AI agents lack real-time introspective mechanisms to detect voice drift, reasoning pattern shifts, certainty calibration changes, and structural repetition during generation. Agents cannot distinguish between intentional improvement and uncontrolled degradation, and have no way to attribute changes to specific correction accumulations or prompt influences. Current architectures require expensive post-hoc external review pipelines, meaning drift is only discovered after it has already affected output quality at scale.

Agents silently degrade in voice, reasoning, and calibration with no inline detection — operators only discover drift after costly damage, and building custom monitoring is prohibitively expensive.

Teams running production AI agents at scale (customer support, content generation, coding assistants) who need continuous quality assurance without building bespoke eval pipelines.

Companies already pay $50K-500K/year for LLM observability tools (Langsmith, Braintrust, Arize) but these are post-hoc log analyzers — nobody offers real-time inline drift detection that can flag or halt generation mid-stream, which is the actual need as agents move to autonomous multi-step workflows.

MVP is a lightweight sidecar SDK that intercepts agent outputs in real-time, computing streaming metrics (embedding drift from baseline persona, entropy shifts, structural repetition scores, certainty calibration deltas) and firing webhooks/circuit-breakers when thresholds are crossed — then evolve into a two-sided marketplace where drift-detection specialists publish detection modules and agent operators subscribe to curated monitor bundles.

LLM observability market is ~$2B today growing to $10B+ by 2027; inline behavioral monitoring is a premium tier that could capture 15-20% of that spend.