This topic covers how AI-enabled stacks are being used in 2026 to detect, investigate, and monitor manipulation in crypto markets. Detection increasingly requires fusing high-frequency on‑chain signals, off‑chain order-book and social data, and labeled historical events; that drives demand across four tool categories: Market Intelligence, Competitive Intelligence, Data Analytics, and Web Scraping. Practically, web scraping and streaming connectors collect exchange order books, social chatter, and token listings; data analytics platforms ingest and normalize those feeds for feature engineering and anomaly detection; competitive and market-intelligence tools contextualize actor behavior and attribution; and agent frameworks automate triage and alerting. Representative tools in this stack include Sisense for embedded BI and customizable analytic dashboards; Perplexity AI for quick, sourced research and contextual citations on emerging indicators; LangChain to build and orchestrate agentic LLM workflows that maintain state, query databases, and enrich signals; Warp as an Agentic Development Environment that accelerates developer iteration on monitoring code and agents; and Microsoft 365 Copilot to streamline reporting, incident summaries, and analyst collaboration across documents and spreadsheets. Why it matters in 2026: regulatory scrutiny and multi‑chain, cross‑venue manipulation techniques have grown, while adversaries use automation and mixing services to evade simple heuristics. Combining web scraping, robust feature engineering, real‑time analytics, and agentic automation reduces detection latency and improves investigative throughput, though challenges remain around data quality, attribution certainty, and false positives. Effective solutions focus on modular pipelines—scrapers, ingestion, analytics, agents, and human-in-the-loop review—so teams can iterate models, preserve audit trails, and adapt to evolving tactics.