.

## Related Pages

- [Agentic Development Environment](https://fabriqa.ai/agentic-development-environment.md)
- [AI agent orchestration](https://fabriqa.ai/ai-agent-orchestration.md)