AI will change software development and the fundamentals of how people work, with senior engineers becoming more important rather than sidelined. Agentic AI combined with Model Context Protocol can speed software development but also expands potential attack surfaces. Context engineering will replace prompt engineering for complex projects by using structured inputs such as model choice, token limits, and links to relevant data, apps, and systems, enabling teams of agents with distinct personas. Providing too much context can overwhelm agents, increase cost, and cause contradictions, so humans must learn context engineering. As AI becomes embedded in workflows, copying and pasting unverified outputs will increase, and domain expertise will be needed to distinguish correct from incorrect results and prevent errors reaching production.
"In 2026 and beyond, AI will not just change how software is developed, but change the very fundamentals of how people work. Far from being sidelined, senior engineers will become more important than ever. Not all is positive; the combination of agentic AI and Model Context Protocol (MCP) may accelerate software development, but they also broaden potential attack surfaces. Other developments on the horizon, including ambient AI, AGI, and breakthroughs in biotech, signal AI's profound impact not just on DevOps but society as a whole."
"Especially suited to more complex projects, context engineering will replace prompt engineering with a more structured approach to provide more accurate and targeted results. Context engineering involves factors such as which model to use, token limits, and linking to relevant data, apps, and systems, so it can be used to build AI teams to solve problems, with different agents assuming separate personas."
"However, feeding agents too much context can massively backfire. Apart from the cost, when presented with too many tokens, agents can become overwhelmed and confused, and may even start contradicting themselves. So, finding the right balance is essential, and that means humans prioritizing the learning of context engineering skills (as they have with prompt engineering)."
"As AI becomes even more embedded in work processes, the problems caused by people copying and pasting AI results and passing them along to colleagues without double-checking them will grow. We all know AI gets things wrong, and its results need verifying; otherwise, someone else along the line will have to do remedial work (or, even more worryingly, the slop escapes into production). However, this is not just about laziness. The problem is that people without deep domain expertise do not know the difference between good and bad AI output."
Read at DevOps.com
Unable to calculate read time
Collection
[
|
...
]