# It's time to be right.

DevFeed: [It's time to be right.](<https://devfeed.tech/articles/it-s-time-to-be-right-12594.md>)

Original publisher: [Read original article](<http://brooker.co.za/blog/2026/04/30/be-right.html>)

Author: Marc Brooker

Published: 2026-04-30T00:00:00Z

Content type: opinion

Language: en

Sources: [Marc Brooker's Blog](<https://devfeed.tech/sources/marc-brooker-s-blog.md>), [Marc Brooker's Blog](<https://devfeed.tech/sources/marc-brooker-s-blog-2.md>)

Topics: [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Software Engineering](<https://devfeed.tech/topics/software-engineering.md>), [Software](<https://devfeed.tech/topics/software.md>), [debugging](<https://devfeed.tech/topics/debugging.md>)

Tags: [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [development](<https://devfeed.tech/tags/development.md>), [software](<https://devfeed.tech/tags/software.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>)

## AI overview

The article argues that the future opportunity for agentic AI in software development and knowledge work will be constrained more by defect rate than by capability. It examines defects by frequency and seriousness, describing how agents can work around gaps in their underlying models. Lower-consequence uses, including one-off scripts, experiments, tools, and basic UIs, are presented as a substantial opportunity, while consequential work requires much stronger reliability and expert oversight.

## Source excerpt

It's time to be right. Outcomes continue to matter. Earlier this week, I spoke at AI Dev 26. This is what I spoke about there. I've been making money, in some form, building software for nearly 30 years. The last five months have been the most exciting of that entire time. I'm extremely optimistic about the future of software, and the future of software engineering as a field. But I have a hypothesis about agentic AI for development, and for knowledge work broadly: in future, the size of the opportunity for agentic AI will be more limited by defect rate than capabilities. Let's break that down a little bit, by thinking about defects along two axes: how serious the defects are, and how frequent they are. These axes intentionally conflate two inputs--how hard the problems are and how capable agents are at solving them--and focus only on the output that matters most: user-experienced defects. We're also focusing on outputs from an AI agent here. Agents are feedback loops. Feedback loops, just like in electronics and control theory, can have significantly different capabilities from their underlying components. In simpler terms, agents can work around model gaps very effectively1. Simplifying further, we'll arrange these axes into a kind of four-blocker, and think about the kinds of people that would use an agent in each block. High defect frequency, high defect seriousness. Basically nobody. Except maybe a small set of true believers and early adopters. If an agent is making highly consequential mistakes often, it's simply not going to be useful to a lot of folks. High defect frequency, low defect seriousness. People working on problems where slop is OK. This is a larger opportunity, because slop is OK fairly frequently. If I'm using an agent for low consequence stuff, like summarizing an email about this fall's soccer league, it's likely to do a better job than I'd do by skimming. And, as much as it tends to hurt our sense of professional pride, there's also a huge oppo