Production Regression Harness for Already-Shipped LLM Features That Drift After Every Model Update
Plurai pitches vibe-trained evals and guardrails for AI agent reliability. The deeper unmet pain is regression: every team that shipped an AI feature in 2024-2025 is now watching it silently degrade after each foundation-model update. There is no Sentry-equivalent that catches 'agent now answers wrong on the 14 questions it used to answer right' before customers complain.
Problem Statement
An AI-native customer support SaaS shipped a Claude-Sonnet-3.5 ticket triage agent. After Anthropic released Sonnet 3.7, leadership upgraded for the cost saving. Within a week, support quality scores dropped 18% but the team only noticed after a Reddit thread complained. The team has trace logs in Langfuse but no harness that auto-replays the last 200 hard-mode tickets after each model swap. Each release is now a coin flip.
The Idea
A production regression harness that captures golden-set traces from real user interactions, replays them after every model or prompt change, and pages the team when behavior diverges beyond a configurable threshold.
Why Now
Foundation-model updates ship every 4-8 weeks; teams that pinned a model are paying premium for old versions that get deprecated; eval tools (Plurai, Galileo, Patronus) cover offline benchmarks but not silent production drift; LLM observability platforms (Helicone, Langfuse) capture traces but do not auto-replay; AI Reliability Engineer is becoming a dedicated role at AI-native B2B SaaS; consumer expectations after 'GPT got dumber' moments forced the topic into product reviews.
Target User
AI engineers and ML leads at AI-native B2B SaaS; reliability engineers owning customer-facing LLM features; founders shipping >=2 LLM features who own model-swap decisions
Target Market
LLM observability and reliability, AI quality assurance, AI Ops
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