Shared design concept

Definition

A shared design concept is the common model that lets people and coding agents reason about the same system: why it exists, what outcome matters, which domain terms mean what, where responsibilities belong, which constraints bind, and how success will be tested. It is an alignment artifact that can evolve with evidence—not a promise that ambiguity disappears or a license for unattended execution.

Shared mental models and domain language are established software practices; this explicit agent-facing framing remains practitioner-led and implementation-specific.

Why it matters

Coding agents amplify whatever conceptual structure they receive. A shared concept gives subsequent specifications, tasks, interfaces, and tests a coherent vocabulary.

The concept should be inspectable and revisable. Interviews, examples, diagrams, and existing system evidence can expose disagreements before they become parallel implementations.

System anatomy

Purpose

The user or business outcome and the reason it matters.

Domain language

Stable terms for entities, rules, states, and exceptions.

Boundaries

Responsibilities, interfaces, exclusions, and ownership.

Acceptance

Observable conditions that would show the concept works.

Important distinctions

A single prompt

A prompt is one instruction event; a shared concept is durable, reviewable project knowledge used across many decisions.

Detailed implementation

The concept aligns meaning and boundaries without prescribing every code-level choice.

Implementation signals

• Interview accountable stakeholders before decomposing ambiguous work
• Record contested terms, decisions, and open questions
• Test the concept with examples and a thin implementation slice

Failure modes

• Humans and agents use the same word for different ideas
• A polished specification hides unresolved product or domain decisions

Sources and further study

1. AI Engineer — "Software Fundamentals Matter More Than Ever" — Matt Pocock

   A conference talk arguing that shared language, deliberate software design, deep modules, and fast test feedback become more important as coding agents increase implementation throughput.

   Use in this library: Practitioner perspective. This is an attributed practitioner argument illustrated with examples, not a controlled study showing that the proposed practices produce universal outcomes.

   video · video · Published 2026-04-23
2. Matt Pocock — Skills for Real Engineers

   An open repository of composable, user-invoked agent skills covering discovery, specifications, tickets, implementation, review, testing, debugging, teaching, and codebase design.

   Use in this library: Practitioner perspective. The repository documents one evolving workflow. Its MIT license covers repository materials, not separate videos, course branding, or an assurance claim for downstream use.

   guidance · guidance · Published 2026
3. GitHub — GitHub Spec Kit

   Documents an intent-driven workflow that turns a specification into a plan, tasks, and implementation artifacts while supporting multiple coding agents and replaceable process extensions.

   Use in this library: Official guidance. This is official project documentation, not causal evidence that spec-driven development improves every project; its artifact flow is one configurable process model.

   guidance · guidance · Published 2026
4. Anthropic — Agentic coding and persistent returns to expertise

   Reports a privacy-preserving observational analysis of roughly 400,000 Claude Code sessions, including task composition, planning-versus-execution decisions, domain expertise, and operationalized success.

   Use in this library: Empirical evidence. The analysis is observational and vendor-specific, so it does not establish causality or generalize automatically to other tools, teams, tasks, or definitions of success.

   research · research · Published 2026-06-16