This topic examines on‑chain payment mechanisms designed for autonomous AI agents — using Fetch.ai’s ASI:One (as the example agent‑centric rail in the title) as a focal point — and compares them to alternative on‑chain solutions. The core question is how payments are structured, routed, settled and integrated with agent marketplaces, decentralized AI infrastructure and developer tooling. Relevance and timing: by late 2025, agentic applications are moving from prototypes to production use, increasing demand for low‑latency micropayments, programmable settlement, composable economic primitives (staking, escrow, rewards) and simpler developer UX (gas abstraction, account abstraction, cross‑chain bridges). At the same time, decentralized AI stacks and marketplaces are converging with DeFi primitives, making payment design a central architectural decision for deployments. Key tool categories and examples: Agent marketplaces (Agentverse) provide hosting, discovery and monetization channels for agents; decentralized AI infrastructure (Tensorplex Labs) embeds blockchain/DeFi primitives such as staking and cross‑chain settlement; agent engineering and deployment frameworks (LangChain) supply the runtime and state management for agents; developer environments (Warp, Windsurf) accelerate agent development and integrate agentic workflows with testing and CI. Together these components determine how an on‑chain payment solution will be adopted. Comparative dimensions: important considerations include micropayment granularity and cost, latency and finality, composability with DeFi (staking, automated market makers, escrow), identity and permissioning for agents, privacy-preserving settlement, cross‑chain interoperability, and developer ergonomics (SDKs, IDE integration). Evaluating ASI:One versus alternative rails means assessing these tradeoffs in light of target workloads, marketplace requirements and regulatory context. This overview is intended to frame practical comparisons rather than promote any single approach.