Vibe Coding Doesn't Survive Production

A demo is a greenfield: no legacy, no compliance, no one else committing to the branch. Your codebase is the opposite. The reason vibe coding — prompting until something runs — feels productive and then quietly fails is that it produces output faster than anyone can verify it. With one engineer on a toy project, that's fine. At team and production scale, unverified output costs more than it saves. The patterns below all exist to close that gap.

Context clarity is the highest-leverage step in any agent-assisted build, and it happens before code exists. Run a grilling session against your own docs first: interrogate the requirements, the constraints, the edge cases, the assumptions buried in a ticket from eight months ago. And the output is something you keep: a committed PRD that captures what you're building, with acceptance criteria that crystallize as the grilling sharpens the picture. A context with near-zero ambiguity is the difference between a clean result and expensive rework. Garbage in still means garbage out, and an agent will confidently build the wrong thing faster than any human could.

A context window is ephemeral; your repo isn't. So every artifact worth keeping gets committed and given a durable home that outlasts the session. A consistent per-ticket layout makes that automatic:

feature-name/
  raw/                    external assets pulled from Jira, Figma, Linear
  PRD.md                  the "what" plus acceptance criteria, from the grilling session
  implementation-plan.md  the vertical tracer-bullet slices
  execution-plan.md       running scratchpad kept while building each slice
docs/adr/                 architectural decision records (repo-wide, first-class)

The PRD holds the what, not the how, and it stays committed. The implementation plan is your list of vertical tracer-bullet slices— thin cuts that each prove the whole path works end to end. It can live in the repo, GitHub, Jira, or Linear, as long as it has a life outside any single context session. The execution plan is the scratchpad you keep as you implement each slice with TDD: the surprises and gotchas you hit, captured so you can mine them later into higher-leverage docs like architecture guides and runbooks.

Architectural decision records sit apart from the ticket, in a first-class location like docs/adr/, because they outlast the feature that produced them. Any load-bearing choice — anything hard to reverse — gets logged the moment the grilling surfaces it, with the justification, the alternatives you weighed, and the trade-offs. That's how the work survives a session reset, an onboarding, or your own memory three weeks later, and it hands the next agent the same clarity the last one had instead of starting cold.

Don't let a context window become a junk drawer. Quality degrades as it fills, so start fresh sessions deliberately. Fan out subagents for research so the main thread stays clean. Use handoff skills to pass results between sessions instead of dragging everything along. As a working rule, stay under 200k tokens of context if you want consistent output, and monitor it constantly. Context bloat is one of the most common and least visible causes of declining agent quality.

Generation is cheap now. Verification is where value is won or lost. A long-running autonomous task is only as good as the agent's ability to check its own work, and you have to build that ability in deliberately. Test-driven development is how you build it: red, green, refactor, one test at a time, still produces the cleanest code even when an agent is the one writing it. Each passing test is a checkpoint the agent can't fake. Pair that with Playwright for end-to-end coverage that runs locally and in CI, and the quality of your tracer-bullet breakdown becomes the thing that decides whether you get real value or convincing slop.

You don't need one model to do everything, so use your most capable model for reasoning and planning, where depth matters more than speed. Then switch to faster, high-intelligence models for the building phase, where throughput matters and the plan is already set. Matching the model to the job keeps both cost and quality where you want them, and it stops you from paying premium reasoning rates to fill in boilerplate.

What's key is that none of this is about chasing the newest tool. Advantage compounds for teams that do the unglamorous work of building a codebase agents can read, with context discipline and verification loops tight enough to trust autonomous output. If you lead a technical org, start with what pays off immediately: write the PRD and tracer-bullet slices for your next build, and commit your architectural decisions to the repo where the next agent will find them. Then make TDD non-negotiable for agent-written code.

The future of development isn't vibe coding. It's structured, artifact-driven, agent-assisted engineering, and the teams building that discipline now are the ones who'll still be shipping when the novelty wears off.