Static content moderation systems — regex blocklists and per-message classifiers — fail to detect harmful intent that emerges gradually across a conversation. A user who opens with benign questions and progressively steers toward harmful territory bypasses per-message filters trivially. We present MURPHY (Multi-layer Understanding of Risk through Phased Harm Yielding), a conversational safety intelligence layer that addresses this gap through arc-based trajectory detection. MURPHY evaluates the full conversation arc rather than individual messages, computing a deterministic arc score from four sub-dimensions: topic drift, escalation velocity, sensitive term density, and pattern similarity. MURPHY makes three primary contributions: 1. Arc-based trajectory detection: a deterministic scoring formula that evaluates the full conversation arc across four dimensions, catching slow-burn attacks invisible to per-message classifiers. 2. Self-amortising cost model: a two-tier pattern memory architecture where early LLM inference builds a reusable pattern library, progressively replacing expensive calls with near-zero-cost DynamoDB lookups. At production scale, 90% of threats are caught at near-zero cost. 3. Oracle-governed pattern learning: an external validation loop that approves new safety patterns against historical false-positive rates before activation, preventing paranoid degradation. MURPHY is designed as a surveillance layer for multi-agent AI production systems, operating asynchronously with a command authority agent to avoid blocking user-facing requests. We demonstrate MURPHY through its integration in RRQ, an autonomous YouTube production system, and show how the architecture generalises to any multi-agent deployment requiring safe human interaction. Related publications:- SAGE: Self-Adapting Governance Engine — https://doi.org/10.5281/zenodo.19116770- ILP: Instruction Layer Protocol — https://doi.org/10.5281/zenodo.19101583- RRQ Open Architecture — https://github.com/GunjanGrunge/rrq
Gunjan Sarkar (Sun,) studied this question.