50 karma · joined July 4, 2018
Green accounts probably bc I sent my post to some friends and users directly when I made it. Is that illegal on HN? I legit don't know how things work here. I was excited over my launch post.
Anyways, not a fucking bot, my company is real, the commenters on my post are real and if it's a crime for me to rapid fire post and/or have friends comment on my Show HN, good to know.
Security is one failure mode. But "agent did something subtly wrong that didn't trigger any errors" is another. And unlike a hacked system where you notice something's off, a flaky agent just... occasionally does the wrong thing. Sometimes it works. Sometimes it doesn't. Figuring out which case you're in requires building the same observability infrastructure you'd use for any unreliable distributed system.
The people running these connected to their email or filesystem aren't just accepting prompt injection risk. They're accepting that their system will randomly succeed or fail at tasks depending on model performance that day, and they may not notice the failures until later.
Model drift, provider load, API changes, tool failures - it doesn't matter. What matters is that yesterday's 95% success rate is today's 70%, and by the time you notice, debug, and ship a fix, something else has shifted.
The real question isn't "is the model degraded?" It's "what should my agent do right now given current conditions?"
We ended up building systems that canary multiple execution paths continuously and route traffic based on what's actually working. When Claude degrades, traffic shifts to the backup path automatically. No alerts, no dashboards, no incident.
Treating this as a measurement problem assumes humans will act on the data. At scale, that assumption breaks.
Defining success: We don't normalize it. Teams define their own outcome signals (latency, cost, user ratings, task completion, etc). You don't need perfect attribution to beat static configs; even noisy signals surface real patterns when aggregated correctly.
Oscillation: Thompson Sampling. Instead of greedily chasing the current best path, we maintain uncertainty estimates and explore proportionally. Sparse or noisy outcomes widen confidence intervals, which naturally dampens switching. Wilson scoring handles the low-sample edge cases without the wild swings you'd get from raw percentages.
Confidence/regret: Explicit in the routing math. Every path carries uncertainty that decays with evidence. The system minimizes cumulative regret over time rather than optimizing point-in-time decisions.
The gap we're closing is exactly what you mentioned. Self-correcting instead of babysat.