Stop Vibe Coding. Start Velocity Engineering with Codex and MCP

AI coding tools have created a strange illusion for a lot of non technical leaders.

They see a slick demo where someone drops a single prompt into an AI model and gets a full stack app back. The conclusion is tempting.

Why keep paying for senior engineers when a product manager with good vibes and a clever prompt can do “the same work”?

You know that is wrong. The teams seeing the biggest productivity gains from tools like Codex are the ones who already had strong engineering foundations. The people who know how to structure problems, review results, and control risk get more leverage. The people who do not, just get more ways to create chaos.

The real gap is not between people who use AI and people who do not.
The real gap is between vibe coders and teams that practice velocity engineering.

This post is about making that jump in a way that feels honest to engineers and legible to executives. Codex is the concrete example, and MCP is the backbone that makes it repeatable.

Vibe coding, Vibe engineering, Velocity engineering

There are three very different behaviors that people keep mixing together.

Vibe coding
Vibe coding is the fantasy that you can one shot a magical prompt and have an AI model spit out a complex, production grade system. Sometimes you get lucky and it works once. The tests mostly pass. The demo looks fine. It feels like the rules of engineering changed overnight.

But there is no stable understanding of your architecture, environment, or constraints. The next time you run the same prompt, the behavior changes because the context is different, dependencies shifted, or you simply hit a different part of the model’s probability space. That is not engineering. That is gambling with a very expensive random number generator.

Vibe engineering
Vibe engineering is the first step away from that. You still use natural language, still lean heavily on Codex, but you start thinking like an engineer again. You talk about breaking work into subproblems. You involve tools like MCP to bring in documentation and real context. You stop asking for miracles and start asking for structured help.

Vibe engineering is a mindset. It is the moment you stop treating AI as a party trick and start treating it as a junior teammate who needs clear direction.

Velocity engineering
Velocity engineering is what happens when that mindset turns into an operating system for how your team ships. You have a method. You have tools wired in cleanly. You have feedback loops and tests. You can point to actual metrics such as lead time, incident rate, and coverage improving over time.

Put another way

• Vibe coding chases impressive demos.
• Vibe engineering fixes your intent.
• Velocity engineering fixes your system.

The rest of this post is about how to use Codex and MCP to make that system real.

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Codex as a teammate, not a toy

If you treat Codex like autocomplete on steroids, you will get autocomplete behavior. It might save time on trivial snippets, but it will not move your real delivery metrics.

Used properly, Codex is closer to a junior or mid level engineer embedded in your IDE and tooling. It can

• Read and navigate your repository.
• Propose refactors across multiple files.
• Implement features given a clear spec and examples.
• Write and run tests when given a way to execute them.
  
  

What it cannot do is own accountability. Design, safety, and final judgment are still your job.

Three practical implications follow.

1. Codex is highly sensitive to context quality
   If Codex has access to the right files, logs, and documentation, it can look frighteningly capable. If it does not, it will hallucinate types, misuse APIs, and quietly embed security bugs.
   
   
2. Codex works best when you give it missions, not lines
   Asking “write a function to sort this list” is fine, but you do not need an agent for that. Asking “implement this feature, in this service, with this test strategy and these constraints” is where the tool starts to feel like a teammate, not a gadget.
   
   
3. Codex needs guardrails that live in your system, not in your memory
   The way you bring in context, the way you execute tests, the way you talk to external systems should be standardized and automated. This is what MCP is for.

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MCP in one paragraph for working engineers

The Model Context Protocol is an open standard that lets large language models connect to external tools and data in a consistent way. You can think of MCP as a universal adapter that sits between AI assistants and your real systems.

Before MCP, every model and every tool needed its own custom integration. If you had three internal services, two external APIs, and a couple of models, you got an N times M mess of one off glue code. Each integration aged badly and none of it was reusable.

With MCP, you flip that around.

• Systems expose capabilities as MCP servers.
• AI assistants or IDE extensions act as MCP clients.
• Any compliant model can now see and call those tools through a shared protocol.
  
  

For velocity engineering, the important part is that you can standardize how Codex does things like

• Read and modify code in your repo.
• Pull the latest API documentation from internal or external sources.
• Run tests and linters and return structured results.
• Query logs or metrics from observability systems.
• Interact with feature flags, ticketing, and deployment workflows.
  
  

Once that is in place, you can move away from copy pasting context into prompts and instead let Codex call tools as needed, with real authentication, logging, and permission controls.

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The VIBE method: a pattern you can actually use

To turn vibe engineering into velocity engineering, you need a simple method that engineers will actually follow. Here is one that works well in practice.

VIBE stands for Verbalize, Instruct, Build, Evaluate.

1. Verbalize

Make the problem legible

Most bad AI output is just ambiguous human input. Verbalize is where you turn fuzzy intent into something Codex can work with.

A simple pattern is RICE Q.

Role
Tell Codex who it is in this interaction. For example”

“You are a senior backend engineer on the billing team. You follow our existing patterns and treat the code in this repo as the source of truth.”

Instruction
State the task in clear, boring language.

“Implement support for sending receipts through Provider X whenever an invoice moves to the paid state.”

Context
Use MCP to attach real artifacts. For this example you would include

• The current invoice service implementation.
• The existing receipt email flow.
• Provider X API reference pulled live through a documentation MCP server.
  
  

Examples
Show one similar feature or integration that already exists so Codex can copy the style and architecture.

Q
Ask Codex to reflect and clarify before it touches code.

“Before you make any changes, summarize the requirement, list your assumptions and unknowns, and propose a high level plan. Wait for my confirmation.”

You have now moved from vibes to a usable spec that sits in both your head and the model’s context.

2. Instruct

Get a plan, not a blob

Once the problem is verbalized, ask Codex for a concrete plan and task list.

A good instruction looks like this:

“Given the requirement and the codebase context, propose a step by step implementation plan. Include
a list of files to read,
a list of files to edit or create,
a testing approach,
and explicit checks for security, observability, and roll back.”

Treat the output exactly like you would treat a junior engineer’s plan.

• Push back on unnecessary complexity.
• Cut risky scope.
• Clarify data flows and failure modes.
  
  

Only when that plan looks reasonable do you ask Codex to start writing code. This is where the consulting part of your brain pays off. You are using Codex to surface assumptions early instead of fighting them in production.

3. Build

Use Codex plus MCP as a construction crew. Now you connect the plan to real systems through MCP.

A practical setup looks like this:

• A repo MCP that lets Codex read and patch files instead of working on pasted snippets.
• A docs MCP that fetches the latest API references for each external dependency instead of relying on model training data.
• A test MCP that can run your test suites, linters, and basic commands and return structured results.
  
  

Then you give Codex a mission with clear constraints:

“Follow the approved plan. Work file by file. At each step
read the existing code,
apply the minimal change needed,
run the relevant tests through the test tool,
and stop to ask for help if tests fail in a way you cannot quickly fix.”

Notice what is happening here

• You are not pasting giant blobs of code into a chat window.
• You are not asking Codex to invent unknown APIs from memory.
• You are orchestrating a series of small, observable changes through tools that your team controls.
  
  

That is velocity engineering. It respects the constraints of your system while exploiting the parallelism and persistence of AI.

4. Evaluate

Close the loop with tests and critique.

Velocity without feedback is just flailing faster.

In the VIBE method, Evaluate runs continuously but it is worth calling out explicitly.

At minimum, Codex should

1. Write or update tests for every change it makes.
2. Run those tests through the MCP test tool and surface failures.
3. Propose fixes and re run until the relevant suite is green.
4. Generate a summary of the diff that calls out risk, complexity, and any follow up work for humans.

For more critical paths, ask for deeper critique

• Have Codex review its own changes for performance, security, and readability.
• Ask it to annotate the diff with comments that mimic a thorough code review.
• Feed runtime signals such as error logs or slow query traces back into future iterations through observability tools exposed via MCP.
  
  

From a team lead or executive perspective, this is where trust comes from. You are not asked to believe in some vague “AI quality”. You see tests, explanations, and patterns of review that align with your existing engineering standards.

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The first MCP every team should build

If your team is new to MCP, do not wire in every system on day one. That just recreates the complexity you were trying to escape.

Start with a documentation server.

Build a single MCP server whose job is simple

Given a task and some hints, fetch the most relevant and current documentation for the APIs and systems involved, and surface it in a structured way to Codex.

Before I had this pattern, I would search for docs in a browser, copy chunks into the prompt, and tell the model to read them carefully. It worked, but it was slow, inconsistent, and easy to forget.

With a docs MCP, your standard flow becomes

1. Codex gets a requirement.
2. Codex calls the docs tool to pull relevant pages for each dependency.
3. Codex uses those pages as ground truth when writing or changing code.
   
   

That single capability cuts down a huge number of hallucinated integrations and subtle contract mismatches.

Once that is solid, you can layer on

• A repo server to navigate and modify code.
• A test server to run suites and static analysis.
• A logs or metrics server to debug issues against reality instead of imagination.
  
  

Each new server comes with a small playbook that says

• What the tool is for.
• When Codex should call it.
• What a “good” result looks like.
  
  

This is how you scale from one project to an entire organization without losing control.

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What velocity engineering looks like day to day

Take a concrete scenario.

You are a forward deployed engineer helping a customer connect their core product to a third party billing provider. You need sync, webhooks, retries, and observability. The timeline is measured in days, not quarters.

In a vibe coding world, someone writes

“Build a service that syncs invoices with Provider Y and handles webhooks and retries.”

The model produces a large block of code. You paste it into a new directory. You spend the rest of the week hunting for mismatched field names, missing edge cases, and broken error handling.

In a velocity engineering world, the flow looks very different

1. You write a RICE Q style brief that spells out the requirement, constraints, and failure modes you care about.
   
   
2. You attach context through MCP
   
   
   
   • Existing invoicing service implementation,
   • Internal retry and circuit breaker utilities,
   • Provider Y documentation,
   • and your standard logging and metrics helpers.
     
     
3. You ask Codex for a plan and iterate on it until it passes the smell test for reliability and simplicity.
   
   
4. You let Codex implement the plan step by step using repo and test tools, with your reviews on each meaningful chunk of work.
   
   
5. You use Evaluate to enforce tests, gather diff summaries, and document tradeoffs.
   
   

The outcome is not just that the feature appears faster. It appears in a form that your team can own, extend, and debug. It fits the rest of the architecture. It speaks your logging language. It respects your risk boundaries.

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The executive view: how to know this is real, not theater

From the outside, it is easy to confuse vibe coding demos with real productivity gains. Here is how you can tell if a team is actually practicing velocity engineering.

• Lead time from idea to merged change is going down without a spike in incidents.
• Test coverage and automated checks are trending up, not down.
• Senior engineers say Codex is taking work off their plate, not creating more cleanup work.
• New engineers can become effective in unfamiliar code faster because the AI plus MCP stack shortens the path from “what does this system do” to “I can safely ship changes”.
  
  

You should also see thoughtful guardrails around MCP

• Strong authentication and narrow scopes for each tool.
• Clear logging of which actions Codex performed and why.
• Ownership for each MCP server, just like you have ownership for any other internal service.

If you do not see those things, you are probably watching vibe coding with a shiny integration story on top.

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Closing thought

The first wave of AI coding hype asked whether AI would replace engineers. That question already feels dated.

The interesting story now is about the gap between teams that treat tools like Codex and MCP as toys and teams that treat them as core infrastructure.

Vibe coding will always be good for a dramatic demo.

Vibe engineering is the mindset shift where you start using these systems seriously.

Velocity engineering is where the real value lives. It is Codex plus MCP plus methods like VIBE, wired into your existing processes, with clear measures of success.

Do that well, and you do not just write code faster. You build an organization that ships quickly, safely, and repeatedly.

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