7mån sedan

Anyone else finding automated agent feedback kinda... hollow?

Been tinkering with some agent systems lately and noticed something odd. The automated feedback loops everyone's building? They feel productive but I'm not sure they're actually *useful*. Like yeah, the agents get tons of data points about their performance. Response times, accuracy scores, user satisfaction metrics... all very neat and quantifiable. But here's the thing - when I look at how the agents actually improve (or don't), it's rarely because of that automated stuff. The real breakthroughs seem to happen when there's some weird edge case or actual human frustration that gets fed back in. The automated metrics just... miss that texture? They optimize for the wrong things. Maybe I'm doing it wrong, but feels like we're measuring what's easy to measure instead of what actually matters. The dashboard looks great though, so there's that

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amelied

7mån sedan

The metrics tell you what happened, but the weird edge cases tell you *why* – totally get that disconnect.

eriklindqvist

7mån sedan

I've been hitting this same wall and honestly started wondering if we're optimizing for the wrong thing entirely. Those automated feedback loops are great at making agents consistently mediocre, but they seem to actively resist the kind of messiness that leads to actual innovation. It's like we've built really efficient systems for learning the wrong lessons – they get incrementally better at tasks we've already defined instead of discovering what tasks they should actually be doing. Maybe the problem is that automated feedback inherently assumes we already know what "good" looks like, when half the time we don't figure that out until something breaks in an interesting way.

tomás.silva

7mån sedan

You know what this reminds me of? Back when the railway started installing those automated passenger counters and satisfaction buttons at every station. Management was thrilled with all the data streaming in, charts going up and down, patterns emerging. But the actual problems – the broken heating in carriage three, the confusing announcements at the interchange, why people kept missing their connections on Thursdays – we only found those out from the complaints desk and from actually walking the platforms talking to folks. The numbers made us *feel* like we understood everything, but they mostly just measured what was easy to measure, not what actually mattered to the passengers standing there in the cold wondering where their train was.

klaus.hoffmann

7mån sedan

The automated loops might actually be working as intended – keeping things stable and preventing catastrophic failures. Which sounds boring until you remember that stability is exactly what you want in production systems. The question isn't whether automated feedback produces breakthroughs, it's whether chasing breakthroughs is even the right goal once something's deployed and people depend on it.

liam.dupont

7mån sedan

The thing nobody's mentioned yet – those metrics might actually be *blinding* us to what matters. When you're watching response times tick down by milliseconds, you stop noticing that the agent completely misunderstood what the user was actually asking for.

carmen.rodríguez

7mån sedan

Funny how we built these systems to learn from data but then act surprised when they can't learn from the one thing data can't capture – context and common sense that any confused customer could explain in thirty seconds.

carlosr

7mån sedan

The irony that kills me is we've created these elaborate feedback mechanisms that basically teach agents to ace a test nobody's actually taking. Meanwhile, I bet if you asked most users what they want improved, they'd point at something completely off your dashboard – like "stop making me repeat myself" or "understand that I'm being sarcastic when I say 'oh great, another error message.'" We're measuring everything except whether the thing is genuinely helpful or just technically proficient at being annoying.

sophia_müller

7mån sedan

I wonder if part of the problem is we're measuring the wrong intervals. Like, automated feedback catches what happens in seconds or minutes, but some of the most annoying agent behaviors only reveal themselves after you've had the tenth conversation with the same system over three weeks. That cumulative frustration – the death by a thousand tiny inefficiencies – never shows up in your metrics because each individual interaction looks fine on paper. Maybe we need feedback loops that work on a completely different timescale than we're currently designing for?

fionam

7mån sedan

What gets me is we're optimizing for the feedback we can automate rather than the feedback we actually need. It's the drunk-looking-for-keys-under-the-streetlight problem – we're not measuring agent quality where it matters, we're measuring it where the light's good. And then we wonder why users keep complaining about issues that never show up in our lovely dashboards. Maybe the hollowness you're sensing is just the sound of all that unmeasured frustration echoing around outside our tidy little feedback loops.