Agentic AI platforms for scientific research are systems that combine model orchestration, domain knowledge, and workflow automation to help researchers run experiments, summarize results, and manage institutional knowledge. This topic covers marketplaces that provide multi‑model access (pay‑per‑use model hubs), agent frameworks and low‑code builders for designing and operating agents, semantic and data platforms that index research artifacts, and knowledge‑management layers that connect dispersed documents and emails into queryable context. Relevance in early 2026 stems from wider adoption of agentic patterns in labs and computational groups: researchers increasingly need multi‑model orchestration, reproducible pipelines, and secure ways to query proprietary datasets. Tool trends include no‑code/low‑code platforms (e.g., MindStudio) to lower the barrier to compose and deploy agents; model marketplaces and pay‑per‑use hubs (e.g., GenAI Hub) for rapid access to specialized models; semantic search/content platforms (DeeperMind.ai) and unified search layers (AI Knowledge Search by Amurex) to surface institutional knowledge; agentic developer environments (Windsurf) and self‑hosted assistants (Tabby) that keep code and data local; and multi‑model chat aggregators (Ockto Chat) for comparative model evaluation. Key practical considerations are provenance, data security, model selection, and integration with lab information systems. Agentic platforms promise productivity gains but require explicit pipelines for validation, versioning and audit trails to be research‑grade. For institutions like Anthropic, Allen Institute and HHMI, the focus is on combining robust agent architectures with curated scientific datasets and governance to support reproducible, interpretable workflows rather than one‑size‑fits‑all automation.