What's Next After Vibe Coding? | How Will Development Styles Change in the AI Era?

“Development where AI generates code based on your vibe” is certainly fast and easy. However, many of you have probably found that this approach tends to fall apart when dealing with slightly larger features, multi-person development, or modifications with operations in mind.
In fact, if you read the official materials from various companies today, you’ll see that the focus of development is beginning to shift from “generating code on the spot” to “planning, implementing, verifying, and finalizing through review.”
I’ll carefully outline what comes next after the approach commonly known as “Vibe Coding,” based on official websites, documentation, and release notes. To put it simply, the next mainstream approach will be a development style that combines agent-based, specification-driven, and evaluation-driven approaches.
In the main text, we’ll examine, in order, why we can say this, what will change in practical settings, and where individual and team developers should start.

First, the Conclusion: What Comes After Vibe Coding Is “AI-Agent-Based Specification, Verification, and Review Development”

As of now, we have not found any official documents from public institutions or major vendors that provide an official definition of the term “Vibe Coding” itself.
Therefore, for the purposes of this article, we will treat it as a term that generally refers to “a development style where you make requests in natural language with a casual tone, prompting AI to write code with enthusiasm.”

Based on this, a review of primary sources reveals that companies are promoting the following direction:

• In an official OpenAI release (May 16, 2025), Codex is positioned as a “cloud-based software engineering agent capable of handling multiple tasks in parallel.”
• The official GitHub blog (May 19, 2025, updated May 23, 2025) illustrates the workflow in which the Copilot coding agent receives an issue, works on it in the background, and even generates a draft pull request
• Google’s official documentation (“Agent Mode Overview,” as of February 24, 2026) states that Gemini Code Assist’s agent mode is based on a multi-step workflow involving proposal, approval, tool usage, and execution
• Anthropic’s official documentation (“Best Practices for Claude Code”) repeatedly recommends practices such as “explore, plan, and then code” and “providing a way to verify your own work.”
• The official OpenAI developer blog (December 30, 2025) summarizes the major shift in 2025 as a transition from “step-by-step prompts” to “a workflow where tasks are delegated to agents.”

In other words, what’s coming next isn’t merely “automated generation by AI.”
The major trend evident from official sources is a shift from development where humans make rough requests and AI writes the code on the spot to development where humans define objectives, constraints, and success criteria; AI generates implementation candidates; and the final product is confirmed through testing and review.

The benefit is increased reproducibility.
No matter who makes the request to the AI, it becomes easier to achieve results of a consistent standard.

The downside is that it may feel a bit cumbersome at first.
The joy of creating something on the fly diminishes, and it becomes necessary to establish specifications, verification processes, and rules.

One thing to watch out for is that the smarter the AI becomes, the more likely it is to function even with vague instructions.
Just because it works doesn’t mean it’s correct; in fact, it becomes easier to quickly mass-produce “misimplementations that seem to work.”

Personally, while a momentum-driven approach is quite useful for short scripts or UI drafts, I’ve found that when making changes to an existing codebase, writing out specifications and verification criteria clearly reduces the need for revisions.
Going forward, “designing to make it harder for AI to make mistakes” will be more important than “techniques for getting AI to write code.”

Changes That Will Occur Here

• Define the objectives and constraints before writing code
• Determine the success criteria first
• Assign tasks to the AI as a “responsible party” rather than an “implementer”
• Evaluate the output not just as code, but as a whole—including PRs, tests, and explanations

Why Vibe Coding Alone Will No Longer Be Enough

The answer is simple: development doesn’t end with code generation alone.
In the AI era, what’s important in development—more than writing code—is understanding existing code, analyzing dependencies, verifying them, and safely incorporating changes.

According to OpenAI’s official release (May 16, 2025), Codex not only writes code but also answers questions about the codebase, fixes bugs, and proposes pull requests.
The official GitHub blog (May 19, 2025) also illustrates how the Copilot coding agent launches a virtual environment, analyzes the repository, and builds up commits into a draft PR.
Google’s official documentation (February 24, 2026) also states that Agent Mode performs tasks using multiple contexts, such as reading and writing files, terminal tools, MCP servers, URLs, and search results.
Anthropic’s official documentation also explicitly states that performance declines as context expands, and that without verification mechanisms, the system is prone to producing outputs that “seem correct but are actually broken.”

Turning this around, it means that Vibe Coding is effective only in limited situations.
While it excels at building small prototypes from scratch, it becomes less effective when the following conditions overlap:

• Modifying existing products
• Development requiring long-term context
• Development subject to team review
• Development involving security or permissions
• Development that will require maintenance later

The upside is that Vibe Coding isn’t inherently bad; if you know where to apply it, it will remain a powerful tool.
It offers significant value for brainstorming, prototyping, creating UI drafts, and writing scripts.

The downside is that carrying that success directly into production development increases the risk of errors.
In particular, proceeding without testing, specifications, or reviews will end up costing more to fix later.

It’s important to note that the experience of “the AI creating it in one go” does not guarantee reproducibility.
There’s no guarantee that the same quality will be achieved the next day with a different model, a different developer, or in a different context.

As an observation, in personal development, there are many situations where “if it works, it’s a win,” making Vibe Coding very appealing.
However, the longer a tool is used or the more it becomes a public service, the greater the emphasis ultimately becomes on design, verification, and operations.

Situations Where Vibe Coding Is Particularly Prone to Failure

• Large-scale modifications spanning multiple files
• Projects with strict naming conventions or design guidelines
• Projects with complex bug reproduction conditions
• Features involving permissions, billing, or personal information
• Code intended to be read by others later

The First Trend to Gain Traction Next: Specification-Driven Development with AI

The next major trend will be a “write the specifications first” culture.
However, this does not mean a return to the old-fashioned, cumbersome culture of detailed specification documents.
Instead, the focus will be on short but clear specifications designed to be fed to AI.

According to official Google information, Gemini Code Assist’s agent mode proposes a plan, and the process proceeds only after the user approves it.
Anthropic’s official documentation also clearly states, “Explore first, plan next, then code.”
On the official GitHub blog, assigning an issue to a team member is itself a way of formalizing the unit of work.

In other words, in future development, “creating task definitions that prevent the AI from getting confused” will be more important than “starting to write code.”

The benefit is that requests for revisions are less likely to veer off course.
Once “what,” “to what extent,” and “what constitutes completion” are determined, both the AI and humans can work more efficiently.

The downside is that it requires the ability to write clear specifications.
Precisely because we live in an era where code is generated even from ambiguous requests, those who can reduce ambiguity will have an advantage.

One point to note is that writing specifications that are too long can be counterproductive.
As noted in Anthropic’s official documentation regarding context bloat, more information isn’t necessarily better for AI.

Based on my observations, people who are skilled at using AI don’t necessarily write flashy prompts.
Rather, they’re good at keeping the target files, prohibited actions, completion criteria, and verification methods short and consistent.

How to Write Specifications That Are Easy for AI to Process

• State the objective in one sentence
• Specify the files or scope to be modified
• List what not to do
• Define the completion criteria
• Include verification commands and expected results

Examples of Poor Specifications

• “Just fix it so it looks good”
• “Optimize everything—both design and logic”
• “Improve it while maintaining the existing feel”
• “Eliminate bugs and speed it up”

Examples of Good Specifications

• “Fix only the validation on the login screen”
• Target only specific files
• Do not change UI text
• Specify the tests to be added
• Determine success criteria first

The Second Trend Set to Become Mainstream | Verification-Driven Development with AI

From now on, verification will take center stage over generation.
Anthropic’s official documentation explains that “giving Claude a way to verify its own work” offers the highest leverage.
This is because by providing tests, screenshots, and expected outputs, the AI can check its own work.
OpenAI’s official Agent evals documentation also recommends a process of measuring quality through reproducible evaluations and continuously improving.

This shift is extremely important.
Until now, the focus has tended to be on “how smart the AI is,” but going forward, “how to measure the AI’s results” will be the deciding factor.

The advantage is that quality can be assessed based on objective criteria rather than intuition.
This makes it easier to compare results using the same evaluation metrics, even if the person in charge or the model changes.

The downside is that it takes time to prepare evaluation data and tests.
In existing projects, in particular, weaknesses in the test infrastructure are likely to be exposed early on.

One point to watch out for is that if there are too few evaluation criteria, “optimization just to pass the evaluation” can occur.
Don’t be lulled into a false sense of security just because tests pass; it’s also necessary to check for operational inconsistencies and the user’s perspective.

As an observation, rather than asking the AI to make the same corrections over and over, it’s often faster in the long run to first establish conditions such as “it passes if this requirement is met.”
In the AI era, speed is determined not by generation speed, but by the minimal number of retries required.

Verification Criteria to Define First

• Do unit tests pass?
• Do type checks pass?
• Does Lint pass?
• ExistingAre any features broken?
• Are the UI changes as intended?

Practical Approaches

• Define acceptance criteria before generation
• Prioritize execution results over AI explanations
• Standardize the reproduction commands
• Don’t aim for perfection on the first try; assume an iterative validation process

The Third Trend Set to Become Mainstream: Review-Centric Development

As AI becomes more powerful, the human role will shift from “writer” to “approver.”
The official GitHub blog (May 19, 2025) explains that the output from the Copilot coding agent accumulates as draft pull requests and includes safeguards to prevent CI/CD from running automatically without human approval.
This is highly symbolic.
This is because the unit of development is shifting more clearly back to Issue → Work → PR → Review, rather than local editing on one’s own machine.

Even in OpenAI’s official Codex introduction, PR proposals take center stage.
In other words, in future development, it will be important not only to “how to get the AI to write code” but also to “how to evaluate the AI’s changes.”

The benefit is that a history of changes and the reasoning behind them is preserved.
This makes it easier to review later and facilitates accountability within the team.

The downside is that differences in review skills directly translate into differences in quality.
The more code the AI generates, the greater the burden on the reviewer.

It’s important to note that even if a PR’s description is impressive, the accuracy of the code itself is a separate matter.
Since AI is also skilled at writing explanations and summaries, it’s crucial not to be overly swayed by the persuasiveness of the text.

In my opinion, top developers in the future will be those who can spot dangerous changes the fastest, rather than those who can write code the fastest.

Points to Emphasize in Reviews Going Forward

• Does it meet the specifications?
• Has the scope of changes become too broad?
• Are the tests truly effective?
• Are there any security or permission vulnerabilities?
• Will future maintenance efforts increase?

Things to Avoid in Code Reviews in the AI Era

• Approving a pull request based solely on the description
• Feeling reassured just because tests passed
• Failing to verify the intent behind changes
• Overlooking unrelated changes

The Fourth Trend Set to Become Mainstream: Context-Connected Development

Even if AI is powerful on its own, its accuracy drops if it cannot connect to the necessary context.
Standardized connections, such as the MCP, are crucial in this regard.

In an official announcement by Anthropic (November 25, 2024), the Model Context Protocol (MCP) was introduced as an open standard for securely connecting AI assistants and data sources in both directions.
Google’s official documentation also explains that agent mode can utilize MCP servers.
The official GitHub blog also indicates that the Copilot coding agent can connect to external data and features via MCP.

This means that AI is evolving from a mere chat partner into an active contributor connected to internal information, codebases, design documents, issues, and execution environments.

The benefit is that AI can reduce instances of “pretending to know.”
Since it can retrieve the necessary information on its own, this makes it less likely to provide answers that ignore the codebase.

The drawback is that as the number of connection points increases, managing permissions and ensuring security becomes more difficult.
It is necessary to strike a balance between convenience and control.

It is important to note that simply being connected does not guarantee accuracy.
If it connects to outdated documentation, incorrect issues, or broken configurations, the AI will incorporate those errors as well.

As an observation, differences in AI accuracy are not solely due to model differences; they vary significantly depending on the quality of the connected context.
Going forward, “what to connect to” will be just as important as “which model to use.”

High-Value Sources to Connect To

• Repositories
• Issue tracking
• Design notes
• Test results
• Design materials
• Execution logs

Things to Check Before Connecting

• Is it read-only or writable?
• Does it contain any personal information?
• Are there any outdated documents mixed in?
• Are the permissions too broad?

The Fifth Trend Set to Become Mainstream: Parallel Agent Operation

OpenAI’s official Codex introduction highlights the ability to process multiple tasks in parallel.
Anthropic’s official engineering updates also continue to discuss topics related to parallel Claude operations and long-running agent operations.
What this reveals is a shift from a model where a single developer works on one task at a time to a model where multiple AI specialists work simultaneously.

For example, the following division of labor becomes natural:

• Agent 1: Research
• Agent 2: Implementation
• Agent 3: Additional Testing
• Agent 4: Document Updates

The benefit is reduced wait times.
AI can simultaneously handle processes that would be slow if performed sequentially by humans.

The drawback is that integrating the deliverables becomes more difficult.
Even if each part is correct on its own, combining them may cause issues.

One thing to note is that management costs increase as the number of parallel processes increases.
For small-scale projects, it may actually be faster to carefully entrust the work to a single AI.

In my experience, parallelization is most effective when tasks are broken down into “independent units of work.”
Conversely, for changes that are heavily interdependent, you need to design them to be decoupled before attempting parallelization.

Tasks Suitable for Parallelization

• Research tasks
• Adding test cases
• Document organization
• Identifying candidates for refactoring
• Individual fixes to UI components

Tasks Not Suitable for Parallelization

• Changes to complex state management
• Modifications involving database design changes
• Core logic for billing and authentication
• Large-scale architectural changes

So, Where Does Human Value Go?

This may be the point that concerns people the most.
rather than diminishing, the role of human value shifts.

The official OpenAI developer blog (December 30, 2025) summarizes 2025 as “the year it became easier to deploy agents in production.”
The official GitHub blog describes how Copilot is being integrated into existing workflows while maintaining human approval and security.
Anthropic’s official documentation also assumes human involvement, emphasizing early course correction, context management, and the establishment of verification criteria.

In other words, the human role will become more significant in the following areas:

• Problem definition
• Formulating specifications
• Prioritization
• Designing success criteria
• Review and final decision-making
• Handling exceptions
• Managing accountability

The benefit is that it’s easier to free yourself from routine tasks.
You can focus on higher-level decision-making.

The downside is that it becomes harder to differentiate yourself based solely on “what you can write.”
Going forward, skills in design, decision-making, and control will become more visible.

One important point to note is that even as more tasks are delegated to AI, responsibility cannot be handed over to it entirely.
In particular, humans will bear the ultimate responsibility for production system failures, personal information, and legal risks.

As an observation, as AI adoption progresses, people with strong, often unassuming skills will consistently deliver results.
These include the ability to refine requirements, anticipate how systems might fail, and spot inconsistencies during code reviews.

Skills That Will Be Highly Valued Going Forward

• The ability to write good issue reports
• The ability to anticipate failure scenarios before implementation
• The ability to assess the impact of changes
• The ability to quickly spot differences
• The ability to articulate ideas clearly to the team

How Will Personal Development Change?

In personal development, the appeal of “Vibe Coding” will remain for some time to come.
This is because speed and enjoyment are major assets.
However, if you plan to release your work or maintain it long-term, you’ll be better off moving to the next stage.

My recommendation is to adopt a “light” approach to specifications, testing, and reviews.
Heavy processes aren’t necessary, but simply establishing a minimal framework will reduce the number of failures.

The benefit is that it will save your future self a lot of trouble.
Code written by AI tends to become ambiguous when reviewed later on.

The downside is that the initial sense of blazing speed will decrease slightly.
However, since you’ll spend less time backtracking, you’ll likely be faster overall.

One thing to watch out for is not trying to solve everything just by choosing the right model.
Even high-performance models can’t fully compensate for vague instructions and insufficient verification.

Best Practices for Individual Developers to Adopt Right Away

• Write your objective in three lines before making changes
• List completion criteria as bullet points
• Have the AI generate a plan before implementation
• Run verification commands after implementation
• Review the diff before merging

How Will Team Development Change?

In a team setting, the success or failure of AI adoption depends more on operational frameworks than on individual skill.
This is why official GitHub documentation emphasizes issues, pull requests, approvals, and policies.
Documents from Google and Anthropic also assume that planning approvals, tool authorizations, and context design are in place.

The main focus of team work will shift as follows:

• Prioritizing the development of issue templates over sharing prompts
• Prioritizing the sharing of verification criteria over sharing know-how
• Prioritizing operational rules over individual skills
• Prioritizing the design of AI-based review processes over manual reviews

The benefit is that it makes it easier to reduce reliance on individual expertise.
You’ll no longer be overly dependent on a single person’s “AI expertise.”

The downside is that establishing rules requires significant effort during the initial implementation phase.
In particular, permissions, security, and review procedures need to be clearly defined.

One thing to watch out for is that allowing too much freedom in AI usage can actually undermine the team’s quality.
It’s more stable to decide in advance “what tasks can be entrusted to AI.”

Things the team should decide in advance

• Scope of tasks to be entrusted to AI
• Specification templates
• Mandatory tests
• PR review criteria
• Permission rules for external connections
• Approval workflow before deployment to production

What’s Likely to Happen in Development Environments Starting in 2026

The following outlook is based on an analysis of primary sources.
Please read this as speculation, not as definitive fact.

Based on official information from various companies, the following trends are likely to intensify in the future:

• Delegation based on issues and tasks will increase, rather than relying on IDE autocomplete
• Work requiring plan approval will increase, rather than requests made via chat
• Competition will shift from code generation to verification, evaluation, and operations
• Use of agents connected to multiple tools will increase, rather than standalone AI
• Differences in developer proficiency will be more evident in the quality of judgment than in implementation speed

On the other hand, we should remain cautious about whether fully autonomous development will become widespread anytime soon.
Even in Google’s official documentation, “agent mode” is still labeled as a preview, and all companies are emphasizing approval, authorization, review, and safety measures.
Based on currently available official materials, a future where AI handles everything completely without human involvement seems less realistic than a future where humans supervise while delegating substantial tasks to AI.

Expected Changes

• Less time spent writing code
• More time spent writing specifications
• Increased importance of testing and evaluation
• The quality of code reviews will become a key differentiator
• Designing contextual connections will become a new measure of skill

Types of Primary Sources Referenced

This article is based on the following primary sources available as of April 6, 2026.
We have prioritized official information as much as possible, rather than commentary or rumors from external media.

Main Sources Cited

• OpenAI official release “Introducing Codex” (May 16, 2025; updated June 3, 2025)
• OpenAI official developer blog “OpenAI for Developers in 2025” (December 30, 2025)
• OpenAI Official Documentation: “Agent evals”
• GitHub Official Blog: “GitHub Copilot: Meet the new coding agent” (May 19, 2025; updated May 23, 2025)
• Official GitHub Documentation: “Requests in GitHub Copilot”
• Official Google for Developers Documentation: “Agent mode overview” (as of February 24, 2026)
• Official Google Blog: “New in Gemini Code Assist: Agent Mode and IDE enhancements” (July 17, 2025)
• Anthropic Official Announcement: “Introducing the Model Context Protocol” (November 25, 2024)
• Anthropic Official Documentation: “Best Practices for Claude Code”

Summary | What’s Next Is Manageable AI Development, Beyond “Development by Instinct”

What comes after Vibe Coding isn’t just high-performance code generation.
It’s manageable AI development—where you write specifications, have the AI plan, verify, and finalize through review.
Even a glance at primary sources shows that companies are already steering in that direction.
What will be required of developers going forward is not just the ability to use AI comfortably, but also the ability to deploy it safely, with high reproducibility, and into production.
To start, try incorporating just these three practices starting today.

Three Things to Start Right Now

• Before asking AI to do something, write down the objective and completion criteria
• Always run tests and verification procedures after implementation
• Review changes before adopting them

Precisely because we’re in an era where simply “having AI create something that feels right” isn’t enough, these somewhat mundane habits of specification, verification, and review become your newest and most practical tools.

Primary source checked

• blog.google