Imagine you are describing a new feature in just a few sentences: The customer portal should export invoices as PDFs. In doing so, access rights must be taken into account and appropriate tests must be added. A coding agent then reads the codebase, locates the relevant sections, plans the change, and implements it. It then runs tests, responds to error messages, and refines its own changes.
Such processes are changing the nature of software development. AI is no longer limited to adding individual lines of code. Modern tools can handle entire tasks while accessing files, tests, documentation, and development tools. Depending on the tool, they focus on tests, documentation, or specific areas of a codebase.
This saves time and lowers the entry barriers. At the same time, the amount of code that needs to be reviewed and categorized is growing. After all, a solution can look clean and pass a test successfully even though it may be unnecessarily complicated, technically inappropriate, or difficult to maintain in the long run.
From Assistant to Agent
With traditional AI assisted coding, the process remains largely in human hands. The AI completes a line of code, explains a function, or suggests a small section of code. Agentic coding goes a step further. An agent is given a goal and breaks it down into several steps. It gathers context, modifies files, uses tools, checks results, and adjusts its approach.
The key difference lies in the scope of the task. People no longer oversee every single step. Instead, they define the framework, provide the necessary context, and review the results.
We also frequently come across the term “vibe coding.” This usually refers to a development style in which an application is created primarily through natural-language instructions. This can work surprisingly well for a prototype. For a product intended for long-term operation, however, a compelling first impression is not enough. It becomes clear -at the latest when issues of security, maintainability, and future expansions arise – whether there is a sound concept behind the generated code.
Why the Barriers to Entry Are Falling So Quickly
Agentic Coding makes software development more accessible. An idea can first be formulated in everyday language. The agent translates it into technical steps and handles a large part of the implementation. This saves time, especially when tasks can be clearly described.
This includes recurring changes, simple functions, boilerplate code, documentation, tests, and manageable refactorings. It also makes it easier to get started with an unfamiliar codebase or a new framework. The agent can explain existing structures, find relevant files, and suggest an initial solution. However, anyone who wants to evaluate the result must still understand the underlying concepts.
For prototypes, in-house tools, and clearly defined functions, this is a major advantage. Ideas become visible more quickly and can be tested sooner. As a result, custom software becomes more appealing to more companies because less time is spent on pure paperwork.
Test-driven development is also taking on new significance. The desired behavior can first be described in the form of tests. This provides the agent with a concrete guideline for its implementation. In practice, this is very helpful as long as the tests actually reflect the correct behavior. A test only verifies what has been previously considered and defined. Missing requirements remain invisible even to the best agent.
Reasoning Improves Results and Sometimes Makes Them Unnecessarily Complicated
Coding agents typically work in multiple loops. They analyze the task, create a plan, implement changes, and then review the feedback from their tools. This process is often referred to as reasoning. Simply put, the system processes several intermediate steps before arriving at a result.
This improves the quality of many tasks. An agent can detect an error, discard its approach, and try a second solution. However, more intermediate steps do not automatically lead to better code.
In practice, developers sometimes tend to make a simple task more complicated than it needs to be. They introduce additional abstractions, build in extra layers as a precaution, and account for rare edge cases, even though the product does not actually need them. The result appears well-thought-out, but it leads to more code, more tests, and more maintenance work.
The underlying problem is simple: Agents often lack an understanding of the broader business and technical context. They see the current task and the specific section of the codebase that has been provided. Only if context is explicitly provided they can tell why a deliberately simple solution was chosen, which features are planned for the future, or what compromises a team has already made.
The Most Dangerous Mistake Looks Convincing
Powerful language models have become more reliable, but they remain prone to errors. They can misuse interfaces, make assumptions about a codebase, or invent functions that sound plausible. Added to this are biases stemming from training data and from the context an agent receives for its task.
The problem is rarely an obviously broken block of code. More difficult to deal with are solutions that look professional, pass tests, and only reveal their weaknesses later on. A function may be implemented correctly in isolation but still undermine the architecture of the entire system. It may introduce a new pattern that is not used elsewhere. It may maintain duplicate data, unnecessarily tighten dependencies, or make future extensions more difficult.
That is why human judgment remains essential, particularly when it comes to architectural decisions, interfaces, and fundamental program logic. An agent can identify various possible solutions, write code, and significantly speed up implementation. The crucial question, however, remains: Which solution is the best long-term fit for the product, the existing architecture, and the project requirements? This decision requires experience, an understanding of the context, and a comprehensive view of the entire system – qualities that only a human can provide.
Data Issues are Also Part of Quality
Anyone who uses a coding agent grants it access to source code, documentation, and project structures. Depending on the task, configurations, logs, or sample data may also be processed. This makes the choice of tool a matter of data protection and responsibility.
Before deployment, it must be clarified what information may be transmitted to a model, where it will be processed, and what agreements are in place with the customer. For some projects, high-performance external models may be an option. Others require more isolated environments or self-hosted models. There is no one-size-fits-all solution here. The decisive factors are the data, the risk, the specific intended use, and the customer’s approval.
Agentic Coding Requires a Clear Framework
Agentic coding can significantly speed up development. Especially for well-defined tasks, it now delivers results that would not have been possible in such a short time just a few years ago. The benefits increase when requirements are clear, tests remain meaningful, and the agent is provided with sufficient context.
At Sensetence, we deliberately incorporate such tools into our day-to-day development work. We take advantage of their speed, but we also carefully evaluate how well a solution fits into the product, what data is being processed, and whether the resulting code will remain understandable in the long term. We remain responsible for the final result.
Are you planning a digital product or looking to further develop an existing application in a targeted way? Talk to us about your idea. Together, we will determine where agentic coding can help you move forward efficiently and what guidelines your project needs.
In the second part of our series, we will explain what this transformation looks like in practice. There, we are going to show how our approach to AI-powered development at Sensetence has evolved step by step: from the first instances of code completion to today’s agent-based workflows.