Autonomous executive research assistants are AI agents designed to handle the recurring, high‑value information tasks executives and leadership teams rely on: synthesizing market and competitive intelligence, producing board‑quality briefings, summarizing meetings and surfacing actionable risks or opportunities. This topic covers the emerging class of purpose‑built agents (examples in market names include Sakana Marlin and Mastercard Virtual C‑Suite) and the platforms and frameworks that enable them. These systems combine several technical and product trends: multi‑agent orchestration and agent frameworks that chain specialized subagents; retrieval‑augmented generation and real‑time web grounding for up‑to‑date, cited answers (Perplexity AI); enterprise connectors, governance and no‑code builders for safe deployment (IBM watsonx Assistant); and task‑specific tools such as meeting capture and action‑item extraction (Fireflies) and document chat interfaces for multi‑document analysis (ChatPDF). Conversational assistant models (Anthropic’s Claude family) provide the conversational and developer‑friendly front end for research, writing and analysis. Why it matters now: organizations increasingly expect AI to do more than draft text — they need agents that integrate internal data, cite sources, follow compliance constraints and produce audit trails for decisions. The category spans AI agent marketplaces (discovering prebuilt executive agents), automation platforms (orchestrating workflows), and research tools that prioritize sourcing and explainability. Practical evaluation focuses on accuracy of grounding, integration with enterprise data, auditability, and controls for data governance. For buyers, the critical tradeoffs are real‑time sourcing and traceability versus task coverage and ease of integration; for vendors, the priority is composability, security and transparent provenance rather than standalone model performance.