singularity-forge/docs/building-coding-agents/02-what-to-keep-discard-from-human-engineering.md
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Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-25 22:39:33 -06:00

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What to Keep & Discard from Human Engineering

KEEP & Amplify

Practice Why It Matters More for AI
Clear product intent & experience specs AI needs direction, not instructions. "How should it feel?" drives architecture.
Acceptance criteria as the backbone Becomes TDD at its logical extreme — human writes tests in natural language, AI makes them true.
Vertical slicing Even more critical — prevents AI from going deep down a wrong path fast and confidently.
Interface-first approach Creates natural checkpoints, makes systems modular and replaceable.
Explicit constraints & non-functional requirements Narrows the search space. Without them AI may produce technically correct but strategically wrong systems.
Architecture Decision Records (ADRs) Prevents AI from "accidentally" undoing decisions made weeks ago.
Feedback loops Build → test → observe → refine. Accelerated to machine speed.

DISCARD

Practice Why It's Dead Weight
Estimation rituals (story points, velocity, sprint planning) AI doesn't get tired, doesn't context-switch, works at machine speed.
Communication overhead (standups, design reviews, PR reviews) Only one communication channel matters: human ↔ agent.
Manual code review for style Automated linting + formatting handles this deterministically.
Step-by-step instructions Provide outcomes, not "how."
Heavy upfront documentation AI can read the entire repo instantly. Document intent and why, not how.
Gradual skill-building No ramp-up, no knowledge silos, no "only Sarah knows how that module works."
Defensive architecture against human error Tests still needed, but for a different reason: verifying AI's interpretation of intent.

The New Human Role

Responsibility Description
Defining "good" Vision, personas, experience specs, success metrics
Taste & judgment Aesthetics, emotional experience, brand voice
Strategic decisions Which problems matter, product pivots
Gut checks at milestones Does this feel right?

The core shift: Human = intention + taste. AI = exploration + execution.