This topic examines the rise of agentic booking — autonomous, multi-step AI agents that assemble itineraries, compare options and execute bookings — alongside alternatives such as Google Search Canvas and traditional travel planning tools. As of 2025, improvements in multimodal LLMs, retrieval-augmented generation (RAG) and agent orchestration have made practical end-to-end travel agents and marketplace ecosystems viable for both consumers and enterprises. Key trends include increased use of model families (Google Gemini, Cohere), developer frameworks (LangChain, LlamaIndex) for document agents and RAG pipelines, and agent platforms that add governance, monitoring and workflows (Kore.ai, Agentverse, IBM watsonx Assistant). Microsoft 365 Copilot illustrates how embedded assistants can support planning within productivity apps, while DeepL supports localization and multilingual customer interactions for global bookings. Agent marketplaces and tool marketplaces are enabling discovery and commercial deployment of specialized travel agents, but adoption hinges on observable governance, secure API integrations, and human-in-the-loop controls to manage payments, cancellations and compliance. Compared with conventional itinerary builders, agentic booking systems promise automation, dynamic rebooking and richer personalization, yet introduce tradeoffs around data privacy, transaction security and vendor integrations. For organizations evaluating options, the practical stack often combines a large multimodal model (Gemini or Cohere), retrieval and document indexing (LlamaIndex), an orchestration/agent framework (LangChain), and deployment/observability via platforms like Kore.ai or Agentverse; enterprise virtual assistants such as watsonx Assistant or embedded copilots can layer on compliance and user-facing workflows. This comparison is relevant now because 2025 tooling has matured enough for production pilots, but careful selection of frameworks, marketplaces and governance remains essential to balance automation benefits with operational and regulatory risk.