Agentic AI Coding Tools: 10 Tools for Building Software With AI

Agentic AI coding tools help people build software with less manual work. They have the ability to read project files, edit code, run commands, check errors, and prepare changes for review. Instead of only giving advice, they can carry out parts of the development process. Tools for AI Agent coding are changing everyday, making it difficult to keep up. You may be wondering what tool to choose and which to use for what purpose. This guide will give you a quick overview of the most popular agents AI coding tools.

These tools are useful for all sorts of people: developers, founders, and product teams, but most interestingly, they can also give non-technical people the power to code their own tools. A developer can ask for a bug fix. A founder can build a first version of an app. A product team can move repeated engineering tasks out of someone’s daily queue. And you can also do all of that, with the right agent coding tool.

That same idea is now sprouting beyond software teams. Handinger gives companies managed AI agents that run in the cloud and get work done across tools like CRM, email, calendar, tasks, and files. Handinger helps automate searching, reading, clicking, emailing, and scheduling, then returns structured results that teams can use in their workflows.

That makes Handinger useful for people building with AI and for teams that want to use agents in daily work. Developers can use agentic AI coding tools and embed Handinger in the product with an API to build their own product. Teams can use Handinger to run the work around the product, such as research, follow-ups, scheduling, reporting, and internal tasks.

What are agentic AI coding tools?

There are many different kinds of Agentic AI coding tools. Basically, they can take a coding task and work through it in several steps. They can usually work autonomously, but this varies depending on the type of tool you are using. A simple coding assistant may suggest a line of code with auto-completion features. An agentic coding tool can look through the source code of a project, decide which files matter, make edits, run checks, and show what changed.

These tools can also be useful for people just starting out as devs, for example for learning a codebase. A developer joining a new project can ask why a feature works a certain way and a coding agent can read the files and explain the flow in plain language for the dev. That saves time before any new code is even written.

Handinger works with the same basic idea of delegation, but outside the code editor. A company can create a specialist agent for a repeated job, connect it to the right tools, and trigger it from the Handinger app, an API call, a schedule, or an email sent to the agent’s own inbox. Handinger is a way to automate your company work with managed agents and put agents to work and automate repetitive tasks.

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What are the best agentic AI coding tools?

The best agentic AI coding tools depend on the job. Some are better for developers who live in the terminal. Some are better inside a code editor. Some work in the cloud and can handle tasks while the team reviews the results later. Some help non-technical people build apps from plain English.

Top 10 agentic AI coding tools worth knowing

Here are 10 tools worth knowing.

1. Handinger

Handinger is a managed agent platform for companies that want AI agents to do real work in the cloud. It is not only an agentic AI coding tool in the narrow sense. It is broader than that. While many agentic AI coding tools help developers write and change code, Handinger helps teams create AI agents that run repeated company workflows across the tools they already use.

Companies can connect tools such as CRM, tasks, email, calendar, and files, then create specialized agents for jobs their teams repeat every day. Each agent runs in the cloud, stays connected to company data, and can be triggered from the Handinger app, through the API, on a schedule, or by sending an email to the agent’s own inbox.

That makes Handinger useful for both technical and non-technical teams. A developer can use agentic AI coding tools like Claude Code, Cursor, or Codex to build the software. Then the company can use Handinger to run the work around that software. For example, a team could create agents that research leads, update CRM records, summarize emails, create tasks, prepare reports, or coordinate follow-ups.

Handinger is also valuable for people building AI products. Instead of building the agent infrastructure from scratch, teams can use Handinger to create managed agents that can be embedded into their own product or triggered through an API. This can save engineering time because the team does not need to build the cloud worker system, tool connections, scheduling, inbox triggers, and execution layer on its own.

For companies that want to put agents to work, Handinger is a strong first choice. It brings the agentic AI coding idea into business operations. Coding agents help build the product. Handinger helps teams create cloud agents that do the repeated work inside the company or inside the product itself.

2. Claude Code

Claude Code is one of the clearest examples of an agentic AI coding tool. According to Anthropic, Claude Code can read your codebase, edit files, run commands, and work with development tools. It is available in the terminal, IDE, desktop app, and browser.

Claude Code is useful for developers who want to hand off real coding tasks without leaving their workflow. You can ask it to fix a bug, explain a project, write tests, update files, or help with Git work.

It is especially helpful when the task touches more than one file. Instead of asking separate questions about each part of the project, the developer can describe the goal. Claude Code can inspect the code, make a plan, and apply changes.

3. OpenAI Codex

OpenAI Codex is a coding agent for building, fixing, and reviewing code. OpenAI says Codex can generate code from a description, adapt to an existing project, explain unfamiliar codebases, and review code for bugs or edge cases.

Codex also has a cloud version. OpenAI says Codex can write features, answer questions about a codebase, fix bugs, and propose pull requests for review. Each task runs in a separate cloud environment with the project loaded.

That setup helps teams run more than one coding task at a time. A developer can ask Codex to investigate a bug while working on another task. The team can then review the result before merging anything.

3. GitHub Copilot

GitHub Copilot is a common choice for teams already using GitHub. It helps inside editors and also has agent features for GitHub workflows.

GitHub says Copilot’s cloud agent can research a repository, create an implementation plan, make changes on a branch, and let the user review the diff before creating a pull request.

This is useful because many software teams already work through issues, branches, and pull requests. Copilot can fit into that process without asking the team to create a new system.

For example, a developer can assign a small issue to Copilot. Copilot can prepare the changes, and the developer can review them before they reach the main codebase. That keeps people in control while reducing the time spent on routine work.

4. Cursor

Cursor is an AI code editor built around agentic development. Cursor talks about Agent mode, rules, skills, MCP servers, CLI, models, and team setup.

Cursor is useful for developers who want an agent inside the editor. It can help write code, change files, answer questions about the project, and support larger feature work.

The benefit of using Cursoe is having speed with context. When the agent lives in the editor, it can work with the code that is already open and the project rules the team has set. That makes it easier to go from having an idea to making a change.

Cursor also helps you work on products and it speeds up the process. A founder or developer can build a prototype, revise it, fix issues, and add features without switching between many tools.

5. Windsurf

Windsurf is an AI coding environment with an agent called Cascade. According to Windsurf, Cascade supports Code and Chat modes, tool calling, voice input, checkpoints, real-time awareness, and linter integration.

Windsurf is useful for developers who want an AI assistant that can work across files and keep track of the project state. It can help with code edits, debugging, and multi-step tasks.

Checkpoints are helpful because agentic coding can move quickly. A developer can try a change, inspect the result, and go back if the direction is wrong. That reduces the risk of letting an agent work across a project.

Windsurf is a good fit for teams that want speed but still want review. The agent can do much of the typing, searching, and fixing. The human still decides what is correct.

6. Devin

Devin is an AI software engineer from Cognition. Devin’s docs say it can write, run, and test code. Devin can also work on Linear or Jira tickets, new features, bug fixes, and internal tools.

Devin is built for delegation. A team gives it a clear task, such as fixing a bug or building a small internal tool. Devin works through the task and returns something the team can review.

That is useful when engineering teams have many small jobs that slow people down. A developer may not want to spend half a day on a routine migration or a small admin tool. Devin can take on some of that work.

The key is giving it a clear request. A vague task creates poor results. A detailed task with expected behavior, test steps, and limits gives the agent a much better chance.

7. Replit Agent

Replit Agent is built for people who want to create apps from plain language. Replit says Replit Agent turns ideas into apps, designs, slides, and more from plain language, with no coding required.

This makes Replit Agent useful for beginners, founders, students, and small teams. A person can describe the app they want and let Replit handle much of the setup and code generation.

That lowers the barrier to testing ideas. A founder can create a working version of a tool before hiring a full engineering team. A student can learn by seeing how the app is built. A small team can create internal tools without starting from a blank project.

Replit Agent is strongest when the goal is clear and the first version does not need complex custom systems. For larger products, a team may still need developers to review the code, improve the design, and handle security.

8. JetBrains Junie

Junie is the AI coding agent from JetBrains. JetBrains says Junie runs in JetBrains IDEs, in the terminal, or in automated development pipelines.

That makes Junie useful for teams already working in tools like IntelliJ IDEA, PyCharm, WebStorm, GoLand, PhpStorm, RubyMine, RustRover, or Rider. Developers can use the agent without leaving the JetBrains environment they already know.

JetBrains also says Junie can plan and carry out multi-step actions, make large edits, run tests or terminal commands, and report progress.

A good way to describe Junie is this: it helps inside the editor, but it can also support the work that happens around code checks and releases. That helps teams keep agent work tied to normal development habits.

9. Google Jules

Jules is Google’s coding agent. Google says Jules helps fix bugs, add documentation, and build new features. It integrates with GitHub, understands the codebase, and works while the developer moves on to other tasks.

Jules is useful for tasks that are clear but time-consuming. That can include writing tests, updating docs, fixing smaller bugs, or handling code cleanup.

The main benefit is focus. Developers can give Jules a task and then spend time on work that needs more judgment. When Jules finishes, the developer can review the changes before using them.

Jules is also useful for teams that want coding agents tied to GitHub. A task can start from a repo, and the output can be reviewed before it becomes part of the project.

10. Cline

Cline is an open-source AI coding agent. Cline’s docs say it lives in the editor and terminal, can read and write files, run terminal commands, use a browser, and help build features through conversation. Cline also asks for approval before each action.

Cline is useful for developers who want more control. Because each action needs approval, the developer can follow the agent’s work step by step. That is helpful when learning how agentic AI coding tools behave.

Cline also gives developers more flexibility because it is open source. Teams can inspect the project and adapt it to their needs.

This makes Cline a good choice for people who want to understand the process, not only get the result. It can be slower than a more autonomous tool, but the extra control can reduce surprises.

How to choose tools for agentic AI coding

Choosing tools for agentic ai coding starts with the work you want to remove from someone’s day.

If the work happens inside code, choose a coding agent. If the work happens across company tools, choose a managed agent platform like Handinger. Many teams will need both.

For editor-based work, look at Cursor, Windsurf, GitHub Copilot, Cline, or Junie. These are useful when developers want the agent close to the files they are editing.

For terminal-based work, look at Claude Code, Cline, Codex CLI, or Junie. These are useful for developers who already prefer command-line workflows.

For cloud-based engineering tasks, look at OpenAI Codex, Devin, GitHub Copilot cloud agent, or Google Jules. These tools are useful when a team wants to assign a task, let the agent work, and review the result later.

For app building from plain language, look at Replit Agent. It is useful when the goal is to create a working first version without setting up a full development environment.

For company workflows, look at Handinger. It is useful when the task is not only to write code, but when they want to save their resources and want Handinger to do the work instead of building their own infrastructure for it.

The best way to choose is to test each tool on real work. Pick one bug, one small feature, and one repeated workflow. See how much time the tool saves. Check how easy it is to review the output. Notice where the agent gets stuck.

Security is also important to consider. A coding agent may need access to source code. A company agent may need access to email, files, CRM records, or calendars. Give each agent only the access it needs for the job.

Good instructions matter too. Agents work better when they know the goal, the limits, the tools they can use, and the expected output. A short vague prompt may work for a small task, but a company workflow needs a clear role and clear rules.

Start building something with agentic AI

Agentic AI coding tools are an exciting new advancement that will change the way products are built. They help developers move from idea to working code with less time invested. Instead of only suggesting snippets, these tools can inspect files, make changes, run checks, and help prepare work for review.

The main value is not that they replace people. Their value is that they reduce the slow, repetitive parts of building software. Developers can spend more time on product decisions this way, and architecture, user experience, and quality while agents help with implementation, testing, documentation, and routine fixes.

For teams building with AI, the future is not only about writing code faster. It is about putting agents to work in useful, controlled ways. Agentic AI coding tools help build the software. Handinger helps bring the same working-agent model to you.

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