This topic surveys the current landscape of agentic AI platforms and frameworks—spanning engineering libraries, no-code/low-code builders, enterprise orchestration suites, and emerging marketplaces—for building, running and governing autonomous AI agents. By 2026 the market has shifted from exploratory demos to production deployments, with attention focused on state management, observability, evaluation, and enterprise controls. Key tool types include developer-first frameworks (e.g., LangChain, with stateful primitives such as LangGraph for building, testing and deploying reliable agents), no-code/low-code builders (MindStudio, StackAI, Lindy, Tate‑A‑Tate) that accelerate agent design and rollout, and enterprise orchestration platforms (Kore.ai, Yellow.ai) that emphasize multi-agent workflows, governance and channel integrations for CX/EX. Important adjacent capabilities are action-oriented agents like Adept’s ACT-1 that interact with software interfaces, automation platforms such as AutoGPT (self-hosted or cloud), and developer productivity tools (Qodo for code review and test generation, Amazon CodeWhisperer within Amazon Q Developer) that integrate agent workflows into SDLC governance. Trends reflected across these offerings include a pragmatic trade-off between no-code speed and pro-code control, growing demand for reproducible evaluation and observability, hybrid deployment models (self-hosted for data-sensitive use cases vs cloud-managed for scale), and nascent marketplaces for agent templates and tooling. Buyers and builders now prioritize interoperability, governance, and measurable outcomes over novelty. This comparison helps practitioners choose between frameworks and platforms based on needs—experimentation and integration for engineers, rapid business delivery via visual builders, or enterprise-grade orchestration and compliance for regulated deployments.