What Is Agentic Coding? A Practitioner's Guide to AI Coding Agents

Agentic coding is when you give an AI agent a goal, and it runs a loop until the job is done or it gets stuck. The agent reads your repo, edits files, runs commands, checks test output, and tries again. You supervise. You do not type every line.

That is the short answer. The useful answer is messier — because the term gets thrown at autocomplete, chat windows, and full autonomous runs alike.

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What Is Agentic Coding?

Agentic coding is a development style where an AI coding agent takes a goal and executes a multi-step plan against a real codebase.

The agent can:

• read and search files
• edit across multiple paths
• run shell commands and tests
• read failures and retry

You stay at the review layer. You set boundaries. You merge when the change is right.

IBM and Google both describe the same core idea: move from "chat with AI about code" to "assign work to AI and validate the result." The Google Cloud definition calls it a shift from passive consultant to skilled contractor. That framing is close to how it feels in practice.

The loop that makes it agentic

A chat assistant answers once. An agent iterates.

A typical loop looks like this:

Goal from you
     ↓
Agent plans sub-tasks
     ↓
Agent edits files / runs tools
     ↓
Tests or build fail?
     ↓ yes → read output → adjust → retry
     ↓ no
Report back for your review

Researchers call this a reason-and-act pattern. The model reasons, acts, observes feedback, and reasons again. Aidan Li's agentic coding guide puts it well: the agent is not just an LLM. It is a model plus a harness — tools, context, retry logic, and stop conditions.

Without the loop, you do not have agentic coding. You have a fast typist.

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Agentic Coding vs AI-Assisted Coding vs Vibe Coding

These terms overlap in marketing copy. In daily work they mean different levels of autonomy.

AI-assisted coding is Copilot-style help. The AI predicts the next chunk. You still own every keystroke that lands.

Vibe coding is interactive. You and the model go back and forth in one session. You are in the inner loop.

Agentic coding is delegation. The agent can work across files and commands while you do something else — then you review a finished diff.

I wrote a longer comparison in Vibe Coding vs Agentic Engineering. That post focuses on control and responsibility. This one focuses on the agent loop itself.

Quick rule from practitioners like Tembo's 2026 guide: if you read output token by token, it is assisted. If you review turn by turn in chat, it is vibe-style. If the agent runs while you are away, it is agentic.

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Four Levels of Coding Autonomy

Autonomy is a dial, not a switch. This scale survives every tool rename.

Level 1 — Autocomplete. Single-line or block suggestions. No plan. No tools.

Level 2 — Assisted edit. Multi-line edits in one file. You approve each change.

Level 3 — Supervised agent. The agent uses tools, runs tests, and iterates. You watch or check in often.

Level 4 — Delegated agent. You hand off a scoped task. The agent works toward a pull request with minimal back-and-forth.

Most production work I do sits at Level 3. Level 4 is powerful for bounded tasks — migrations, test backfill, repetitive refactors. It is risky for architecture you have not specified.

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What a Coding Agent Actually Does

Strip the hype and a coding agent does five jobs.

1. Context gathering

The agent searches the repo, reads relevant files, and sometimes pulls docs or tickets. Good agents isolate noisy output — search logs in a subagent, summaries back to the parent.

2. Planning

It breaks your goal into steps: which files to touch, what order, what to verify. Weak plans cause expensive thrash. Strong plans look boring.

3. Tool use

Real agents run grep, edit files, install packages, execute test suites, and sometimes open a browser. Cursor's subagents split explore, bash, and browser work so the main thread stays focused.

4. Self-correction

Failed tests and build errors become feedback. The agent reads stderr and tries again. This is the feature that separates agents from one-shot chat.

5. Handoff

You get a diff, a summary, or a PR. Your job starts here — not before.

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When Agentic Coding Works Well

Agentic coding earns its keep on tasks with clear success criteria.

Strong fits:

• Test-driven changes — pass/fail is an objective signal
• Refactors with a fixed target — rename a module, migrate an API shape
• Boilerplate and scaffolding — CRUD endpoints, form wiring, config files
• Bug fixes with a repro — failing test or logged stack trace
• Documentation passes — sync docs to code the agent can read

I use agents heavily when the codebase already has tests and lint rules. The loop needs a signal. Without one, the agent guesses — and guessing at scale is how you get "AI slop."

Weak fits:

• Greenfield architecture — no constraints yet, many valid wrong answers
• Novel domain logic — business rules live in people's heads, not files
• Security-sensitive paths — auth, payments, PII without hard guardrails
• Untested legacy — the agent cannot tell correct from plausible

For those, stay at Level 2 or 3 with a tight scope. Or pair agentic runs with the engineer-led workflow in agentic engineering.

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A Practical Agentic Coding Workflow

Here is the workflow I use on real repos. It is closer to engineering management than to traditional typing.

Write a one-paragraph goal (outcome, not implementation)
        ↓
List constraints (files, patterns, commands, "do not touch")
        ↓
Let the agent plan — challenge the plan before code
        ↓
Agent implements in a branch or worktree
        ↓
Agent runs tests / lint / typecheck
        ↓
You review diff + reasoning trace
        ↓
You run the app and check edge cases
        ↓
Merge or send back with specific fixes

Scope beats clever prompts

"Vague ambition" is the fastest way to waste agent time.

Bad: "Make auth better."

Better: "Add refresh token rotation to the existing JWT flow in src/auth/. Reuse the session store. All tests in auth.test.ts must pass. Do not change the public API shape."

The second prompt gives the agent a definition of done.

Permissions are a feature

Agents with full shell access can delete data, leak secrets, or install bad packages. Treat permissions like production access.

Practical guardrails:

• run in a sandbox or isolated worktree
• deny production credentials in the agent environment
• require human review before merge
• log what the agent ran

Enterprise teams call this governance. Solo developers should still do the same things — just without a dashboard.

Verification debt

Agents can produce code faster than you can review it. That gap has a name: verification debt.

If you skip review because the tests passed, you inherit bugs, style drift, and architecture you do not understand. Code review is becoming a taste problem — agents make that shift urgent, not optional.

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Tools That Support Agentic Coding (2026)

The harness matters as much as the model. A few categories:

IDE-native agents — Cursor Agent and Composer, with subagents, parallel worktrees, and repo-wide edits. I use this daily.

Terminal agents — Claude Code, OpenAI Codex, Google Gemini CLI. Strong when the loop lives in shell and git.

Cloud / async agents — tasks that run on a VM and open a PR while you work on something else. Best for well-scoped, well-tested work.

Platform suites — GitHub Copilot coding agent, GitLab Duo, enterprise bundles with policy and audit trails.

No tool removes the need for judgment. Pick the harness that matches where you already work — editor vs terminal vs CI — and keep the review step non-negotiable.

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Common Mistakes (and How to Avoid Them)

Mistake 1 — Confusing speed with understanding. The agent finished in ten minutes. You cannot explain the change in a standup. That is a knowledge gap, not a win.

Fix: Ask the agent for a short design note before implementation. Read it. If it is nonsense, stop.

Mistake 2 — No tests, no loop. Without pass/fail signals, self-correction is theater.

Fix: Add tests first — even have the agent write them for existing code.

Mistake 3 — Mega-prompts. One message that asks for auth, billing, admin UI, and deploy.

Fix: Split into tasks that fit one review session each.

Mistake 4 — Skipping the plan review. The agent starts coding on a bad decomposition.

Fix: Approve or edit the plan explicitly. "Proceed" should mean you agree with the approach.

Mistake 5 — Treating agent output as trusted. Tests green does not mean correct for your product.

Fix: Review for architecture, security, and fit — not just syntax.

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Agentic Coding and the Future of Software Engineering

Agentic coding does not remove developers. It moves the center of gravity.

Less time on boilerplate and file churn. More time on goals, constraints, system design, and review. The job starts to look like leading a junior engineer who never sleeps — fast, eager, and wrong in subtle ways unless you supervise well.

That is why I pair agentic coding with agentic engineering: you own decisions; the agent owns execution speed. The combination is how I ship solo without pretending I read every generated line on day one.

If you are exploring AI in your career, the skills that compound are judgment, testing discipline, and clear communication of intent — not faster typing.

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Quick Reference: Is This Task Agentic-Coding Ready?

If you hit "no" on most rows, do not delegate end-to-end. Use the agent for search, drafts, or a single file — not a full feature.

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Where to Go Next

• Vibe Coding vs Agentic Engineering — control, responsibility, and the 90% problem
• Code Review Is Becoming a Taste Problem — what to review when agents write the diff
• How to Transition from Software Development to AI Engineering — skills that matter on the other side of this shift

Agentic coding is not magic. It is a loop — plan, act, verify, repeat — with you at the approval gate. Used with scope and tests, it is the most practical step up from chat-based coding I have found. Used without guardrails, it is a fast way to ship code you do not own.

The difference is not the model. It is whether you treat the agent as an author or as a contractor you still sign off on.