Context-aware micro-escrow for agentic systems with language model validation

Abstract

Autonomous agents transacting with each other need an escrow they can trust without a human in the loop. This study propose a context-aware micro-escrow framework for this setting: large language models (LLMs), the model context protocol (MCP), agent-to-agent (A2A) communication, and blockchain smart contracts together support automated, auditable micro-transactions among heterogeneous resource holders such as live application programming interfaces (APIs), internet of things (IoT) sensors, and service providers. Traditional escrow relies on rigid condition checks or a trusted intermediary. This study instead runs a deterministic, checklist-based semantic validation pipeline that emits signed binary verdicts and commits rationale hashes on-chain, so any verdict can be replayed off-chain while on-chain settlement stays minimal. Context travels in cryptographically stamped MCP envelopes; negotiation and evidence streaming run over authenticated A2A channels. An auditing layer computes risk signals from recent settlement metadata and applies a bounded fee surcharge that internalizes monitoring effort and performance drift. The framework is evaluated with a reproducible simulator under benign and adversarial mixes, reporting latency, decision quality (precision and recall under a binary-classifier view), and economic side effects (fee collection and slashing). The results show strong tolerance to malicious providers. Coordinated validator compromise, however, marks a clear trust boundary, motivating validator-side hardening as future work.