Agentic Research

Agentic Research is a paradigm of information synthesis where AI agents autonomously execute multi-step workflows to fulfill complex investigation goals. It moves beyond static retrieval toward active, iterative discovery, verification, and reasoning.

Key Characteristics

• Autonomous Reasoning: The ability to decompose high-level research queries into actionable sub-tasks.
• Tool-Augmented Investigation: Utilizing external environments and software (e.g., web browsers, code interpreters, and specialized platforms like notebooklm) to gather data.
• Iterative Refinement: Self-correcting loops that analyze, cross-reference, and synthesize findings to mitigate hallucinations.

Recent Technological Shifts

• Integrated Deep Research: A transition from simple retrieval to profound, multi-layered investigation capabilities (e.g., notebooklm Deep Research).
• Enhanced Source Discovery:
  • Fast Research: High-speed, high-level information retrieval for foundational context.
  • Deep Research: Exhaustive, multi-step investigative processes for niche or complex topics.
• Multimodal Expansion: The integration of advanced visual intelligence and sophisticated processing (e.g., nano-banana-pro).

Related Entities

• agentic-ai
• rag (Retrieval-Augmented Generation)
• deep-research
• multimodal-ai

Backlinks

• 2026 04 14 Notebook LM new features

Source Notes

• 2026-04-23: [[lab-notes/2026-04-23-Claude-Routines-Action-Based-AI-Automation-for-Business-Event-Response|Claude Routines: Action-Based AI Automation for Business Event Response]]
• 2026-04-23: Engine Survival: The Critical Role of Oil Pressure and Warning Lights