"Vibe coding" was coined by Andrej Karpathy in a February 2025 tweet: "There's a new kind of coding I call 'vibe coding', where you fully give in to the vibes, embrace exponentials, and forget that the code even exists." A year and a half later, the practice has matured from a tweet into a genuine engineering style — with its own tools, its own best practices, and its own emerging failure modes.

This is what we have learned at Techglock from doing it on real client projects, what we recommend, what we have stopped doing, and what's worth your attention as the tools keep evolving through 2026.

What Vibe Coding Actually Is in 2026

The honest definition: vibe coding is letting a capable AI agent write most of the code while you focus on describing intent, reviewing output, running tests, and steering at the strategic level. You still read the diff. You still decide what ships. But you stop typing characters into a file and start typing thoughts into an agent.

The shift is not "AI helps you write code" — that's just autocomplete, which has existed since 2021. The shift is "AI writes whole files, refactors across the codebase, runs tests, fixes its own type errors, and asks you to review the result." That's a different workflow with a different cognitive load.

By mid-2026 the practice is genuinely productive for the right kind of work. It is genuinely dangerous for the wrong kind. The distinction matters.

The Tools That Won

Twelve months ago the landscape was crowded: Cursor, Windsurf, Cline, Aider, Continue, Cody, Copilot, and a dozen others. By mid-2026 the field has compressed to roughly four serious tools, each optimised for slightly different workflows:

  • Cursor — still the IDE most professional engineers reach for. Strongest agent loop, deepest editor integration, best handling of large codebases.
  • Claude Code — terminal-native agent. Best for headless workflows, scripts, refactors that touch many files, and anyone who lives in a terminal rather than an IDE.
  • Windsurf — the strongest competitor to Cursor on the IDE side. Cascade is genuinely good; the value proposition is mostly indistinguishable.
  • GitHub Copilot Workspace — the enterprise default. Integrated with GitHub Issues, Actions, and PR flows. Less raw capability than Cursor or Claude Code, but the org-level controls and SOC 2 story matter at scale.

The models powering these tools also matter, and the recommendation shifts every few months. As of mid-2026 the consensus among engineers we work with: Claude Opus 4.8 for agentic work that crosses files, GPT-5.5 Pro for general-purpose pair-programming, Gemini 3.5 for very large codebases where the context window is decisive. Most teams route between them.

Where Vibe Coding Works Brilliantly

The pattern of work where AI agents materially outperform a senior engineer working alone:

  • Greenfield prototypes. Anything where "get to a working demo" is the goal and the production hardening will happen later. Agents can ship a working SaaS skeleton in an afternoon that would have taken a team a week in 2022.
  • Cross-cutting refactors. Renaming a concept across 200 files, migrating an ORM, updating a deprecated API call everywhere — work that's mechanical but tedious. Agents do this reliably and consistently.
  • Test writing. Generating unit tests for existing code, writing test cases for edge conditions, expanding coverage. The agent reads the implementation, generates tests, runs them, fixes failures.
  • Boilerplate-heavy code. Form components with validation, CRUD admin panels, OpenAPI clients, data transfer objects. Anywhere the pattern is rote and the variations are minor.
  • Reading and explaining unfamiliar code. Drop an agent into a codebase you've never seen, ask it to explain the architecture, and you'll have a usable mental model in ten minutes.
  • Migration projects. Vue 2 to Vue 3, Angular to React, JavaScript to TypeScript, Webpack to Vite. The agent handles the syntactic transformation; you handle the architectural decisions.

Where Vibe Coding Fails Badly

And the failure modes — patterns where teams who go all-in produce code that has to be partially rewritten later:

  • Subtle business logic the agent didn't get right. The code compiles, the tests pass (because the agent wrote the tests too), and the behaviour is plausibly correct — but the edge cases the business actually cares about were missed. This is the most insidious failure mode.
  • Performance-critical code. Agents will write correct algorithms that aren't optimal. For most code that's fine. For the inner loop of a hot path, it isn't.
  • Security-sensitive code. Auth, payment, multi-tenant isolation. Agents make mistakes that look reasonable and aren't. We require human review of every line in these areas.
  • Architecture decisions. Agents are good at executing within a chosen architecture and bad at choosing the architecture. The question "should we use a queue here?" still belongs to a human.
  • Unfamiliar codebases with no tests. Without a test harness as a guardrail, the agent's changes are easy to break things you didn't expect.

Context Engineering Replaced Prompt Engineering

The term "prompt engineering" peaked in 2023. The term that replaced it in 2026 is "context engineering" — the practice of giving the agent the right information at the right time so it doesn't have to guess.

Concretely, what this looks like:

  • A CLAUDE.md / cursor.md / .windsurfrules file in the repo root that describes the project, coding conventions, and gotchas the agent should know.
  • Up-to-date type definitions and JSDoc. Agents read types. Bad types produce bad code.
  • Linked documentation in the prompt. If the agent is touching the Stripe API, paste the relevant Stripe docs link. The agent will fetch and read them.
  • Tight scope per task. Asking the agent to "build a checkout flow" is a context engineering failure. Asking it to "implement the price-calculation function with these inputs and this expected output" is a context engineering success.
  • Example code from the same codebase. Show the agent the pattern you want it to follow. It will match the pattern.

The shift is from "craft a clever prompt" to "structure the inputs so the right answer is obvious." This is a normal engineering skill, not a magic art.

The MCP Standard Made Agents Composable

The Model Context Protocol (MCP), launched by Anthropic in late 2024 and adopted by every major agent in 2025–2026, is the underrated infrastructure win of vibe coding. It lets agents talk to external systems — databases, search indexes, cloud APIs, your company's internal tools — through a standard interface. Once a service exposes an MCP server, every MCP-capable agent can use it.

What this means in practice: by mid-2026 we connect our agents to a database MCP (for schema introspection), a Linear MCP (for ticket context), a Stripe MCP (for production data), a GitHub MCP (for cross-repo work), and a custom company MCP (for internal API docs). The agent reasons across all of them.

If you are using a coding agent in 2026 and have not configured any MCPs, you are missing most of the productivity gain. The agent without context is autocomplete; the agent with the right MCPs is a collaborator.

Spec-Driven Development Came Back

An unexpected effect of vibe coding: teams who tried "let the agent figure it out" produced bad code; teams who wrote a specification first and pointed the agent at it produced good code. Specifications — formal-ish descriptions of what a feature should do — have come back into fashion not because process people wanted them but because agents work better with them.

The format that works for us:

  1. Intent. What problem are we solving and for whom.
  2. Acceptance criteria. What's true when this works.
  3. Constraints. What's not allowed (other systems, data shapes, performance budgets).
  4. Open questions. Anything the human needs to decide before the agent runs.
  5. References. Links to existing similar features in the codebase, library docs, related tickets.

Three or four paragraphs is enough. The discipline of writing them surfaces design decisions early — frequently catching ambiguities that would have shipped as bugs.

The "Vibe Debt" Problem Is Real

The failure mode we now actively watch for: code that was written by an agent, plausibly works, has tests that pass, and is structured in a way that humans struggle to maintain. We call it vibe debt — the technical debt of code that wasn't shaped by a human's deliberate mental model.

Common symptoms:

  • Excess abstraction layers that exist because the agent generated them, not because the problem required them.
  • Duplicate implementations of the same primitive across files because the agent didn't notice the existing one.
  • Tests that test the implementation rather than the behaviour, making future refactors painful.
  • Inconsistent error-handling patterns across files because each agent run made different choices.
  • Type definitions that are technically correct but semantically loose, hiding bugs.

The cure is the same as for any technical debt: someone has to read the code with intent, understand the whole, and refactor toward simplicity. Vibe coding doesn't remove this step; it just delays it.

What We Recommend at Techglock

Our current playbook for client projects:

  1. Agents write the first draft, humans write the architecture. The big shape — the module layout, the data model, the public APIs — is human work. The implementation behind it can be agent work.
  2. Every PR is human-reviewed, regardless of who wrote it. No exceptions. Reviewers spend more time on agent-written code than on human-written code in practice.
  3. Tests as guardrails, not as documentation. We want the agent to be able to verify its own changes; we want the human to verify the verification.
  4. MCPs for everything we work with. Database, ticketing, Stripe, internal APIs, GitHub. The agent should never have to guess the schema.
  5. Specs before code for non-trivial features. Three paragraphs minimum.
  6. Pair-program for the first 20 minutes of any agent-led task. Get the context right; then you can step away.

What Vibe Coding Hasn't Changed

For honesty, here is what's still the same:

  • Software engineering is still mostly about deciding what to build. Agents make execution faster; they don't decide what's worth building.
  • Senior engineers are still the bottleneck. The skill of evaluating a change for correctness, security, and maintainability is more valuable, not less.
  • Codebases still rot when nobody owns them. The agent will happily generate code on top of code on top of code. Someone still has to pay the maintenance cost.
  • Customer empathy and product taste are still the hardest skills. No agent in mid-2026 is good at deciding the right product to build.

Where to Start If You Haven't

For engineers and teams new to agent-driven development, the order we recommend:

  1. Install Cursor or Claude Code. Pick one, learn it well, expand later.
  2. Set up a CLAUDE.md or .cursorrules for your repo. Spend an hour on this — it pays for itself in a week.
  3. Configure one MCP. Database is usually the highest impact for the lowest effort.
  4. Start with a low-risk task. A boilerplate component, a CRUD endpoint, a test expansion. Build the muscle of reviewing agent output.
  5. Try a spec-driven task. Write a three-paragraph spec, hand it to the agent, review the result. Iterate the spec, not the code.

If you only do one thing after reading this: drop a CLAUDE.md or .cursorrules file in your current project tomorrow morning. Describe the codebase, the conventions, the gotchas. Use the agent for one task. You will understand what the term "vibe coding" actually means by lunch.