The release of AI agents marks the latest shift in the developer landscape. These tools are currently being marketed for their ability to make developers’ lives easier. However, in many cases, AI agents are so new that use cases are unclear or undefined, which can complicate development cycles rather than accelerate them. What’s more, many enterprises want to be seen as adopting AI in the “right” way, which stifles experimentation as organizations dictate which AI tools their teams can and can’t use.
To encourage effective AI use among developers and smooth the path to adoption, enterprises must define clear use cases for AI agents. Read on to find out more about how enterprises can use AI agents.
1. Security Updates and Patches
Enterprises can often take months to manually roll out security updates or patch known issues. There is no reason updates should take this long, and, in their simplicity, these updates are ripe for “agentifying”. The shift from human-led to agent-led updates will allow organizations to significantly reduce update cycles. AI agents also provide an avenue for enterprises to reduce engineer toil, which is often worsened by strict deadlines such as milestone moments like Patch Tuesday.
One IDC report, sponsored by JFrog, found that software developers spend nearly a fifth of their workweek (19%) on security-related tasks. It’s not uncommon for some of these to be simple and manual tasks that can be automated with AI agents, such as application scan reviews and secrets detection. The same report found that forcing developers to focus their efforts on such manual tasks is costing organizations $28,000 per developer per year. The use of AI agents has significant potential to drive down this cost when applied to manual workflows that security teams currently struggle with.
Common vulnerability and exposure (CVE) fixes and other simple, repeated security updates don’t require human input for resolution, other than to verify fixes proposed by an agent. If enterprises implement AI agents free engineers from these simple security-related tasks, and will see a major uptick in the value and production from their engineers’ working time.
2. Code Review/Testing
There have never been enough software engineers to meet enterprises’ needs. In today’s economy, this black hole has become increasingly evident. No enterprise can afford to hire entire teams of testers, meaning that code review frequently becomes a workflow blockage.
According to one report, three-quarters of software engineers often sacrifice functional safety to meet code delivery deadlines. Code review and testing offer an opportunity for enterprises to adopt AI agents to remove this common bottleneck in development cycles, especially when simple code from known libraries is in review.
Of course, AI agents can’t take the roles of human coders. In its current state, it is incapable of the creativity required for things like vibe coding and complex code, but AI agents can be applied successfully for code testing to reduce toil for human developers and teams.
3. Augmenting Engineers’ Creativity
AI agents can make excellent “assistants” to carry out the groundwork of coding projects. These tools can augment human developers for the initial stages of this work, including figuring out how an environment should operate. Setting up an environment should be a quick process, taking no more than 5-10 minutes, but in extreme cases, it can take weeks.
AI agents can help with this initial stage of any development task by spinning up an environment in moments based on the prompt given to it by an engineer. This will enable developers to instead focus on the real craft of writing code and exploring creative work rather than spending excessive time on the early stages of any coding project.
The Potential Pitfalls of AI Agent Adoption
There is a risk that adopting AI agents across an entire enterprise will reduce opportunities for junior developers to learn. From both an organizational and industry perspective, AI agents cannot replace the next generation of software engineers. If AI does all the basic work that junior developers would typically have carried out, the early-career workers would find fewer learning opportunities for themselves.
Given that there has always been a shortage of software engineers, organizations must take measured approaches toward maintaining a healthy pipeline of engineers. This means identifying areas where junior developers can take on responsibility and learn, such as writing code, supporting code reviews and assisting in debugging and resolving issues. By giving these tasks to junior developers, organizations can ensure that the talent pipeline continues to produce developers who can perform in large enterprises.
Secondly, AI agents are also regularly over-marketed products, and successful enterprises will be able to decipher reality from the hype. Many practitioners have already found that there are use cases where AI agents cannot be used, despite the promises made by marketing departments. Engineers must work out which tasks can be handled by agents and which must remain in human hands. Human-led tasks are often deeply complex, requiring proprietary data or legacy systems with hard-to-access data that AI agents would struggle to interpret. Accurately identifying where AI tools can be used to bring real value, rather than being used for the sake of using them, will differentiate successful enterprises from the competition.
AI agents cannot be adopted as a hammer in need of a nail. They must be used to solve existing challenges that it is suitable for.
The Two-Step Strategy for AI Agent Success
To successfully adopt AI agents, there are two steps enterprises must take.
First, enterprises must cast a wide net and give teams as many tools as possible to experiment with, in safe environments, to enable creativity. After all, AI adoption is more than just a box-ticking exercise. Engineers must be able to use AI agents to improve their day-to-day experience and not have their suite of available AI tools reduced in the name of uniformity.
Second, engineers must be encouraged to get involved in peer-to-peer research and discussions to find the tools that work for them, as well as use cases that bring tangible value to the organization. From social media discussions to in-person conversations and events, developers best learn from each other. To see the benefits of AI agent adoption, developers must be encouraged to have these conversations with their peers; otherwise, AI agents will just become the latest rung in the ladder of misplaced investment.
