AI agents can solve, document, and report software bugs in minutes. Does this change the role of the developer? Inevitably. There is less coding under pressure, but more responsibility for the bigger picture. Technical Architect Lauri Eliander shares his observations on tackling software bugs in the age of AI agents.
Read the summary (AI-generated, human-reviewed)
- AI agents can automate much of the routine work involved in fixing software bugs, but developers take on greater responsibility for the overall process, quality, and the information the agent works with.
- A good agent needs to know not only what to do, but also when to stop. Clear boundaries, well-reasoned rules, and strict quality criteria are essential for reliable agentic work.
- The developer’s role is shifting from execution toward guidance and quality management. Agent performance can be improved by refining its instructions and carefully reviewing the results.
The automation that agents bring to the software development process is often not about automating the thinking itself, but everything around it. Reasoning is only a fraction of the work an agent does when fixing a software bug. Most of its time is spent reading the ticket, changing its status, creating a branch, packaging the solution, compiling the code, installing it, writing testing instructions, and assigning the tester.
This is exactly the work that takes up a developer’s day. It is also the easiest part to describe in writing because it is largely mechanical.
The harder question is not “Can an agent do this?” but rather:“Have we documented our way of working precisely enough to hand the task over to someone else?”
The most important instruction is not what to do, but when to stop. The most valuable part of an agent’s instructions is the gate at the beginning: the conditions that must all be met before the agent can proceed, and an instruction to return the ticket for further specification if even one of those conditions fails.
An agent that has been told what to do will always do it. An agent that has been told when to refuse is finally useful.
In agentic work, the primary risk is not whether the model reasons correctly. It is whether it is reasoning from the right starting point.
Do agents take the joy out of coding?
A developer’s responsibility is increasingly focused on quality management, and this responsibility is no lighter than before.
Previously, you could think of that responsibility as being spread across 30 small decisions made while writing code. Now it is concentrated in two places: what you write in the agent’s instructions beforehand, and what you verify in the result afterward.
There are fewer decision points, but each one carries more weight.
Better quality does not always come from more capable AI. Sometimes, it comes from the constraints we give it.
When an agent-written Jira entry receives praise, the real reason may be an instruction that prevents the agent from using technical language, class names, or table names in a comment intended for a customer. Without that constraint, the agent would write a more technical message that is less useful to the reader. This is worth saying out loud because it is the opposite of what we might normally assume.
So, do agents take the joy out of coding? Not really, at least not for me. AI constantly opens new perspectives and possibilities that I would not have thought of myself. Right now, that creates the same kind of excitement I felt when I first learned to program.
Three lessons I have learned from working with AI agents:
1. Explain the reason behind every rule. When a rule includes the “why,” an agent can apply it to situations that the person who wrote the rule did not anticipate. A generic rule alone does not provide enough context.
2. Set the bar at zero errors and zero warnings. A flexible threshold invites an AI assistant to justify an exception. The agent will happily write a convincing paragraph explaining why one particular warning is acceptable.
3. Improve the process by editing the instructions, not by training the agent.
One morning, a test run failed because of a detail in an access control rule. The issue was turned into a new paragraph in the instructions. That afternoon, the agent passed the same point successfully. The improvement was a single paragraph of text in the instructions – something a person can read, review, and challenge.
Read also:
This is how AI is changing work: Moving from task execution to continuous orchestration of work
The new era of software development is not a threat, but an opportunity to build a new kind of competitive advantage