Horizon Fidelity Monitor MCP server

Horizon Fidelity Monitor

"Quality is not a model property — it is a conversation property."

Horizon is a real-time conversation health monitor for AI agents. It tracks the structural dynamics of multi-turn conversations — semantic drift, information gain, ontological gap width, temporal desynchronisation, circadian cognitive load, conversation velocity, and causal reachability — dimensions that LLMs do not reliably surface from inside the conversation.

Horizon ships two measurement planes. The conversation plane (above, always present) measures the health of a dialogue turn by turn. The optional mission plane — Memento Mori — measures elapsed calendar time against goals: ages, deadlines, stalls, per-entity latency, and share of a finite horizon. It is inert until you configure a store. See Mission plane.

Horizon is not a manipulation, sycophancy, or human-influence detector — it measures conversation dynamics, not whether an agent is steering or flattering the user. See LEGAL.md §1.

Why an external monitor? LLMs have limited and unreliable self-knowledge: introspection research shows partial self-access that is brittle and degrades on complex or out-of-distribution tasks (Binder et al. 2024; arXiv:2512.12411). So rather than depend on a model reporting its own conversation dynamics, Horizon measures them externally with cheap, deterministic, always-on arithmetic that does not call the model at all.

Why this exists

Multi-turn AI agents lose accuracy. The ICLR 2026 Outstanding Paper "LLMs Get Lost In Multi-Turn Conversation" (Laban, Hayashi, Zhou & Neville — Microsoft Research / Salesforce Research) reports 39% average accuracy degradation across multi-turn evaluation — a structural property that standard observability tools (LangSmith, RAGAS, DeepEval) cannot see because they measure responses, not conversations.

Horizon was built to close that gap. It is observability first: it surfaces conversation dynamics that response-level tools miss, using cheap deterministic arithmetic with zero model calls. In four controlled A/B scenarios where Horizon events drove a re-grounding intervention we measured a +15.7% composite quality lift and 87% fewer hallucination events — but those are synthetic, scripted scenarios with a hand-tuned controller, not a production result. Treat them as promising in-house evidence, not a guaranteed outcome (see Validation and LEGAL.md §5). Every signal — information gain, divergence, estimated ontological gap width, causal reachability — is a standard information-theory or arithmetic measure computed on text embeddings and timestamps; see 4D Spacetime Signals for the full definitions.

• Read the demand proof → ICLR 2026 Outstanding Paper (Laban et al.)
• Read the category argument → docs/content/naming-the-category-conversation-dynamics-monitoring.md
• Read the engineering case → docs/content/why-every-production-agent-needs-conversation-dynamics-monitoring.md

Getting started

Shortened. The full README is on GitHub.