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Vessel Guard-AI Based Human Factor Risk Detection
Vessel Guard is an AI-assisted maritime safety system designed to prevent human-error-related accidents caused by fatigue, stress, and reduced operational readiness onboard vessels. Life at sea exposes crew members to shift work, sleep fragmentation, vibration, isolation, and high-risk tasks in deck, engine, and bridge operations. While many shipping companies provide access to teletherapy with reactive tools and disconnected from daily operational safety decisions. They do not integrate fatigue thresholds, maritime regulations, or structured escalation to command. Vessel Guard addresses this by combining short daily crew check-ins with a deterministic rules engine based on MLC fatigue standards and wellbeing scales. The system translates human factors into operational risk levels ) and escalates only high-risk cases to the Captain or designated officer. The AI layer does not diagnose or classify risk. It extracts structured data and generates safety-focused summaries, while final decisions remain human-controlled. This human-in-the-loop design ensures regulatory alignment, privacy protection, and institutional credibility. Vessel Guard is designed for maritime sector seeking proactive duty-of-care compliance, improved safety culture, and measurable reduction of fatigue-related incidents—without adding administrative burden to crew.
15 Feb 2026
Trustless AI Agent for Pay-Per-Task on Arc
Trustless AI Agent is a demonstration of agentic commerce, showing how autonomous AI systems can transparently price tasks and settle payments in USDC on the Arc blockchain using Circle’s developer infrastructure. The project addresses a common problem in AI services: pricing trust. Users often do not know how much a task should cost, while developers struggle to implement fair, usage-based billing. This challenge becomes more important in agentic systems, where AI agents act autonomously and interact with financial infrastructure. In this system, a user submits a task through a simple frontend. The backend calls Gemini to analyze the input and classify its complexity as LOW, MEDIUM, or HIGH. Based on this classification, the AI suggests whether the task should be free or paid and proposes a USDC amount. To keep the system trustless, the AI never directly controls payments. The backend enforces deterministic guardrails using a fixed pricing table. Even if the AI output is incorrect or attempts to overcharge, the backend corrects it before any transaction occurs, ensuring predictable and auditable behavior. If payment is required, the backend executes a USDC transfer using Circle Developer-Controlled Wallets, settling the transaction on Arc Testnet. Wallet balances can be queried to verify that funds were transferred correctly, demonstrating real onchain settlement. Circle Product Feedback This project uses Arc Testnet, USDC, and Circle Developer-Controlled Wallets. Circle Wallets enable secure, programmable wallet management suitable for autonomous agents, while USDC provides stable pricing for pay-per-task models. Wallet creation, balance checks, and transfers worked well. The main challenge was limited Arc-specific end-to-end examples. More agent-focused reference implementations would improve the developer experience.
24 Jan 2026
