As more software engineers use AI agents daily, there’s also more sloppy software, outages, quality issues, and even a slowdown in shipping velocity. What’s happening ?

When it comes to AI agents and AI tooling, most of the discussion focuses on their potential boosts for efficiency, faster iteration, and the pushing out of more code, faster.

Last month, the Pragmatic Engineer newsletter took an inside look into how Uber is adopting AI, internally. The rideshare giant has built close to a dozen internal systems to deal with code generated by AI agents. However, when quantifying the impact of AI, the focus was on how much output has increased, and how devs who use more AI also generate more pull requests ; these are the “power user” devs who generate 52% more PRs than devs who use AI less. There was no mention of product quality – at all !

And there are signs that product quality is dropping overall. Here are some some problems that come which the usage of AI for example :

Anthropic : degraded flagship website. An annoying UX issue irritated paying Claude customers – and no one at Anthropic noticed. The company moves very fast, generates 80%+ of production code with Claude, but quality and user experience seem to be taking a backseat.

Amazon : AI-agent reliance triggers SEVs. Amazon’s retail org has a leap in outages caused by its own AI agents. Now, senior sign off is needed for junior engineers’ AI-assisted changes.

Big Tech : “use AI or you’re unproductive.” Companies like Meta and Uber are tracking AI token usage in performance reviews, putting pressure on engineers to use it heavily — irrespective of the tools’ quality impact.

OpenCode : more time spent cleaning up. Dax Reed, OpenCode’s creator, warns that AI agents are lowering the bar for what ships, discouraging refactoring, and don’t speed teams up.

Startups : founders see LLMs slowing down long-term velocity. Sentry’s CTO and others observe that while AI removes the barrier to getting started, it also produces bloated, hard-to-maintain code that slows long-term development.

Research : AI agents underperform claims. Some studies show AI coding tools produce short-lived velocity gains followed by significant tech debt increases.

In this situation Engineers with strong architectural sense become more critical than ever, proposed solutions include formal validation methods, and perhaps reviving some old school QA ideas.

Anthropic seems to be prioritizing moving very fast over doing so with high quality. There is no denying that the company is moving at incredible speed and running laps around competitors. A good example is how they built Claude Cowork in just 10 days. Claude Cowork handled work with Microsoft Word and Excel documents surprisingly well, to the point that it set off a “code red” inside Microsoft’s Office division.

Microsoft responded as fast as possible, but it still took 2-3 months to launch their (cloned) response, called Copilot Cowork earlier in March 2026, with full access still to follow soon.

In the case of Anthropic, moving fast with okay quality seems to make good business sense : they build a better product than what already exists, so no matter if it’s a bit rough around the edges ; they can fix quality issues post-launch and still be months ahead of the competition.

During a meeting at Anthropic, This meeting was the regular “Last month in Stores Tech” operational one, but what was new was the note telling staff to attend this “optional” meeting, and the mandate for senior engineers to sign off code changes from juniors. The outages may have been caused by less experienced engineers over-trusting GenAI’s output. Also, there were incidents caused by AI changes.

Separately, the company’s cloud computing arm — Amazon Web Services — has suffered at least two incidents linked to the use of AI coding assistants, which the company has been actively rolling out to its staff.

AWS suffered a 13-hour interruption to a cost calculator used by customers in mid-December 2025 after engineers allowed the group’s Kiro AI coding tool to make certain changes, and the AI tool opted to « delete and recreate the environment ».

Again, a tool causing an outage is not its own fault : it’s on the engineer who lets the tool run wild. If I delete two lines of code, then push it to production, and the server crashes, the fault is not with the text editor or the Git client, but with me who made the change. Similarly, if you prompt an AI agent to do something, and the AI agent goes off and does its stuff which causes an outage, then responsibility lies with the engineer who didn’t set up guardrails for the agent.

Inside large tech companies, it’s becoming a career risk to not use AI at an accelerated pace, regardless of output quality. These large companies are the ones likely to be mulling layoffs, like Meta reportedly preparing to cut up to 20% of staff. And when it comes to identifying redundancies, it’s a fair assumption that things like “AI usage” and “pull requests per engineer” will be taken into account, especially as one theme of such layoffs will almost certainly be that the employer wants to focus more on AI.

So, it’s common sense (and self-preservation) to use more AI, if only not to be seen as unproductive. Their perceived output will rise and engineering leadership will share more reports about productivity being up, and interpreting more code generated and more pull requests as the proof.