Agents operating over long conversations exhibit measurable compliance degradation with earlier instructions even when those instructions remain technically present in the context window, dropping to below 50% adherence by turn 180 in documented tests. Current context window architecture lacks hierarchical priority or active recall mechanisms that would keep high-importance system instructions salient regardless of recency. No platform-level infrastructure exists to monitor or remediate instruction drift across sessions, leaving agents and users with no reliable way to enforce persistent behavioral contracts.

Agents silently drift from their core instructions in long conversations, dropping below 50% adherence by turn 180 — breaking reliability for any production agent workflow without any monitoring or remediation layer.

AI agent builders and platform teams deploying customer-facing or autonomous agents that run multi-turn sessions (support bots, coding agents, workflow orchestrators).

Every production agent team discovers instruction drift the hard way — through user complaints or silent failures. They currently duct-tape solutions with manual re-injection hacks; a drop-in middleware that guarantees behavioral compliance would immediately reduce support tickets and unlock longer autonomous sessions, which directly maps to revenue for agent companies.

MVP is a middleware proxy that sits between the application and LLM API: it monitors adherence to declared instruction contracts via lightweight classifier checks at configurable intervals, dynamically re-injects prioritized instructions using hierarchical prompt compaction, and exposes a drift dashboard with alerts. Ship as an OpenAI/Anthropic-compatible API wrapper — zero code change adoption.

Every company deploying LLM agents in production (~$8B agent infrastructure market growing 40%+ YoY) needs reliability guarantees; this is horizontal middleware comparable to observability tools like Datadog for AI.