AI agent platforms and frameworks are the tooling layer for building, running and governing autonomous or semi‑autonomous applications that coordinate large language models, tool use and external systems. In 2025 the landscape spans four categories: Agent Frameworks (development libraries and state management like LangChain and its LangGraph primitives), AI Automation Platforms and autonomous agent runtimes (AutoGPT, AgentGPT, Agentverse), AI‑native IDEs and developer platforms (Windsurf/Codeium, Replit, Tabnine, Tabby), and marketplace/discovery layers where agents can be listed and monitored. This topic is timely because agentic workflows have moved from experimental demos into production use: teams demand composability, stateful orchestration, multi‑model support, and enterprise features such as private/self‑hosted deployments, observability and governance. Key tradeoffs now drive platform choice — openness and extensibility (LangChain, open-source Tabby), no‑code accessibility (AgentGPT, Agentverse marketplaces), integrated developer flow and multi‑model editing (Windsurf, Replit), and enterprise controls for security and compliance (Tabnine, self‑hosted AutoGPT variants). Anthropic’s Claude family and platform assistants like Microsoft 365 Copilot and GitHub Copilot exemplify how assistant capabilities are being embedded into productivity tooling rather than only into standalone agents. When comparing platforms look at state management, model-agnostic integration, deployment options (cloud vs on‑prem), monitoring and evaluation tooling, and marketplace ecosystems for reuse and monetization. The result: a pragmatic landscape where interoperability, safety controls and developer ergonomics determine whether an agent is a lab prototype or a reliable production service.