What Is Agentic Coding? Agentic vs Vibe Coding

Short answer: agentic coding is a way of building software where an AI agent is given a goal and works toward it in a loop: it plans, reads the codebase, edits files, runs commands such as tests or builds, checks the results and keeps going until the task is done. The developer sets the goal and the guardrails and reviews the result. It differs from vibe coding, which is about not reviewing the generated code, and from classic AI-assisted coding, where the AI only suggests and the human performs every action.

The three terms are often used interchangeably, which causes confusion. This guide separates them, shows what an agentic loop looks like in practice, and explains how to adopt it safely.

Agentic vs vibe coding vs AI-assisted coding

Aspect	AI-assisted coding	Vibe coding	Agentic coding
Who acts	Human; AI suggests	AI generates, human accepts	AI acts within permissions
Typical tools	Autocomplete, chat	Any generator, often prompt-to-app	Coding agents in terminal or IDE
Scope per step	A line or function	Whole features, loosely specified	Multi-file tasks with verification
Runs commands?	No	Sometimes	Yes: tests, builds, linters
Code review	Implicit (human types it)	Minimal by definition	Expected before merge
Best for	Fast typing, explanations	Prototypes, throwaway projects	Real features, refactors, bug fixes

AI-assisted coding

This is the familiar pattern: inline completions, a chat panel that explains an error, or a generated snippet you paste in. The model never touches your files or terminal on its own. You stay the operator for every action.

Vibe coding

Andrej Karpathy introduced the phrase in a post on 2 February 2025, describing a style where you “fully give in to the vibes” and “forget that the code even exists”. Simon Willison later argued that not all AI-assisted programming is vibe coding: in his definition, vibe coding means building software with an LLM without reviewing the code it writes. That can be great for prototypes and low-stakes experiments, and risky for production systems.

Agentic coding

Agentic coding describes how the AI works, not how carefully the human reviews. The agent has tools, such as reading and writing files, searching, running shell commands and fetching documentation, and it chains them together to reach a goal. You can do agentic coding with very careful review, or carelessly. The best results come from the former.

How an agentic coding loop works

Most coding agents follow a similar cycle:

1. Understand the goal. The prompt, plus project rules (for example AGENTS.md) and any linked spec.
2. Explore. Search and read relevant files, check types through a language server, look at tests.
3. Plan. Break the task into steps, often tracked as a to-do list.
4. Act. Edit or create files, apply patches across several modules.
5. Verify. Run the test suite, type checker, linter or build.
6. Iterate. Read failures, fix, and run again.
7. Report. Summarise what changed so a human can review the diff.

The verification step is what separates a capable agent from a code generator. An agent that runs npm test, reads the stack trace and fixes the failing case is doing real engineering work. One that only writes code is guessing.

A practical example

Say you want to add rate limiting to an Express API. In Assumer, a session might look like this:

npm i -g @4born-solutions/assumer
assumer login
cd my-api
assumer

First, press Tab to switch to the read-only plan agent and ask:

Add per-IP rate limiting to all /api routes (100 requests per 15 minutes).
Look at how middleware is registered and how tests are structured, then
propose a plan. Do not edit anything yet.

The plan agent reads src/app.ts, finds the middleware chain, checks the test setup and proposes a plan: add a middleware module, register it before the router, add a config value, and write tests for the limit and the reset window.

Press Tab again to switch to the build agent and say “Implement the plan.” The agent writes the middleware, edits the app entry point, adds tests, runs the test command, fixes any failure, and shows you the diff. If you do not like a change, /undo reverts it.

For CI or scripts, the same work can run non-interactively:

assumer run "Run the test suite and fix any failing tests in src/limits/"

Why agentic coding matters

• Larger units of work. You delegate a task, not a line.
• Built-in verification. Agents run the checks you would run anyway.
• Context awareness. Rules files, language servers and MCP servers give the agent the same context a teammate would have.
• Parallelism. Subagents can research while the main session keeps a clean context.

We deliberately avoid quoting productivity percentages here. Published numbers vary widely by task, codebase and team, and the honest answer is that results depend on how well you scope tasks and set guardrails.

Guardrails: making agentic coding safe

Plan before you build

Use a read-only mode first. In Assumer and OpenCode this is the plan agent; Cline calls it Plan mode. Agree on the approach before any file changes.

Set permissions per tool

Configure each tool as allow, ask or deny. A common setup is to allow reads and searches, ask before shell commands and edits, and deny anything destructive.

Keep everything in version control

Work on a branch. Review the diff. Agents make mistakes, and git makes them cheap.

Make tests the definition of done

Tell the agent, through your rules file, which commands must pass before a task is complete:

## Definition of done
- `npm run typecheck` passes
- `npm test` passes
- No new lint warnings from `npm run lint`

Review like it is a pull request

Treat agent output like a pull request from a capable but new teammate. Read it, question it and test it.

Spec-driven development: agentic coding with a contract

A growing practice is to write a short specification first, covering behaviour, constraints and acceptance criteria, and have the agent implement against it. The spec becomes the contract that both the human and the agent check the work against. Assumer supports spec-driven development directly, and assumer meeting can turn a meeting transcript into a development plan, so decisions made in a call become a structured starting point instead of scattered notes.

Tools for agentic coding

• Terminal agents: Assumer, OpenCode, Claude Code, Codex CLI, Gemini CLI, Aider.
• Editor-based agents: Cursor, Cline, Kilo Code, Google Antigravity.

All of them share the same core idea: give the model tools, a loop and context, and keep a human in charge of direction and review.

FAQ

What is agentic coding in simple terms?

Agentic coding is when an AI agent takes a goal, plans the steps, reads and edits files, runs commands such as tests, checks the results and repeats until the task is done, while a developer sets the goal, the guardrails and reviews the outcome.

Is agentic coding the same as vibe coding?

No. Vibe coding, a term popularised by Andrej Karpathy in February 2025, means accepting AI-generated code without really reading it. Agentic coding describes how the AI works, autonomously and in a loop, and is compatible with careful human review.

Is AI-assisted coding different from agentic coding?

Yes. Classic AI-assisted coding means autocomplete and chat answers where the human performs every action. In agentic coding the AI performs the actions itself, such as editing many files and running the test suite, within permissions you set.

Is agentic coding safe for production code?

It can be, if you use guardrails: a read-only planning step, per-tool permissions, version control, tests the agent must pass, and human code review before merging. Treat the agent’s output like a pull request from a new teammate.

Which tools support agentic coding?

Terminal agents such as Assumer, OpenCode, Claude Code, Codex CLI, Gemini CLI and Aider, and editor-based agents such as Cursor, Cline, Kilo Code and Google Antigravity.