AI Coding Agents vs Vibe Coding: What Is the Difference and What Can You Delegate?

AI tools for software development have changed quickly.

At first, a developer asked a chatbot a question, received a code snippet, and manually copied it into a project. Later, editors gained the ability to modify selected files. Modern coding agents can now inspect repositories, create plans, run commands, observe errors, and continue working within the permissions they receive.

As a result, terms such as vibe coding, AI development, and agentic development are often used as though they mean the same thing.

They do not.

Vibe coding describes how a person communicates desired outcomes to AI through natural language and iterative feedback.

An AI coding agent describes a technical system that receives instructions together with tools for performing actions.

The same developer may use both approaches at the same time.

What is vibe coding?

Vibe coding is a workflow in which a person describes the desired product behavior in natural language and uses AI to create or modify the implementation.

For example:

Add order history to the customer dashboard, include a status filter, and allow customers to repeat a previous order.

The person may then:

• clarify the interface;

• change the behavior;

• provide an error;

• request mobile support;

• add access control;

• revise the output.

The main characteristic is that the work is directed through goals and expected outcomes rather than manual control over every line.

Vibe coding does not define how independently the AI acts.

A developer may use:

• a regular chat;

• an AI editor;

• a terminal tool;

• a local coding agent;

• a cloud agent;

• several agents in parallel.

For a broader introduction, read “Who Are Vibe Coders?”.

What is an AI coding agent?

An AI coding agent receives access to tools and can perform a sequence of actions to complete a development objective.

Depending on the product and permissions, an agent may:

• read files;

• search the codebase;

• inspect dependencies;

• edit several files;

• create files;

• run commands;

• install dependencies;

• execute tests;

• run builds;

• inspect logs;

• perform Git operations;

• prepare a diff;

• create a pull request;

• return a work report.

Modern coding agents may work locally in a terminal or editor and may also execute delegated tasks in cloud environments. Codex supports local workflows, cloud background tasks, parallel agents, and worktrees. Claude Code supports project file access, command execution, tool restrictions, and permission modes.

The main difference from a normal chat is that the agent can perform an action, observe the result, and continue.

AI coding agents and vibe coding compared

The approaches are not competitors.

A vibe coder may describe a goal in natural language, delegate implementation to an agent, and review the result.

How is an agent different from code generation?

A normal model may receive this request:

Write a function that prevents duplicate order creation.

It may produce useful code, but it may not know:

• which file contains the order logic;

• how the database is structured;

• which tests already exist;

• which endpoints call the function;

• which project conventions must be preserved.

The developer needs to collect the context and apply the answer.

An agent may instead:

1. Locate the order handler.

2. Inspect the data model.

3. Review existing constraints.

4. Find related tests.

5. Prepare a plan.

6. modify the backend logic.

7. Add a repeated-request test.

8. Run the tests.

9. Observe a failure.

10. Revise the implementation.

11. Present the final diff.

OpenAI describes this interaction between user instructions, the model, and executable tools as an agent loop.

How does the agent loop work?

A simplified loop looks like this:

goal → research → plan → action → observation → revision → verification

Goal

The person describes the required outcome.

Research

The agent inspects the repository and identifies related logic.

Plan

The agent determines the expected changes and checks.

Action

It edits files or runs commands.

Observation

The tool returns a result:

• the test passed;

• the build failed;

• the file was not found;

• types do not match;

• the command returned an error.

Revision

The agent adds this information to the context and continues.

Verification

It runs the agreed checks and returns the result for human review.

One user message may produce many internal actions. Clear boundaries and stopping criteria therefore matter.

Levels of AI autonomy

Autonomy is not a simple on or off setting.

Level 1. Suggestion

AI explains a solution or generates a code fragment.

The person:

• selects the file;

• applies the change;

• runs commands;

• fixes errors.

Level 2. File editing

AI can modify selected files, while the person confirms important actions.

This works well for:

• small refactors;

• type corrections;

• copy changes;

• component updates.

Level 3. Agentic task

The agent receives a goal and investigates the relevant part of the repository.

It may:

• modify several files;

• execute tests;

• fix related failures;

• prepare a report.

The person accepts or rejects the result.

Level 4. Background task

The work is performed separately from the developer's active session.

The agent may return:

• a branch;

• a diff;

• a pull request;

• a report;

• a list of risks.

Codex cloud tasks, for example, run in separate isolated environments containing the repository before returning changes for review.

Level 5. Multiple agents

Several agents work on separate tasks in parallel.

For example:

• one agent modifies the backend;

• one adds tests;

• one updates documentation;

• one reviews the output.

This may accelerate independent work, but it requires careful decomposition and integration.

Tasks that suit AI coding agents

Agents perform best when the scope is limited and the result is verifiable.

Repository exploration

Examples:

• find where roles are checked;

• explain the payment flow;

• map module dependencies;

• locate the cause of a repeated request;

• list application routes.

An agent may explore an unfamiliar repository faster than a developer opening it for the first time.

Fixing a limited defect

Strong task:

Prevent duplicate request creation after a double click. Do not modify the database structure or public API. Add a test for two identical requests.

Weak task:

Fix everything that is wrong.

Repetitive refactoring

Examples:

• replace a deprecated component;

• update imports;

• change logging format;

• extract a repeated validation;

• add the same field to several forms.

Test preparation

An agent may:

• inspect the existing style;

• add unit tests;

• prepare an integration scenario;

• create a regression test;

• run the test suite.

The tests still require review. An agent may create a test that confirms its own incorrect assumption.

Build and type failures

Suitable tasks include:

• TypeScript failures;

• invalid imports;

• deprecated APIs;

• dependency conflicts;

• broken production builds;

• schema and type mismatches.

Documentation

An agent may:

• update README files;

• document environment variables;

• prepare setup instructions;

• synchronize documentation with code;

• describe an API.

Pull request preparation

It may help:

• summarize changes;

• explain checks;

• list risks;

• prepare a review description.

Tasks that require stronger human control

Some tasks can be delegated only as preparatory work.

Payments

Defects may cause:

• duplicate charges;

• incorrect order states;

• repeated product delivery;

• broken refunds;

• financial inconsistencies.

Server confirmation, duplicate events, and idempotency need separate verification.

Authentication

Dangerous failures include:

• access to another account;

• incorrect role checks;

• sessions that never expire;

• user impersonation;

• unsafe account recovery.

Access control

A passing interface test does not prove that a regular user cannot call an administrative endpoint directly.

Server-side permissions must be verified.

Production database migrations

An agent may prepare a migration, but it should not automatically run against production.

Review:

• data loss;

• table locking;

• rollback;

• execution time;

• compatibility with the previous application version;

• backup availability.

Data deletion

Bulk deletion, account removal, and record transformation require confirmation and recovery planning.

Infrastructure and deployment

An agent should not freely:

• modify a firewall;

• restart production;

• remove containers;

• change DNS;

• expose secrets;

• disable backups.

Smart contracts and financial logic

These changes require specialized review, edge-case testing, and independent verification.

Why passing tests do not guarantee correct behavior

Consider this task:

Charge the platform fee after the transaction is completed.

The agent may implement the fee and create a test for the implementation.

The tests pass.

However, the real business rules may be:

• the fee applies only to protected transactions;

• direct transactions have no platform fee;

• cancelled transactions should not be charged;

• a repeated webhook must not create another charge.

When the context is incomplete, an agent may implement the wrong business model correctly.

Two levels of review are required:

1. Technical correctness.

2. Business-rule correctness.

How to write a strong task for an agent

The more autonomy the agent receives, the clearer the boundaries should be.

Goal

What must change after the task?

Weak:

Improve authentication.

Stronger:

After a user's role changes, an old token must not preserve administrative access.

Context

Explain where the relevant logic is and how it currently works.

The role is stored in the JWT at sign-in. Server actions trust the token value.

Problem

Describe reproducible incorrect behavior.

When an administrator is downgraded, the existing JWT still grants access to administrative actions.

Scope

State which areas may be modified.

You may change server-side role validation and tests. Do not change the token format or client navigation.

Prohibited changes

Record important restrictions.

Do not disable authentication, add bypass flags, or modify roles in the database.

Acceptance criteria

Describe an objective result.

After a role change, the next server request using the old token returns 403.

Checks

List required commands and scenarios.

Run authentication tests, type checking, and the production build.

Output format

State what the agent should return.

Show the plan first. After implementation, list modified files, test results, and remaining risks.

Copyable agent task template

Goal

Which result is required?

Context

Where is the relevant logic and how does it currently work?

Problem

How can the incorrect behavior be reproduced?

Scope

Which modules and files may be changed?

Restrictions

Which parts of the system must not change?

Acceptance criteria

How will the result be verified?

Checks

Which tests, builds, and user scenarios must be executed?

Risks

Which data, payments, or permissions may be affected?

Output

Should the agent return a plan, diff, report, change list, and verification instructions?

Restricting agent access

AI agents should receive the minimum access required for the task.

Modern tools provide permission controls. Claude Code can allow or deny specific tools, limit agentic turns, and start in a selected permission mode. Codex uses sandboxing and approval mechanisms to control actions.

Use Git

Before the task:

• create a branch;

• confirm that the working directory is clean;

• save current changes;

• prepare a rollback path.

Use an isolated environment

Options include:

• sandbox;

• container;

• separate worktree;

• temporary database;

• test project;

• isolated cloud environment.

Do not provide unnecessary secrets

An agent may not need:

• production API keys;

• administrator passwords;

• wallet seed phrases;

• payment credentials;

• real user data;

• unrestricted SSH access.

Use test values and limited accounts.

Restrict dangerous commands

Require approval for:

• deleting files;

• infrastructure changes;

• migrations;

• deployment;

• package publication;

• message delivery;

• financial operations.

Limit network access

When a task does not require external internet access, unrestricted networking may be unnecessary.

Keep production separate

Production deployment should remain a separate reviewed step.

Reviewing an agent's result

1. Read the explanation

Confirm that the agent understood the objective and did not change it during implementation.

2. Review modified files

Unexpected changes require investigation.

A copy change should not alter the database schema.

3. Inspect the diff

Look for:

• removed validation;

• weakened types;

• disabled error handling;

• new dependencies;

• public API changes;

• secret handling;

• authorization bypasses;

• temporary workarounds.

4. Run linting and type checks

These catch part of the structural problems.

5. Run tests

Review:

• existing tests;

• new tests;

• edge cases;

• repeated requests;

• incorrect permissions;

• invalid inputs.

6. Create a production build

Development mode may hide build-time problems.

7. Complete user flows

Review the product as a user rather than only as a developer.

8. Test negative scenarios

What happens when:

• permission is missing;

• a request is repeated;

• the network fails;

• data is invalid;

• an external service is unavailable;

• the user clicks twice?

9. Inspect logs

The system should not hide important failures or expose secrets.

10. Confirm rollback

Before merging, understand how to return to the previous state.

Why multiple agents may not be faster

Parallel agents are useful for independent tasks.

Strong example:

• agent 1 updates documentation;

• agent 2 adds tests for an isolated module;

• agent 3 fixes an unrelated interface.

Weak example:

• three agents modify authentication;

• two agents create different migrations for one table;

• several agents edit the same component;

• one agent changes an API without informing the others.

Possible problems include:

• conflicts;

• duplicated work;

• incompatible architecture;

• inconsistent dependencies;

• complex integration.

A multi-agent workflow requires:

1. Task decomposition.

2. Ownership for each module.

3. Shared contracts.

4. Separate branches or worktrees.

5. Integration review.

6. A complete test suite.

What clients should know

Clients do not need to understand every coding agent.

They should ask the contractor:

1. Who owns the architecture?

2. Which tasks are delegated to AI?

3. How are changes reviewed?

4. Who reads the diff?

5. Which tests are run?

6. How is security reviewed?

7. Where does the agent execute?

8. Does it receive production access?

9. Who owns the repository?

10. Who fixes post-launch defects?

A weak question is:

Did you write the code yourself or use AI?

A more useful question is:

Can you explain how the system works, show the verification process, and take responsibility for the result?

What vibe coders should know

A coding agent should not turn the developer into a messenger between an error and a model.

Weak process:

1. The agent creates an error.

2. The developer copies it back.

3. The agent suggests a random fix.

4. The cycle continues until the message disappears.

5. Nobody reviews adjacent behavior.

Strong process:

1. Reproduce the problem.

2. Identify the likely cause.

3. Restrict the change scope.

4. Request a plan.

5. Review the diff.

6. Run tests.

7. Complete the user flow.

8. Record the result in Git.

9. Prepare rollback.

The ability to work with agents is measured by task control rather than prompt volume.

Does AI reduce development cost?

AI may accelerate:

• code discovery;

• repetitive edits;

• test preparation;

• documentation;

• simple bug fixes;

• repository exploration.

Project cost still depends on:

• business complexity;

• feature count;

• integrations;

• security;

• testing;

• publishing;

• maintenance;

• contractor responsibility.

Clients do not pay for the number of manually typed lines.

They pay for a working, verified, and maintainable result.

Agentic development therefore does not guarantee that every complex product becomes inexpensive.

Evaluating AI developers on VibeMarket

VibeMarket clients can review:

• specialization;

• working products;

• personal contribution;

• screenshots;

• videos;

• available verification;

• reviews;

• work process.

A project request should include:

• the goal;

• business rules;

• permitted technologies;

• acceptance criteria;

• testing requirements;

• repository handover;

• access restrictions.

A contractor may use AI agents, but responsibility for implementation remains with the contractor.

Post a project on VibeMarket

Developers can create a VibeMarket profile and present real products, personal contribution, and review practices rather than only listing AI tools.

Final thoughts

Vibe coding and agentic development describe different parts of one workflow.

Vibe coding answers:

How does a person communicate the desired result to AI?

Agentic development answers:

Which actions may AI perform in the development environment?

An AI coding agent can inspect a repository, edit files, run commands, observe failures, and continue working.

It does not accept responsibility for the product.

A person must still:

• define the goal;

• explain the business rules;

• restrict access;

• review the plan;

• inspect the diff;

• run checks;

• accept the result;

• take responsibility for the consequences.

The more autonomy an agent receives, the more important engineering control becomes.