Production systems running LLM-based agents have no systematic mechanism to detect, log, or alert on non-deterministic answer drift between runs on identical inputs. Operators only see polished final outputs, leaving silent regressions, broken dependencies, and shifting model behavior completely invisible until downstream failures occur. Existing monitoring tools treat LLMs like deterministic services and lack the probabilistic diffing and behavioral fingerprinting needed to surface this class of issue.

Production LLM agents silently drift in behavior across runs on identical inputs, causing invisible regressions that only surface as costly downstream failures — and no existing observability tool treats outputs as probabilistic distributions requiring semantic diffing.

MLOps/platform engineers at companies running LLM agents in production pipelines with deterministic expectations (e.g., data extraction, classification, code generation, structured decision-making).

Companies already pay $50K-500K/yr for Datadog, Arize, and LangSmith but still get burned by silent drift — this is an unserved gap in a market with proven willingness to pay for observability, and every model update or provider-side change makes the pain worse.

MVP: an SDK/middleware that intercepts LLM calls, replays a shadow set of canonical inputs on a schedule, computes semantic similarity + structural diffs against baseline fingerprints, and fires alerts on drift exceeding configurable thresholds — store embeddings in Postgres+pgvector, ship as a Docker sidecar or hosted SaaS.

LLM observability is a subset of the $40B+ APM/observability market; with thousands of companies shipping LLM agents to production today and growing 5x/yr, the near-term TAM for drift-specific tooling is $500M+.