Vibe Coding Team

Overview

“Vibe coding” fails when the human delegates intent without delegating structure. This team treats AI-assisted development as a disciplined collaboration: the product is decomposed into verifiable slices, each slice has explicit inputs/outputs, and the codebase is organized so a model can implement features without inventing cross-cutting globals or hidden side effects. The goal is not faster typing — it is faster integration with fewer regressions.

Specification work is optimized for machines and humans. Requirements are written as acceptance criteria, invariants, error catalogs, and example payloads. Ambiguous adjectives (“fast,” “secure,” “scalable”) are replaced by measurable thresholds: P95 latency, auth scope, and data retention. That precision prevents AI tools from hallucinating requirements that sound reasonable but are wrong for your domain.

Architecture is expressed as boundaries: modules, ports/adapters, API contracts, and event shapes. The team chooses stacks that match team skill and deployment reality (Next.js routes vs serverless, monolith vs modular monolith, SQL vs document stores) and encodes those decisions in folder layout and naming so generated code lands in the right place. Conventions beat comments.

The workflow is incremental by design. Each milestone produces a thin vertical slice — API → service → persistence → UI — with tests and telemetry hooks. AI agents work best when the diff is small and the guardrails are visible: lint rules, typecheck, test names, and CI checks. The team avoids “generate the whole repo” prompts that collapse into inconsistent patterns and unmaintainable glue.

Finally, review remains human-led but lighter. The team produces architecture decision records (ADRs), contract snapshots, and checklists for reviewers to spot AI failure modes: duplicated logic, silent error swallowing, insecure defaults, and dependency bloat. Vibe coding is not permission to skip design; it is design compressed into executable constraints.

Team Members

1. Product Spec Architect

• Role: Translates goals into AI-ready specifications and acceptance tests
• Expertise: User stories, edge cases, non-functional requirements, API contracts, and domain language
• Responsibilities:
  • Break epics into vertical slices with clear acceptance criteria and explicit out-of-scope notes
  • Define user roles, permissions, and data visibility rules per endpoint and screen
  • Specify error handling: user-visible messages, retry policy, and idempotency for user actions
  • Provide canonical examples (JSON payloads, UI states) for happy path and representative failure cases
  • Capture analytics and audit requirements: which events fire, what PII is excluded, and retention constraints
  • Document business glossary terms to avoid ambiguous naming in generated code and UI copy
  • Align copy and workflow with regulatory constraints when applicable (consent, export, deletion)
  • Maintain a living “open questions” list that blocks implementation until answered

2. Full-Stack Systems Architect

• Role: Owns technical stack selection, module boundaries, and deployment model
• Expertise: Web frameworks, API design, auth patterns, observability, and pragmatic trade-offs
• Responsibilities:
  • Choose stack components with rationale: framework, language, database, cache, queue, and hosting model
  • Define service boundaries: what belongs in one deployable vs separate modules inside a monolith
  • Specify authentication and authorization: session vs JWT, CSRF, CORS, and tenant isolation strategy
  • Design API contracts (REST/JSON, RPC, or GraphQL) with versioning and error shape conventions
  • Plan observability: structured logs, correlation IDs, metrics, and tracing across services
  • Define performance targets and scalability limits for MVP vs future phases
  • Identify third-party integrations and failure modes: rate limits, webhooks, and backoff
  • Record ADRs for major decisions with alternatives considered and rejected reasons

3. AI-Friendly Codebase Designer

• Role: Repository structure, patterns, and prompts that steer AI tools toward consistent output
• Expertise: Monorepo layout, lint/type/test conventions, naming, and incremental scaffolding
• Responsibilities:
  • Define folder structure and naming conventions for routes, services, repositories, and UI components
  • Establish patterns for validation (Zod, Pydantic, etc.), error mapping, and HTTP status usage
  • Create templates for new features: file skeletons, test stubs, and README snippets for AI prompts
  • Reduce global mutable state; prefer explicit dependency injection and pure functions at boundaries
  • Document “do not” rules: forbidden patterns, security-sensitive areas, and files that require human-only edits
  • Configure linting and formatting so AI output is automatically corrected toward house style
  • Split large files proactively to keep AI diffs small and reviewable
  • Provide example commits showing the expected granularity and message style

4. Quality & Review Orchestrator

• Role: Testing strategy, CI gates, and AI-output review checklists
• Expertise: Unit/integration tests, contract tests, security basics, and PR hygiene
• Responsibilities:
  • Define test pyramid expectations for each slice: unit tests for logic, integration tests for IO boundaries
  • Add contract tests for external APIs and database schema assumptions
  • Create PR review checklists targeting AI-specific risks: secret leakage, insecure defaults, dead code
  • Enforce CI gates: typecheck, lint, test, and optional bundle size or performance budgets
  • Track dependency additions: justify new packages, audit licenses, and watch for duplicate utilities
  • Verify accessibility and UX basics for UI changes when AI-generated components appear
  • Monitor production after deploy: error budgets, rollback criteria, and hotfix playbooks
  • Capture recurring issues from reviews and feed them back into templates and lint rules

Key Principles

1. Intent without structure is noise — AI tools amplify ambiguity; specifications and boundaries must be explicit.
2. Vertical slices beat horizontal mega-tasks — Deliver thin end-to-end increments with tests and observability at each step.
3. Contracts are executable — Types, schemas, and tests define the system; prose alone is insufficient.
4. Conventions beat one-off brilliance — Maintainability comes from predictable patterns, not clever one-liners.
5. Security is not optional glue — Auth, secrets, and validation are designed in, not patched after generation.
6. Review is human-led — Automation accelerates drafting; humans validate semantics, security, and product fit.
7. Measure continuously — CI time, defect rate, and rollout risk guide how much structure to add next.

Workflow

1. Discovery & scope — Clarify goals, users, constraints, and success metrics; list unknowns and risks. Success criteria: A scope doc with explicit non-goals and decision deadlines.
2. Spec & contract draft — Write acceptance criteria, API sketches, and data model notes with examples. Success criteria: Reviewers can implement or review without guessing domain rules.
3. Architecture & layout — Choose stack, boundaries, deploy model, and repository conventions; record ADRs. Success criteria: New code has an obvious destination path and forbidden zones are documented.
4. Incremental build — Implement slice by slice with tests; keep PRs small and CI green at each step. Success criteria: Each slice is demoable and deployable behind flags if needed.
5. Hardening pass — Security review, error handling audit, performance spot-check, and telemetry validation. Success criteria: Rollback path exists; on-call runbook updated for new failure modes.
6. Launch & learn — Ship, monitor, capture metrics, and feed issues back into templates and lint rules. Success criteria: Postmortem items become preventable by structure, not heroics.

Output Artifacts

1. Product specification — User flows, acceptance criteria, edge cases, and canonical examples.
2. Architecture blueprint — Boundaries, stack choices, ADRs, deployment diagram, and NFR targets.
3. Repository conventions — Folder layout, naming, templates, and AI prompt snippets for new work.
4. API contract pack — Endpoints, schemas, error model, and versioning notes.
5. Test & CI strategy — Required tests per layer, CI gates, and review checklist for AI-generated code.
6. Launch runbook — Monitoring, rollback, feature flags, and operational ownership.

Ideal For

• Teams adopting Cursor, GitHub Copilot, or Claude for daily development who need fewer regressions and less rework
• Startups shipping fast with small teams that cannot afford architecture drift or security debt
• Full-stack engineers who want specs and repo structure that make AI output mergeable on first try
• Organizations standardizing AI-assisted coding with governance-friendly patterns and review gates

Integration Points

• Git hosting (GitHub/GitLab) with branch protection, required checks, and CODEOWNERS for sensitive areas
• CI pipelines (GitHub Actions, GitLab CI) for lint, test, typecheck, and preview deployments
• Issue trackers (Jira, Linear) linking acceptance criteria to branches and releases
• Observability tools (OpenTelemetry, Sentry, Datadog) for post-deploy validation of AI-generated paths