What Is Agentic Coding? How AI Coding Agents Work

Agentic coding is the use of AI systems that can plan a coding task, write the code, run it, test it, and fix their own mistakes across multiple steps with little or no human input along the way — as opposed to tools that only autocomplete a line or respond to a single chat prompt. The term describes a category of coding tools, not one product: Anthropic's Claude Code, GitHub Copilot's coding agent, and Google's Gemini Code Assist agent mode are all built around this same loop of planning, acting, and checking the result.

The phrase has been showing up more in searches than in headlines, which is usually a sign that people have run into the tools before anyone explained them. This guide breaks down what agentic coding actually means, how it differs from "vibe coding" and plain autocomplete, what happens inside an agent's planning-and-testing loop (sometimes called a "harness"), how the industry measures whether these agents are any good, and what to watch for before you let one touch a real codebase.

What is agentic coding?

Agentic coding refers to AI coding tools built as agents: systems that take a goal — "fix this bug," "add this feature," "migrate this module" — and then work toward it over many steps on their own, deciding which files to read, which commands to run, and when the job is actually done. A 2025 paper from Cornell and Washington State University researchers, published on arXiv, frames the distinction cleanly: agentic coding systems are "goal-driven agents capable of planning, executing, testing, and iterating tasks with minimal human intervention," while the more conversational "vibe coding" approach keeps a human in the loop at every step through prompt-based, back-and-forth sessions.

Anthropic's own documentation uses the term the same way. It describes its command-line tool Claude Code as "an agentic coding tool that reads your codebase, edits files, runs commands, and integrates with your development tools" — a definition built entirely around autonomous action, not suggestion.

Agentic coding vs. vibe coding vs. autocomplete

These three terms get used loosely, but they describe genuinely different levels of autonomy. Our explainer on vibe coding covers that middle category in depth; the table below lines all three up side by side.

The arXiv paper cited above puts it plainly: vibe coding "emphasizes intuitive, human-in-the-loop interaction," suited to early-stage prototyping and education, while agentic systems are built to "excel in enterprise-grade automation." Neither approach replaces the other — they sit at different points on the same spectrum of how much the AI is trusted to act without asking first.

How agentic coding tools actually work

Strip away the branding and most agentic coding tools run the same basic loop:

1. Explore. The agent reads the relevant parts of the codebase, the issue description, or the failing test to build context before touching anything.
2. Plan. For anything beyond a trivial change, the agent proposes a sequence of steps — sometimes shown to a human for approval first.
3. Act, with tools. The agent calls tools: editing files, running shell commands, executing builds, calling linters, or querying external systems through standards like the Model Context Protocol.
4. Verify. It runs the project's tests or checks, and inspects the output.
5. Iterate. If something fails, it goes back to planning or acting, repeating until the check passes or it hits a limit.

Google's documentation for Gemini Code Assist's agent mode describes this as a cycle rather than a straight line: the model's response "might be a direct answer or a request to use an available tool," and tool results feed straight back into the model, so "this cycle of action and evaluation continues until the task is complete." For bigger jobs, Gemini "might show a high-level plan for your approval" before it starts working through the steps one at a time.

GitHub describes its Copilot coding agent in similar terms: it works "autonomously in a GitHub Actions-powered environment to complete development tasks assigned through GitHub issues or GitHub Copilot Chat prompts, and creates pull requests with the results." According to GitHub's documentation, the agent can research a repository, plan changes, implement them in the background, and run tests and linters in that cloud environment before a human ever reviews a diff.

The "harness": what's actually running the loop

If you've searched "agentic coding harness," this is what that term refers to: the scaffolding around the model that decides what tools it can call, how it manages context, when it stops, and how subtasks get divided up. The model generates text; the harness is the engineering that turns that text into file edits, shell commands, and test runs — and decides what the model gets to see next.

Anthropic's engineering blog describes this directly in the context of Claude Code: the tool ships with a default harness "built for coding," and a newer "dynamic workflows" feature lets Claude "write its own harness on the fly, custom-built for the task at hand," spawning and coordinating multiple subagents that each work in their own context window. The post lists patterns the harness can assemble on demand — generate-and-filter, loop-until-done, and adversarial verification among them — and notes that this flexibility costs more tokens, so it's best saved for complex, high-value tasks rather than routine edits.

Context management is one of the harness's biggest jobs. Anthropic's own best-practices documentation for Claude Code is built around a single constraint: the model's context window fills up, and "performance degrades as it fills." That's why these tools separate exploration from execution, summarize or compact long sessions automatically, and let an agent delegate side work to subagents rather than cramming everything into one conversation.

A few real agentic coding tools

None of this is theoretical — several vendors now ship agent-based coding products, each with a slightly different take on autonomy and where the work happens:

• Claude Code (Anthropic) — a terminal, IDE, desktop, and browser tool that reads a repository, edits files, runs commands, and can hand off to parallel subagents. See our explainer on Claude Code for how it's set up and priced.
• GitHub Copilot coding agent — assigns an issue or chat prompt to an agent that works in an isolated GitHub Actions environment, then opens a pull request for review, according to GitHub's documentation.
• Gemini Code Assist agent mode (Google) — runs inside VS Code and IntelliJ, looping between the Gemini API and a set of built-in and MCP-based tools, with write actions gated behind user approval by default.

All three are instances of the same broader pattern — a goal, a set of callable tools, and a loop of acting and checking — rather than three unrelated products.

How agentic coding is measured: benchmarks

"Agentic coding benchmark" is one of the more specific searches tied to this topic, and it usually leads to SWE-bench, the most widely cited test of whether an AI system can resolve real software issues rather than toy problems. According to its creators, SWE-bench "tests AI systems' ability to solve GitHub issues," drawing 2,294 task instances from pull requests and issues across 12 popular Python repositories. Each task comes with "Fail-to-Pass" tests: checks that fail before the fix and pass once a correct patch is applied inside a Docker container, which is how a submission is scored.

The benchmark's own history illustrates the shift the term "agentic coding" is pointing at: when SWE-bench was released in 2023, a retrieval-augmented baseline resolved just 1.96% of issues. Its follow-up, SWE-agent — described by its creators as the first agent-based AI system built for software engineering tasks — pushed that to 12.47% by giving the model an interactive environment to explore and test its own patches, rather than generating a fix in one shot. Later variants, including a human-filtered 500-task set called SWE-bench Verified, exist because models eventually started climbing high enough on the original set that it became harder to tell top systems apart.

What to watch out for: review, guardrails, and cost

Letting an agent plan, edit, and run commands on its own raises the stakes compared with a single autocomplete suggestion. A few things worth building into any agentic coding workflow:

• Human review stays non-negotiable. GitHub's coding agent is explicitly designed to stop at a pull request rather than merge directly — Copilot "works in the background, then requests a review from you," per GitHub's documentation. Treat that review step as mandatory, not optional, regardless of which agent produced the diff.
• Permissions and approval gates matter. Google's documentation notes that mutating actions in Gemini Code Assist's agent mode "will ask you to allow the operation unless you have configured Gemini to always allow the tool," while read-only actions may proceed without asking. Understand which actions your tool can take unsupervised before you grant broad permissions.
• Define "done" before the agent starts. Agentic tools verify their own work against whatever check you give them — a test suite, a linter, a build. A vague goal and no tests gives an agent nothing real to iterate against, which is a common source of confidently wrong output.
• Watch session limits and cost. GitHub Actions-based coding agent sessions run for a maximum of 59 minutes per task, and draw on both Actions minutes and Copilot premium requests, according to GitHub's documentation. Anthropic similarly flags that its own multi-agent "dynamic workflows" use more tokens than a standard session, and recommends reserving them for complex, high-value tasks rather than routine edits.
• Guardrails live in configuration, not vibes. Most agentic coding tools support project-level instructions — Claude Code reads a CLAUDE.md file (or a shared AGENTS.md) at the start of every session, for example — that encode standing rules, forbidden files, or required checks the agent can't see just from reading the code.

Getting started without getting burned

If you're trying agentic coding for the first time, the lowest-risk entry points tend to be the same ones vendors highlight: writing tests for already-working code, fixing lint errors across a project, or triaging a backlog of small, well-defined bugs — tasks with a clear, checkable definition of success and low blast radius if the agent gets something wrong. Save larger, architecture-level changes for once you've seen how a given tool plans and reports its work, and keep pull-request review in the loop even after the agent says a task is finished.

How agentic coding fits into the wider "AI agent" trend

Agentic coding is really a specific application of a broader idea: software that acts as an agent rather than a simple request-response tool. Our plain-English explainer on AI agents covers that general concept — perception, planning, tool use, and memory — which agentic coding tools apply specifically to reading, editing, and testing code. That's also why the skills transfer in both directions: the same harness patterns (planning steps, tool-calling loops, verification checks) that show up in Claude Code or Copilot's coding agent are increasingly used for non-coding agents too, from research assistants to customer-support bots.

What's next for agentic coding

Agentic coding is still a fast-moving category rather than a settled standard. Expect three things to keep changing: how much autonomy tools are trusted with by default (GitHub, Anthropic, and Google all currently default to human approval for anything that writes or merges code), how harnesses handle longer and more complex tasks without losing context, and how benchmarks evolve as agents climb existing leaderboards — SWE-bench's own move from its original set to a human-verified subset is a preview of that pattern repeating elsewhere. For now, the practical takeaway is the same regardless of which vendor's agent you pick: give it a clear goal, a real test to check its work against, and a review step before anything ships.

Written byFelix Moreau

Felix Moreau writes Pandromeda's software coverage and how-to guides. He covers Windows, macOS and Linux updates, the apps people rely on, emulators and developer tools, and turns official documentation into clear, numbered steps that work on the current version.

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