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A few years ago, AI in programming mostly just suggested the next line of code. Today, a coding agent can take on a task, get up to speed on a project, write the code, run the tests, and present the result for approval. According to the 2025 Stack Overflow developer survey, 31 percent of developers were already using AI agents, with another 17 percent planning to do so.
For companies without their own IT department, this is more than just news from the developer world. Tasks you previously needed an agency for are now within reach. In this article, we explain how a coding agent works, what the term "agentic coding" actually means, and what you should look out for when getting started.
What is a coding agent?
A coding agent is an AI program that independently completes programming tasks over several steps. It reads existing code, plans the implementation, writes and modifies files, executes commands and tests, and fixes its own errors. In the end, it presents the result to a human for review.
The difference from a chatbot lies in the action. A classic chatbot writes code for you that you have to copy, paste, and test yourself. A coding agent works directly within your project. It has access to files and the terminal, sees error messages itself, and makes adjustments until the tests pass.
How a coding agent works: planning, writing, testing, and reviewing
Most coding agents operate in the same loop:
- Understanding the task: You describe what needs to happen, for example, "Add a mandatory field for the company name to the contact form and sync it to the CRM." The agent reads the relevant files and your project rules, often from a file like CLAUDE.md or AGENTS.md.
- Creating a plan: It breaks the task down into steps and shows you the plan. Good agents will ask questions if something is unclear.
- Writing code: It modifies the necessary files, often several at once.
- Testing: It starts the application or runs automated tests, reads error messages, and makes corrections. This loop often runs multiple times.
- Reviewing: At the end, it summarizes the changes and presents them as a pull request—a proposed change for a human to approve.
You are needed at two points: at the beginning to define the goal and at the end to give approval. In between, the agent works on its own for anywhere from a few minutes to several hours, depending on the task. In most tools, you can set what it is allowed to do without asking. Modifying files is usually permitted, but commands that delete or publish something require your consent.
Advanced setups allow multiple agents to work in parallel. One writes the code, a second reviews it, and a third adds tests. This way of working is what is meant when people talk about "agentic coding."
What is agentic coding?
Agentic coding describes a workflow where coding agents handle the bulk of programming tasks. Humans set the goals and rules while overseeing quality. Anthropic explicitly refers to Claude Code as a tool for agentic coding. Andrej Karpathy, who coined the term "vibe coding" in early 2025, suggested "agentic engineering" a year later as a name for the professional version.
The difference from vibe coding lies in accountability. With vibe coding, nobody checks the code as long as the result works. With agentic coding, a human remains responsible for everything that goes live.
To learn more about the concept of vibe coding, its impact on modern workflows, and practical business use cases, check out our guide on vibe coding: meaning & examples.
We explain what agentic AI means beyond programming in Agentic AI explained simply.
Which coding agents are available?
Which agent is the right fit depends primarily on where your code is hosted and how much you want to work within the code yourself.
Entry-level subscriptions for most providers cost between $10 and $20 a month. If you work with agents daily, you'll quickly find yourself in higher-tier plans or on usage-based billing.
What coding agents do well and where they fall short
Well-suited for: Fixing bugs, adding features to existing code, writing tests, updating outdated libraries, and building interfaces between two tools. These are tasks with a clear goal where you can easily verify if the result is correct.
Less suited for: Vague requirements, major architectural decisions, and anything requiring specialized knowledge that isn't documented anywhere. The agent builds what it understands. If your description has gaps, it will fill them with assumptions.
Two figures put this into perspective. In the 2025 Stack Overflow survey, 66 percent of developers stated that AI results are often almost correct, but only almost. More respondents distrusted the accuracy (46 percent) than trusted it (33 percent). A study by the research institute METR from the summer of 2025 was even more telling. 16 experienced open-source developers worked on 246 tasks in projects they knew well. With AI tools, they took an average of 19 percent longer, yet they believed they were 20 percent faster afterward.
The tools have improved significantly since then. Still, the lesson remains: if you introduce coding agents, we recommend measuring the time savings. Your gut feeling can be deceiving.
What do coding agents mean for companies without an IT department?
For companies without their own developers, the question shifts. Previously, it was: Is it worth hiring an agency for this? Today, a tech-savvy person on the team can handle many small tasks themselves using a coding agent:
- Integrations: Automatically transferring inquiries from website forms into the CRM.
- Data processing: Merging exports from inventory management and accounting systems to generate reports.
- Website adjustments: Small changes to pages, landing pages, and forms.
- Internal tools: Calculators, checklists, and small databases for the team.
Coding agents also help with tasks that have little to do with programming. They can rename hundreds of files according to a specific pattern or convert text into a different format. At bakedwith, for example, we use Claude Code for our blog. The agent formats articles for our CMS, creates them as drafts via the API, and generates graphics in our brand style. Nothing goes live until a human has reviewed and approved the draft.
The biggest catch: Without an IT department, you often lack someone who can verify the results. An agent might provide code that works but still contains security vulnerabilities. Plus, if you build a tool with an agent, you’re still responsible for it a year later if an interface changes.
Want to build custom tools and applications without a dedicated development team? Check out our step-by-step guide on coding with AI — how to build your own software without any programming skills.
If you’re starting out without your own developers, we recommend following these four rules:
- Appoint a person in charge: Someone on the team should know all the tools being built and have an external contact for reviews.
- Write down project rules: Use a file like CLAUDE.md or AGENTS.md to define what the agent is allowed to do, which data is off-limits, and how testing should be handled.
- Limit access: Agents should work with test data and their own accounts without admin privileges. A 2025 incident where a Replit agent deleted an entire production database shows just how quickly things can go wrong.
- Review every change: Anything that goes live or involves customer data should be reviewed by someone with development experience beforehand.
When it comes to customer data, you also have to consider privacy issues, such as where the provider processes the data. You can read more about this in AI and data protection. To ensure you don't end up with "shadow IT" tools that no one knows about, it's also worth looking into the topic of Shadow AI.
Conclusion: Coding agents handle the implementation, but the responsibility remains with you
A coding agent plans, writes, tests, and submits its work for review. This means it takes over a large part of the implementation that you previously needed developers or an agency for. Agentic coding means using these agents with clear goals, rules, and controls. For companies without an IT department, this is a real opportunity, provided someone can verify the results and access is strictly limited.
The best way to start: Pick a recurring task that doesn't involve customer data, such as creating a report from two Excel exports. Let Claude Code or Codex build a script for it. Read the plan before the agent starts, and verify the result against an analysis you are already familiar with.
If you want to know which tasks in your company are suitable for coding agents, get in touch with us for an AI consultation.
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FAQ: Frequently asked questions about coding agents
What is the difference between a coding agent and a chatbot?
A coding agent works directly within your project, executing commands and fixing errors on its own. A chatbot writes code that you have to copy, paste, and test yourself. With an agent, you set the goal and review the result.
What is agentic coding?
Agentic coding is a way of working where AI agents handle the bulk of the programming. Humans set the goals and rules, and review every change. This distinguishes it from "vibe coding," where no one actually reads the code.
Which coding agent is the best?
That depends on where you work. Claude Code and Codex are considered the most powerful standalone agents. GitHub Copilot is a good fit if your code is already on GitHub, while Cursor is better if you prefer working directly in the editor.
Will coding agents replace developers?
Not yet. Coding agents take on a lot of the implementation work, but they still need clear tasks and someone to evaluate the results. As a result, the developer's role is shifting more toward planning, code review, and architecture.
Do I need programming skills to use a coding agent?
Not necessarily for simple tasks, as you control them using natural language. However, having basic technical knowledge is very helpful for checking results and assessing risks. If you lack that, someone with development experience should sign off on the changes.
Are coding agents secure?
They are only as secure as the rules you give them. Restricted access, test data, and a review process before going live significantly reduce the risk. It only becomes dangerous if an agent has admin rights to production systems.
Posted by:
Jens is the co-founder of bakedwith, a boutique consultancy firm specialising in smart automation and AI. He helps medium-sized companies and corporations to optimise processes, reduce manual work and achieve growth through efficient workflows. Previously, he was Growth Lead at OMR and worked as a freelancer, helping numerous companies optimise their CRM and automation systems. Jens is passionate about combining growth and efficiency to enable teams to focus on what matters most.
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