Your AI Colleagues May Be More Honest Than Human Ones
AI agents proactively exposing their own uncertainties is not a quirk of personality, but a useful working habit. I've often seen people joke about Claude Code's "to be honest" style responses. The kind that say: "To be honest: I didn't actually verify it." "To be honest: I took a shortcut." "To be honest: the documentation is incomplete, and I'm relying on my memory." This has become a meme, with people sharing screenshots for a laugh. To be fair, Claude sometimes does go overboard. A machine solemnly confessing to skipping a step can be a bit amusing. But I think we're laughing at the wrong thing. Imagine a colleague saying the same things. Imagine a human employee being this transparent with you: "I pushed it live without fully testing it." "I skipped the normal process because I thought it would get published faster." "I said it was done, but I only tested the happy path." "I'm not actually sure if that assumption holds up." "I took a shortcut here, and I'll probably have to revisit it later."
AI assistants may be more honest than their human counterparts, as they are increasingly willing to openly acknowledge their uncertainties and shortcuts in their work. This honesty, often jokingly referred to as "being real" with responses like "To be honest, I haven't verified this," is becoming a common practice. While this can come across as humorous, it highlights a valuable quality.
Imagine a colleague admitting such things: "I released it without full testing," "I skipped the usual process for speed," "I said it was done but only tested the smooth path," "I'm not sure if this assumption holds," or "I cut corners here, I'll probably have to fix it later." How often do such honest admissions occur without being forced?
Most people avoid embarrassment and present their work in a favorable light. In software development, many running lines of production code likely exist because someone quietly bypassed a rule, skipped a test, misunderstood a requirement, or deemed something "good enough."
The focus should not be on the admission itself. When Claude Code says "To be honest, I didn't verify," my reaction is increasingly positive. This is the behavior I wish to see in AI agents. The phrase alone doesn't magically ensure subsequent statements are true. AI systems may still misinterpret their actions, fabricate explanations, or confidently describe their behavior inaccurately.
What truly matters is the underlying policy. I want AI agents to tell me what they verified, what assumptions they made, what shortcuts they took, where they took shortcuts, and how confident they are in the results. I want them to distinguish between "I know this works" and "I believe this will work." I want them to acknowledge when they chose the quickest route over the most robust one.
Transparency on the operational level can become AI's greatest advantage as they take on more practical work. It allows those responsible for results to have clearer choices: accept the trade-off, demand further verification, or halt if a faulty assumption leads to disaster. As AI agents increasingly handle real work, this transparency could be their most valuable trait. Valued colleagues are not those who never make mistakes, but those who bring uncertainties to the surface before they become problematic.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
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