What is agentic coding? How AI coding agents work

Introduction

AI coding has moved well beyond autocomplete. Today, coding agents can inspect a codebase, plan changes, edit files, run tests, respond to failures, and continue working toward a defined goal with limited step-by-step input.

That shift is what makes agentic coding different from earlier forms of AI-assisted coding. Instead of helping with isolated snippets, AI coding agents can take on larger parts of the development workflow. This guide explains how agentic coding works, where it is useful, what risks it introduces, and how developers can use it responsibly.

What is agentic coding?

Agentic coding is a software development approach where AI coding agents work toward a defined goal by planning tasks, using development tools, changing code, checking results, and iterating with limited step-by-step direction from a developer.

Instead of responding to one prompt at a time, the agent can carry a task across several connected steps. A developer might ask it to fix a bug, for example, and the agent can inspect the repository, identify the relevant files, make changes, run tests, review failures, and adjust its approach.

What makes agentic coding different?

Agentic coding brings together five core capabilities:

• Goals: The developer defines the outcome the agent should work toward.
• Autonomy: The agent decides some of the intermediate steps needed to reach that outcome.
• Context: It uses code, documentation, tests, dependencies, and project conventions to understand the task.
• Tools: It can interact with editors, terminals, version control, test runners, APIs, and other development systems.
• Iteration: Results from tests, commands, and errors feed back into the next action.

The developer still controls the boundaries of the work. They set requirements, decide what the agent can access, review important changes, and determine whether the result is ready to merge or release.

What is a coding agent?

A coding agent is an AI system that can understand a software task and take actions inside a development environment to complete it.

A typical coding agent combines:

• An AI model to interpret instructions, reason about code, and decide what to do next.
• Codebase context such as source files, tests, documentation, dependencies, and issue details.
• Development tools such as terminals, editors, Git, test runners, linters, browsers, and APIs.
• Action capabilities that let it read files, edit code, execute commands, or create new files.
• Environmental feedback from test results, build output, errors, and other tool responses.
• Permissions and constraints that define which systems it can access and which actions require human approval.

This combination is what allows AI coding agents to move beyond code generation and participate in a broader AI development workflow.

How did AI coding evolve into agentic coding?

AI coding has developed in stages, with each one giving the system a larger role in the development workflow. Early tools helped developers write individual lines faster. Conversational assistants added reasoning and dialogue. Agentic coding extends that progression by allowing the AI to work toward an outcome across multiple steps.

1. Code completion and autocomplete

Early AI coding tools worked mainly inside the editor. They predicted what a developer was likely to write next based on the surrounding code.

Typical capabilities included:

• Completing a line or function
• Suggesting boilerplate code
• Generating repetitive patterns
• Filling in common syntax

The developer remained responsible for deciding what to build, choosing the next step, running tests, debugging failures, and integrating the final change.

2. Conversational coding assistants

Chat-based tools expanded the role of AI beyond autocomplete. Developers could provide a problem, ask questions about unfamiliar code, request a refactor, or troubleshoot an error through conversation.

These assistants made AI-assisted coding useful for broader tasks such as:

• Explaining functions or code paths
• Generating larger blocks of code
• Suggesting fixes for errors
• Writing tests
• Comparing implementation approaches
• Helping developers understand unfamiliar libraries or APIs

The workflow was still largely developer-directed. Each new action usually began with another prompt or request.

3. Agentic coding

With agentic coding, the unit of interaction becomes a goal rather than an individual coding request.

A developer might ask an agent to add authentication to an existing service or fix a failing checkout flow. From there, the agent can inspect the repository, identify relevant files, plan the changes, edit the code, run tests, examine failures, and continue iterating.

That changes the developer-AI relationship:

• Autocomplete: developer writes, AI suggests
• Conversational assistant: developer asks, AI responds
• Coding agent: developer defines the goal, agent carries out multiple steps, developer reviews the result

This progression is what allows coding agents to take on larger pieces of software development while keeping developers responsible for requirements, constraints, and final engineering decisions.

What makes agentic coding “agentic”?

Generating code with AI does not automatically make a workflow agentic. The difference comes from how much of the task the system can understand, plan, execute, and revise on its own.

Six capabilities usually define an agentic coding workflow.

1. Autonomy

A coding agent can decide which intermediate actions are needed to reach a goal.

For example, if asked to fix a failing API endpoint, the agent may choose to:

• Inspect the relevant route and service files
• Trace where the error originates
• Update the implementation
• Run the affected tests
• Investigate any new failures

The developer defines the objective, while the agent handles part of the execution path.

2. Context

Good decisions depend on understanding the surrounding system. AI coding agents may use context from:

• Source files
• Tests
• Project documentation
• Dependencies
• Issue or ticket descriptions
• Coding standards and repository conventions

The quality of that context matters. An agent working from incomplete information can make technically valid changes that conflict with the rest of the codebase.

3. Planning

Larger engineering tasks usually require more than one change. Coding agents can break a goal into smaller steps and decide how those steps should be sequenced.

A feature request might involve updating a data model, changing an API, modifying the UI, and adding tests. Planning helps the agent connect those pieces before or during execution.

4. Tool use

Tool access turns reasoning into action. Depending on its permissions, an agent may interact with:

• Files and code editors
• Terminals and command-line tools
• Git repositories
• Test runners and linters
• Browsers
• APIs
• Development environments

This is a major step beyond traditional AI-assisted coding, where the model often produces an answer and leaves execution to the developer.

5. Feedback and self-correction

Agentic workflows use feedback from the development environment to guide the next action.

If a test fails, a build breaks, or a command returns an error, the agent can inspect the result, revise its approach, and try again. This iterative loop allows coding automation to extend beyond one-time code generation.

6. Control

Autonomy still needs clear boundaries. Teams decide how much freedom an agent receives through:

• File and repository permissions
• Approved tools and commands
• Access to external systems
• Approval checkpoints
• Testing requirements
• Human review before merge or release

These controls define where the agent can act independently and where a developer must step in. In practice, reliable AI development depends as much on these boundaries as it does on the model itself.

How does agentic coding work?

Agentic coding works through an iterative loop where a coding agent receives a goal, gathers the context it needs, plans the work, takes action, checks the result, and adjusts its approach until the task is ready for review.

The exact workflow varies by tool and task, but most AI coding agents follow a similar sequence.

1. Define the goal and constraints

The process starts with a clear engineering objective. The developer describes what needs to change, which boundaries the agent should respect, and how the finished work will be evaluated.

For example, a task might specify:

• Fix a checkout error without changing the payment provider
• Preserve the existing API contract
• Add tests for the affected behavior
• Follow the repository's current coding conventions
• Do not modify production configuration

Clear constraints give the agent a narrower decision space and make the final result easier to assess.

2. Gather relevant context

Before changing code, the agent needs to understand the part of the system it is working with. It may inspect:

• Relevant source files
• Tests and test fixtures
• Package dependencies
• Technical documentation
• Configuration files
• Existing implementation patterns
• Related issues or requirements

This step is especially important in larger repositories, where a seemingly small change can affect several components.

3. Plan the task

Once it has enough context, the agent determines what needs to happen and in what order.

For a feature change, that plan could involve updating a data model, modifying backend logic, changing an interface, and adding tests. More capable coding agents may revise the plan as they discover new information during execution.

4. Execute the changes

The agent then uses the development tools available to it. Depending on its permissions, it may:

• Read and edit files
• Create new files
• Run terminal commands
• Update tests
• Work with Git
• Call development APIs
• Use browser or debugging tools

This is where coding automation moves from recommendations into direct action inside the development environment.

5. Test and observe

After making changes, the agent checks whether they behave as expected. It might run:

• Unit or integration tests
• Builds
• Linters
• Type checks
• Static analysis
• Application-specific validation

The output gives the agent concrete feedback about what worked and what still needs attention.

6. Self-correct and iterate

A failed test or build does not necessarily end the workflow. The agent can inspect the failure, return to the relevant code, make another change, and run the checks again.

This feedback loop is central to how agentic coding works. The agent can move through several rounds of execution and validation without waiting for a developer to issue a fresh prompt after every step.

7. Return the work for human review

Once the task meets its defined checks, the agent returns the result for review. Depending on the workflow, that may include:

• Modified code
• New or updated tests
• A summary of the changes
• Validation results
• A commit or pull request

The developer then evaluates the implementation, checks the broader engineering implications, and decides whether the work is ready to merge.

In practice, the loop looks like this:

Goal → Context → Plan → Execute → Test → Correct → Review

That cycle gives agentic coding its ability to handle multi-step development work while keeping engineering judgment and final approval with the people responsible for the software.

5. Agentic coding vs. AI-assisted coding vs. vibe coding

The difference between these approaches comes down to who controls the workflow, how much autonomy the AI has, and how large a unit of work it can handle.

Traditional development keeps execution with the developer. AI-assisted coding adds suggestions and generation. Vibe coding relies more heavily on prompting to produce working software. Agentic coding gives the AI responsibility for carrying a defined task through multiple development steps.

A few distinctions are especially useful when these terms appear together:

• AI-assisted coding keeps the developer in the driver's seat. The tool helps write, explain, or debug code, while the developer decides and executes each major step.
• Vibe coding starts from natural-language intent and often involves generating substantial parts of a feature or application through iterative prompting. How closely the developer inspects the underlying code can vary.
• Agentic coding delegates more of the execution loop. The agent can inspect the codebase, determine intermediate steps, use development tools, test its changes, and revise its work before handing it back for review.

This is why agentic coding vs. vibe coding is primarily a difference in workflow and autonomy. Both can begin with a natural-language request, but agentic systems have the tools and feedback loops needed to continue working through the task.

Where does agentic engineering fit?

Agentic engineering applies the same principles at a broader level. It covers how engineering teams design development workflows around agents, including planning, implementation, testing, code review, CI, deployment, permissions, and coordination between multiple agents.

Agentic coding describes the coding work an agent performs. Agentic engineering describes the wider system that makes those agents useful, controllable, and reliable across the software development lifecycle.

What can agentic coding be used for?

Agentic coding is most useful when a task has a clear goal, enough context, and a way to validate the result. Common use cases include:

1. Feature implementation

Coding agents can build well-scoped functionality across several files, then run tests to check whether the feature behaves as expected.

2. Debugging and bug fixing

An agent can inspect errors, trace the relevant code path, make changes, and rerun tests until the issue is resolved.

3. Refactoring and codebase modernization

Teams can use agents to update APIs, restructure code, or apply repeatable changes across an existing codebase.

4. Testing and validation

Agents can generate tests, run existing suites, inspect failures, and revise the implementation based on the results.

5. Migrations and dependency updates

Agentic workflows can help with repetitive updates across many files while checking for compatibility issues along the way.

6. Documentation and codebase exploration

Coding agents can trace dependencies, explain unfamiliar code, summarize implementation details, and keep technical documentation aligned with the codebase.

What are the benefits of agentic coding?

The main benefits of agentic coding come from reducing the amount of manual coordination required to move a development task from idea to validated result.

1. Automates multi-step development work

A coding agent can move through planning, implementation, testing, and revision without needing a new instruction after every step. That makes it useful for bounded tasks with a clear definition of done.

2. Reduces repetitive engineering work

Routine work such as writing tests, updating dependencies, applying refactors, and fixing straightforward issues can require less manual effort when an agent handles the repeatable parts of the workflow.

3. Shortens the edit-test-debug loop

Agents can make a change, run the relevant checks, inspect the output, and revise the implementation in one continuous cycle. This can reduce the time developers spend switching between tools and repeating the same validation steps.

4. Makes parallel execution possible

Developers can delegate separate, well-scoped tasks to agents while continuing with other work. For example, one agent might update tests while another handles a migration or refactor.

5. Shifts developer time toward higher-level decisions

As more execution work is delegated, developers can spend more time on architecture, requirements, code review, trade-offs, and other decisions that depend on engineering judgment.

Best practices for agentic coding

The most effective agentic coding best practices focus on giving the agent a clear task, enough context to make good decisions, reliable ways to check its work, and firm boundaries around what it can do.

1. Start with a bounded, well-defined task

Give the agent a specific outcome and clear constraints. Tasks such as fixing a known bug, updating an API, or adding tests are easier to evaluate than broad requests like “improve this codebase.”

A narrower scope also reduces the chance that the agent makes unnecessary changes outside the intended area.

2. Provide the right context

Coding agents work better when they can see the information that shapes the task, including:

• Relevant source files
• Architecture and project conventions
• Existing tests
• Technical documentation
• Related requirements or issues

Good context helps the agent make changes that fit the existing system instead of solving the task in isolation.

3. Define acceptance criteria and automated checks

Before execution begins, make it clear what a successful result should look like. That might include passing a specific test suite, preserving an API contract, or meeting a defined performance threshold.

Automated tests, linters, builds, and type checks give the agent concrete feedback as it works and make self-correction more reliable.

4. Limit permissions and keep human review

Agents should only have access to the tools, repositories, files, and systems required for the task. Sensitive actions or production changes should remain behind explicit approval.

Human review still matters before important code is merged or released. Developers need to assess the implementation, security implications, maintainability, and whether the change actually satisfies the original requirement.

How does agentic coding change the developer workflow?

Agentic coding changes where developers spend their time. More of the execution loop can move to the agent, while developers focus on framing the work, setting constraints, evaluating results, and making decisions that require engineering judgment.

1. Traditional development loop

Understand task → inspect code → implement → test → debug → retest → review

The developer stays directly involved throughout the process, moving between the codebase, development tools, test output, and debugging steps.

2. Agentic development loop

Define goal → provide context and constraints → agent plans and executes → agent validates and iterates → developer reviews

Here, AI coding agents can handle more of the intermediate execution. The developer remains responsible for shaping the task and deciding whether the resulting change is suitable for the codebase.

What changes for developers?

As AI development becomes more agent-driven, several parts of the developer's role become more important:

• Defining problems clearly: Turning requirements into specific engineering outcomes that an agent can act on.
• Writing useful specifications: Capturing expected behavior, edge cases, constraints, and dependencies before execution begins.
• Providing the right context: Giving the agent access to the code, documentation, conventions, and requirements relevant to the task.
• Setting boundaries: Deciding what files, tools, repositories, and systems the agent can access or modify.
• Designing validation: Establishing tests and acceptance criteria that make success measurable.
• Reviewing generated work: Checking correctness, security, maintainability, and broader effects before accepting a change.
• Making architectural decisions: Resolving trade-offs that depend on product priorities, system design, and long-term technical direction.
• Judging when work is ready: Deciding whether the evidence from tests, reviews, and the implementation is strong enough to merge or release.

The result is a different development rhythm. Developers spend less time manually carrying every task through the edit-test-debug cycle and more time directing the work, evaluating outcomes, and applying engineering judgment where it matters most.

Closing thoughts

Agentic coding changes software development by giving AI a larger role in execution. Instead of responding to isolated prompts, coding agents can work through multi-step tasks, use development tools, react to feedback, and iterate toward a defined goal.

The quality of the outcome still depends on how well the work is framed. Clear objectives, relevant context, strong validation, sensible permissions, and thoughtful review determine whether an agent becomes genuinely useful in the development workflow. For engineering teams, the bigger shift is in where human effort goes, from carrying out every step manually to defining the problem, setting the boundaries, and judging the result.

Frequently asked questions

1. What exactly is agentic coding?

Agentic coding is a software development approach where an AI coding agent works toward a defined goal by planning tasks, using development tools, modifying code, testing results, and iterating with limited step-by-step input from a developer.

Developers still define the objective, provide context and constraints, control permissions, and review the resulting work before it is accepted or released.

2. What are some examples of agentic coding?

Common examples of agentic coding include:

• Implementing a feature across multiple files
• Finding and fixing a bug, then rerunning tests
• Refactoring code across a repository
• Generating and validating automated tests
• Updating dependencies or migrating APIs
• Exploring an unfamiliar codebase and updating documentation

These tasks suit coding agents because they involve several connected actions and provide clear ways to validate the result.

3. What are the key differences between vibe coding and agentic coding?

Vibe coding relies on natural-language prompts to generate software, while agentic coding gives an AI agent greater responsibility for planning, executing, testing, and revising a development task.

In a vibe coding workflow, the developer typically continues prompting and steering the output. In agentic coding, the developer defines the goal and constraints, then the agent handles more of the intermediate execution using codebase context, tools, and feedback from the development environment.

4. How does agentic coding work?

Agentic coding typically follows a loop of goal definition, context gathering, planning, execution, testing, self-correction, and human review.

A developer gives the agent a task and relevant constraints. The agent inspects the codebase, determines the required changes, uses development tools to make them, runs tests or other checks, and revises its work when problems appear. The completed changes are then returned for developer review.

5. What is a skill in agentic coding?

A skill in agentic coding is a reusable capability that tells an AI agent how to perform a specific type of development task or use a particular tool.

A skill may contain instructions, workflows, scripts, reference material, or tool access for tasks such as running tests, reviewing code, working with Git, debugging an application, or following a team's coding conventions. Skills help coding agents apply repeatable methods instead of figuring out every task from scratch.