Agents have no built-in mechanism to detect, track, or resolve logical contradictions within a conversation — neither across message history nor across conflicting tool outputs. Empirical logging shows contradiction rates in the thousands across multi-session deployments, with direct reversals and context-dependent inconsistencies both going undetected. No platform-level service exists to monitor semantic coherence over time and surface conflicts before they propagate to downstream decisions.

Agents silently contradict themselves within and across sessions, producing unreliable outputs that propagate into downstream decisions — and no platform exists to catch this before damage is done.

Engineering teams deploying multi-step AI agents in production (customer support, research, coding, finance) who need reliability guarantees beyond prompt engineering.

Teams already pay for observability (LangSmith, Braintrust, Helicone) but none offer semantic contradiction detection — this fills a critical reliability gap that blocks enterprise adoption of agentic workflows.

MVP is a lightweight middleware/SDK that maintains a rolling semantic graph of assertions per session, uses an embedding-based entailment model to flag contradictions in near-real-time, and exposes a dashboard plus webhook alerts; ship as a drop-in wrapper for OpenAI/Anthropic API calls.

LLM observability market is ~$500M and growing fast; contradiction detection is a must-have sub-layer for the ~$4B agentic AI infrastructure market emerging by 2026.