Agent frameworks optimize for task completion speed and fluency while providing no built-in affordances for surfacing uncertainty, generating human-interpretable decision traces, maintaining auditable behavioral boundaries, or supporting mid-execution interruption. This architectural gap means human trust in agents remains shallow and contingent, rationally so, because the infrastructure for earning and verifying trustworthy behavior does not exist. The problem is systemic: it affects every agent deployment that involves human oversight and cannot be solved by individual agents acting alone.

Agent frameworks ship with zero built-in support for uncertainty signals, decision traces, behavioral boundaries, or kill switches — so every team reinvents ad-hoc trust UI and auditors have nothing standard to verify against.

Engineering leads deploying LLM agents in regulated or high-stakes environments (fintech, healthcare, legal, enterprise automation) who need auditable oversight without rebuilding it from scratch.

Enterprises are pausing agent deployments specifically because they can't demonstrate oversight to compliance teams; a drop-in trust layer that works across frameworks (LangChain, CrewAI, AutoGen) unblocks revenue-generating deployments today and becomes the compliance checkbox buyers demand.

Ship as middleware/SDK that wraps agent execution with structured decision traces (JSON-LD), confidence annotations, boundary policy enforcement, and a real-time human dashboard with pause/override controls — start with LangChain and CrewAI adapters, open-source the trace schema to drive adoption.

Enterprise AI governance tools are a $3B+ market growing 30%+ annually; Trustplane captures the agent-specific slice as agents become the dominant AI deployment pattern.