reliability.
9 writings found
Latest Archives
Why Your AI Agent Works Once But Fails the Next Time
Most benchmarks hide consistency problems behind averages. A new diagnostic tool exposes why capable agents produce unreliable results and how to fix it.
Why Your AI Agent Works Once But Fails the Second Time
Consistency gaps in LLM agents are hiding in plain sight. Why average accuracy masks unreliability, and how to measure what users actually care about.
Why Your AI Agent Succeeds 77% of the Time (But Only 53% Reliably)
Most agent benchmarks hide inconsistency behind averages. Here's why that gap matters and how to measure real-world reliability.
Why Your AI Agent Works Once But Fails the Next Time
Consistency gaps in LLM agents matter more than average accuracy. Introducing consistency guidelines to stabilize agent decisions.
Why Your AI Agent Works Once but Fails Twice
AI agents achieve high average accuracy but fail inconsistently on identical tasks. A new consistency measurement and guideline system reveals why, and how to fix it.
What GitHub's August Outages Reveal About Platform Resilience
GitHub experienced five incidents in August revealing infrastructure challenges. What this means for developers relying on CI/CD and the broader platform architecture debate.
GitHub's August Outage: What the 7-Hour Failure Teaches Us About Scale
GitHub's 7-hour August outage exposed critical scaling challenges. Here's what the incident reveals about reliability, growth, and the future of developer infrastructure.
What Zero-Notice Disasters Taught Meta About Building Unbreakable Systems
How Meta tests for instant power loss across entire data centers and what it means for developers.
What Meta's PowerLoss Storm Teaches Us About Building Resilient Systems
How Meta tests for zero-notice disasters and why it matters for every engineer building distributed systems.