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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11After 15 years in quality assurance, a layoff and a new role at an AI-agent startup left xulingfeng feeling like a beginner again. In a September 22, 2026, DEV Community post, the QA veteran describes unfamiliar workflows, the unease of contributing experience to an AI agent, and a rule for starting over: “understand first, judge second.” This is one person’s early-career reflection, not an independent assessment of the unnamed company or its process.
Why a veteran felt like a new hire
xulingfeng says their 15 years in QA included manual testing, automation, test development and test management. After being laid off, an old colleague contacted them about a tester opening. They offered themself as a candidate, met with the CTO and HR, and joined a young startup building AI agents for an undisclosed vertical market.
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The first days brought a familiar but unsettling feeling: “I have no idea what I’m doing.” The phrase did not mean the author had forgotten how to test. It reflected the challenge of entering a new team, learning its context and figuring out how work moved from idea to release.
Understand the workflow before judging it
The author says the new team worked through a product-to-requirements-to-development-to-test-to-ship sequence that felt unlike their previous workplace. At first, the process seemed messy. But unfamiliarity alone was not enough to show that it was wrong.
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One difference the author appreciated was a slower pace that gave testers more time to think and make decisions. These are impressions formed during the first two weeks, not measured findings about the company. The practical lesson is not to suspend judgment forever, but to learn what a process is meant to do before deciding whether it works.
“So my rule for these two weeks: understand first, judge second.”
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What the fictional Mark story says about AI and experience
The new role also sharpened a question the author had explored in a series of fictional stories about the 36 Stratagems. One character, Mark, is a veteran whose company extracts his experience into a skill and then lays him off. The story uses that premise to examine what can be lost when experience is treated as a collection of reusable answers.
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In the story, the skill reports 96.8% diagnostic accuracy across 312 historical failure scenarios. It then misses a 313th case: a 450ms retry-window compatibility shim, originally written for RabbitMQ and later applied to Kafka. The story says the original context was buried in an old migration note and surfaced at 4 AM. Those figures and events belong to fiction; they are not a real-world benchmark, verified incident or independent test of an AI system.
The author’s point is that a parameter is not the same as the reasoning behind it. In the fictional example, knowing that a retry window was 450ms is less informative than knowing why it was 450 rather than 300. The story says that rationale came from a postmortem but was never documented. That illustrates the author’s argument: useful expertise includes situational context and judgment, not just remembered outcomes.
“The AI didn’t fail because it was wrong. It failed because it was right about yesterday — and yesterday wasn’t running anymore.”
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That closing line is the author’s, spoken through the fictional story. It captures the risk of carrying forward a once-correct answer after the system around it has changed.
Human review matters, but humans miss things too
The post also complicates any simple contrast between fallible AI and reliable people. A reader noticed a one-year-versus-five-year timeline inconsistency in the Mark story—an error the author says they had missed despite rereading it more than 30 times. The reread count and anecdote are author-reported, but the example supports a modest conclusion: fresh human review can catch mistakes, while human attention is not infallible.
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The tension in turning experience into an agent
The author reports seeing two workplace slogans: “Your experience is waiting to be forged into an Agent.” and “Great employees get the work done. Great Agents keep getting it done.” Together, they express the appeal and unease of automation: experience can be made useful to a tool, but the worker sharing it may also wonder whether the tool could eventually replace them.
xulingfeng says they are curious about translating 15 years of judgment into an agent and want to learn hands-on. The post does not establish that the unnamed employer is replacing employees with agents, nor does it measure whether an agent can capture this author’s expertise. It describes a tension the author is choosing to explore rather than resolving it with a claim about AI’s capabilities.
The series on pause—and a paperback mention
At the time of the post, the author says 30 of the 36 Stratagems stories were complete, with the remaining six paused while they learned the new job. They intend to finish the series later. The post also names the older-series paperback AI, Ego & Regret and says it is available on Amazon; its current listing status has not been verified. It is mentioned as a book, not as a QA manual or recommendation.
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