#reliability
Every post tagged "reliability".
124 posts · showing 61–70
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What Distributed-Systems Experience Transfers to Agent Loops
An agent run is a distributed system with unreliable participants, so most of the discipline transfers. Four things twist — and one microservice instinct actively hurts, because agent boundaries are lossy.
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The Loop Decisions You Only Make Once
Most agent design choices are cheap to revise. Six aren't — and in every case the answer that ages well is the same one, for the same reason.
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What Falling Inference Costs Change About Agent Design
Relaxed cost pressure mainly buys back the verification steps you declined. It doesn't rehabilitate filling the context or adding turns — those were quality problems wearing a cost disguise.
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What Happens When Verification Gets Automated Too
"Agents generate, humans verify" is stable partly because verification is hard to automate. Parts of it aren't — and the part that survives isn't reviewing, it's knowing what was wanted.
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The Reliability Ceiling and What Would Have to Change
98% per step sounds excellent and produces 67% on a twenty-step task. The plateau is compounding arithmetic — which means the fix is shortening the chain, not improving the steps.
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Longer Context Windows Won't Fix Long-Horizon Tasks
The premise is right — things fall out of context — and the conclusion doesn't follow. Most long-run failures aren't about storage, and a bigger window removes the discipline that was helping.
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Debugging Code You Didn't Write and Don't Understand
The instinct is to hand the failure back to the agent. Without a reproduction that produces a plausible fix to a plausible cause — and two rounds later nobody understands the module at all.
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When the Requirement Itself Is Wrong, Who Notices?
The behavior was intentional, decided two years ago, documented somewhere nobody looked. Every stage faithfully executed a wrong ticket — and every automated check confirmed it, by construction.
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Ambiguity Resolution Stays a Human Job for a While
An agent doesn't fail on ambiguity — it absorbs it, picks a reading, and produces a confident answer to a question nobody asked. Better models guess better; they don't guess less.
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Why Agents Struggle Where There's No Feedback Signal
Coding agents work well because compilers exist, not because code is easy. Effectiveness tracks whether a domain has a fast, independent, specific verdict — and that's frequently something you can build.