From cloud platforms to developer frameworks, the following companies are leading this shift from AI that regurgitates information to AI that gets work done.
Agentic AI Platforms: Claude, Claude Code
Anthropic is primarily known for its Claude family of AI models and chatbot, but it has expanded beyond conversational AI with agentic tools that can reason through complex tasks and take action across connected systems. Through platforms like Claude and Claude Code, the company enables developers and enterprises to build AI agents capable of writing and reviewing code, conducting research, analyzing documents, using external tools and completing multi-step workflows.
Agentic AI Platforms: ChatGPT, Codex
OpenAI develops the GPT family of AI models and ChatGPT, whose more than 900 million weekly active users have established generative AI as a mainstream technology. The company’s APIs, Agents SDK and native model context protocol are now diversifying ChatGPT from a chatbot company into a full-fledged agentic AI platform, with capabilities like writing and debugging code, conducting deep research, browsing the web and shopping.
Agentic AI Platforms: Webflow AEO, Webflow MCP
Webflow provides a visual website development platform that lets businesses design, publish and manage professional websites without traditional coding. Used by more than 300,000 organizations, the company is moving beyond no-code development with its agentic AI tool, Webflow AEO, which can generate site elements, create pages from natural language prompts and automate content management workflows. Plus, its Webflow MCP (short for Model Context Protocol) server integration connects external AI agents like Claude and ChatGPT directly to Webflow’s design systems and CMS.
Agentic AI Platforms: Vellum for Agents
Vellum gives engineering teams a dedicated sandbox to backtest AI agents before deploying them. Its platform tracks prompts, flags quality regressions, runs evaluations across AI workflows and monitors production behavior so developers can ship AI applications with greater confidence.
Agentic AI Platforms: Amazon Bedrock
Amazon Bedrock provides developers with a single platform for building custom AI agents off of prebuilt foundation models from Amazon, Anthropic, Meta and other major providers. Running on AWS — the world’s largest cloud platform that powers nearly a third of all enterprise cloud networks — Bedrock lets companies deploy their own agentic apps without getting caught up on mode development.
Agentic AI Platforms: Vertex AI Agent Builder
Google Cloud’s answer to agentic AI is Vertex AI Agent Builder, where companies can build using Gemini-powered foundation models. It also connects those agents to Google’s enterprise search and business data. The platform pairs the company’s AI research with its well-established global infrastructure behind products used by more than 3 billion people.
Agentic AI Platforms: Workforce
Relevance AI provides a visual drag-and-drop canvas for building custom AI teams without an engineering queue. Organizations pick it over single-bot builders and developer frameworks for its native multi-agent task delegation, model-agnostic flexibility and handy security guardrails like human-in-the-loop approvals.
Agentic AI Platforms: LangGraph, LangSmith
LangChain gives developers the building blocks to connect AI models with databases, APIs and business software. Its two companion platforms, LangGraph and LangSmith, add the orchestration needed for long-running, stateful AI agents and the testing, debugging and monitoring capabilities needed to deploy them, respectively. With more than 1 billion downloads across its Python and JavaScript libraries, this ecosystem has become the standard toolkit for building custom AI agents.
Agentic AI Platforms: Agent Builder, Maestro, Autopilot
UiPath combines AI agents with robotic process automation to streamline work across both modern applications and legacy systems. With 10,000 customers — including more than half of the Fortune 500 — the platform is helping AI-forward enterprises move beyond rule-based automation toward systems that can reason through messy business operations.






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