Software development has changed more in 2026 than the familiar “AI helps programmers write code” headline suggests. Coding agents can take on entire tasks, teams can produce more software, and leaders have more ways to measure what happens after a release.
I see five trends shaping the rest of the year. They’re connected by one question: as the pace of development rises, can companies still understand, trust, and maintain what they ship?
1. Coding agents become part of the team
AI has moved beyond suggesting the next line of code. A developer can now give an agent a task, let it inspect a codebase, and review the changes it proposes. JetBrains’ 2026 Developer Ecosystem Survey found that, during May through July, 90% of professional developers surveyed used AI coding agents at work at least weekly, and 68% used them daily.
That changes a developer’s day. More time goes toward describing the task, giving the agent useful context, checking its work, and deciding what should ship. I expect strong engineers to remain central because those decisions require an understanding of the customer, the system, and the consequences of getting it wrong.
For leaders, the question is no longer whether a team has tried coding agents. It’s whether the team has a sound way to assign work to them and review the result.
2. Code review becomes a capacity problem
When producing code gets easier, someone still has to understand the changes. That work can pile up at the review stage.
GitHub has published guidance specifically for reviewing agent-generated pull requests. Its warning is practical: an agent may follow patterns in a repository while missing the incident history, unusual customer needs, or operating constraints that the team knows from experience.
I think this will force teams to pay closer attention to the size and clarity of proposed changes. A pull request that explains its purpose, risks, and test results gives a reviewer a fair chance to make a good decision. A flood of unexplained changes puts pressure on people to approve work they haven’t fully examined.
The best measure of an agent’s value won’t be how many pull requests it opens. It’ll be how much useful work the team can review and release with confidence.
3. Software quality moves beyond the code review
A change can look good in a pull request and still cause trouble for customers. New Relic’s 2026 State of AI Coding report found that 78% of the technology leaders it surveyed reported more production incidents after AI-generated code shipped. The same report found that 86% reported more senior staff time spent fixing code. These are respondents’ reports, rather than a measure of the incident rate across all software teams, but the gap deserves attention.
I expect quality practices to follow software further into production. Teams will need to check whether a feature works under real conditions, whether people can complete the task it was built for, and whether a release creates more support work than it saves.
That’s a broader definition of quality than “the tests passed.” It asks whether the software delivers the result the business and its customers expected.
4. Security becomes part of the agent workflow
Coding agents can touch files, dependencies, tests, and development tools. Their usefulness grows with that access, and so does the need to control it.
GitHub’s 2026 guidance for agent pull requests recommends limited permissions and human approval before an agent-driven workflow takes actions that affect production. GitHub has also introduced agentic fixes for code-scanning alerts, with the proposed repair and its validation steps presented for review.
I see this as a shift in everyday development practice. Teams need to know what an agent was allowed to do, what it changed, and how the change was checked. Security can’t depend on someone noticing a problem after the agent has finished.
5. Leaders start measuring outcomes instead of output
AI makes it easier to count activity: generated lines of code, completed tasks, opened pull requests. Those numbers can tell a team what happened, but they can’t tell a leader whether the investment paid off.
DORA’s 2026 report on the ROI of AI-assisted software development argues for connecting development measures to financial outcomes. Its framework also accounts for the learning period when teams adopt new tools, which matters when judging results.
I’d ask whether a team is delivering valuable features sooner, spending less time on rework, and improving the customer experience. If code output rises while incidents and maintenance work rise with it, the productivity story is incomplete.
These trends point to a new balance for software development. Agents can help teams move faster, but speed puts more weight on review, security, quality, and leadership judgment. In 2026, I think the companies that gain the most from AI will be the ones that can show their software works where it counts: in the hands of the people who use it.
