This topic examines agent platforms used to automate enterprise workflows, contrasting multi‑agent systems (typified by Fujitsu’s enterprise‑grade orchestration approaches) with cloud‑provider and research‑lab agent offerings (such as those from Anthropic and Google). It covers two intersecting categories: AI Agent Marketplaces—venues to list, buy, and monitor agents—and Agent Frameworks—engineering stacks to design, test, and deploy agentic applications. Enterprise multi‑agent systems emphasize orchestrated, stateful agents, on‑prem or hybrid deployment, governance, and integration with existing enterprise systems. Cloud vendor offerings tend to focus on scalable hosted services, safety guardrails, and managed integrations with large language models and observability. Key tools and roles from the current landscape include: LangChain (engineering frameworks and LangGraph for stateful, testable agent applications); Agentverse (cloud platform and marketplace for listing and monitoring agents); AutoGPT (open autonomous agent workflows for self‑hosted or cloud deployment); ADEs like Warp and agentic IDEs such as Windsurf, Replit, Cursor, and JetBrains AI Assistant that embed agents into developer workflows; Continue for Continuous AI automation across GUI/CLI/IDE modes; and developer assistants—GitHub Copilot, Blackbox.ai, Qodo—that add coding, testing, and governance capabilities. As of 2025‑12‑14 this comparison is timely: enterprises are moving from experiments to production, demanding observability, reproducible state, multi‑model support, and regulatory compliance. The practical choice involves tradeoffs between control (multi‑agent, on‑prem) and convenience (managed cloud agent services), plus the need for agent marketplaces, evaluation tooling, and integrated developer workflows to deliver reliable, auditable automation.