This topic covers multimodal and agentic vision models and the platforms that operationalize them: large multimodal models (vision+language), developer frameworks that compose agents, enterprise brand/voice agents, edge-optimized vision platforms, and marketplaces that distribute agents and tools. It’s about how vision-enabled reasoning and autonomous agent behavior are integrated, deployed, and governed across cloud, on-prem, and edge environments. Relevance (2026): multimodal models have moved from demos into production use where real-time vision, privacy, latency, and safety constraints matter. Organizations now choose between cloud-hosted models (for scale and capability) and on-device/open-source alternatives (for cost control, latency, and data residency). Agent frameworks and marketplaces have become key to composing, monitoring, and monetizing multimodal agents. Key tools and roles: Google Gemini provides a family of multimodal generative models and APIs via Google AI, Studio, and Vertex AI for enterprise and developer use; Anthropic’s Claude family offers conversational and developer assistants with multimodal inputs; LangChain is an open-source-first framework for building, testing, and deploying agentic workflows and integrations; PolyAI focuses on voice-first conversational agents for contact centers; Yellow.ai targets enterprise CX/EX automation with autonomous, multi-channel agents. Open-source alternatives and edge vision platforms supply customizable, locally runnable stacks that balance capability, cost, and privacy. Practical considerations include model capability vs. latency, observability and safety tooling, franchising agents through marketplaces, and integration with existing enterprise systems. Evaluations should weigh multimodal reasoning quality, deployment options (cloud vs edge), developer tooling, and enterprise operational controls.