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The evidence is clear that AI impacts multiple aspects of the software development lifecycle. A recent Java report found that only 12% of industry professionals surveyed do not use AI tools in their work. One of the hottest AI topics is agentic AI, which is set to make a huge impact on Integrated Development Environments (IDEs) and supercharge developer productivity. However, it is early days for agentic AI, so it is important to understand what it can achieve right now, where it might go in the future, and how it sits alongside existing IDE tools.
Let’s start with what agentic AI means in practice. Agentic AI is like having a pair programmer look at the same content as the developer, understanding what the goal is, and then continuing the developer’s thought process with suggestions. Agentic AI-enabled IDEs could, for example, tell the developer that making a change in this file will impact these three other files. Or, agentic AI could understand and complete complex code tasks for the developer.
Imagine if Microsoft Word could do more than autocorrect, make word suggestions, and write the following three paragraphs. Similarly, agentic AI can take a far broader view, examining and acting on the task at hand and working with the developer on projects that might last months.
Like most new technologies, it is best to start with something relatively contained, and that will deliver specific value. Take upgrading from a previous version of Java, which might have taken six months. Agentic AI could remove much of the toil, making the necessary fixes and improvements, which are then approved or tweaked by a human. The net result might be upgrading 10 times faster compared to previous processes.
Daily, agentic AI within IDEs is likely to make the developer’s workflow faster because, instead of having to stop and think, type everything manually, go through the compile steps, and ensure everything is correct, AI is doing all that. As a result, changes are made faster, flow is swifter, and developers deploy more often. Of course, there is a danger that acceleration might mean even longer wait times (29% of respondents of the Java Developer Productivity Report cited redeploys as one of their biggest obstacles to productivity). The way to overcome that risk is to use an agentic AI together with a productivity tool that allows developers to reload code changes instantly, thus skipping the rebuild, restart, and redeploy cycle typical in software development processes. The result is that developers stay in the flow.
Currently, many agentic AI advancements in IDEs focus on adding plug-ins to popular IDE tools, such as JetBrains, rather than on IDEs designed explicitly for AI. This is likely to be a sustained trend because it is a big jump for many enterprises to install a new IDE with which they are not familiar. Of course, more recent IDE launches built specifically for agentic AI may have their own advantages. Plus, experimentation is very viable with so many IDEs available, many free, and the relative ease of switching between or running several at a time.
However, whatever the IDE strategy, it is vital to put security first, which means vetting vendors and AI models and involving security colleagues in evaluation. Typically, paid-for versions are the safest route because there is more control, with more data privacy guarantees and security assertions. Due to security concerns, it is understandable that some enterprises are banning agent-AI-enabled tools. Again, the Java Development report found that 12% of respondents reported company restrictions on AI tools (climbing to 16% among larger enterprises).
Business leaders used to prohibit the use of open source, yet developers continued to use it, and so it thrived. Ban something and, as history has shown, developers will find a workaround, such as using free tools and paying for them with personal credit cards. It is far better to give teams guidelines and some autonomy while at the same time giving management the transparency and guardrails that are essential to compliance, security, and quality processes.
That said, agentic AI — like any other form of AI — represents a significant cultural and mindset shift, as it revolutionizes how people have been working for decades. Some developers are embracing these tools fast, while others are more resistant to change. However, change is inevitable, and AI is poised to transform the way software developers work on a scale that may be hard to believe.
Imagine talking to the agentic AI in an IDE, telling it there are tests needed for the code it is writing, then telling it to use another tool and write the test in natural language. The developer is speaking in English to the AI, which will then communicate with another AI in English, and all the results will come back in English. These developments are on the horizon. The future of software development is just speaking in natural language, with AI taking care of all the details. Humans are still integral to the process, checking that everything is correct, but this task is likely to become a simple “no-brainer”, akin to glancing at a pop-up window before clicking on it to make it disappear.
These are some of the ways agentic AI can help improve developer productivity, which, of course, is about far more than just programming; it is all the other aspects of the developer’s “overhead”, such as time spent in meetings. However, given that improving productivity is a priority for many software teams, using agentic AI can streamline some work, enhance output, and eliminate mundane tasks.
AI is not the answer to everything, but when combined with other productivity tools, stringent security and compliance measures, and a culture that enables the enterprise to retain control and visibility, it has a major role to play in the future of IDEs. Now is the time to take a closer look at agentic AI, see how it might fit into a developer workflow alongside existing toolsets, and see how it could make a tangible difference.
