This topic covers the use of modern AI stacks to build, backtest and run quantitative crypto-trading strategies and to extract, calibrate and trade signals from prediction‑market platforms. It focuses on three categories — Market Intelligence Tools (real‑time on‑chain/orderbook/sentiment feeds and prediction markets), Data Analytics Tools (feature engineering, time‑series modelling, probability calibration) and AI Data Platforms (agent frameworks, orchestration and deployment). Relevance in late 2025: agentic and autonomous AI tooling matured into production-ready developer platforms, GPUs and inference ecosystems consolidated around large vendors, and on‑chain data + prediction markets became standard alternative signal sources for quant shops. Practitioners are now integrating stateful LLM agents, autonomous workflows and specialized ML tooling to accelerate research-to-deployment cycles while addressing governance and reproducibility. Key tools and roles: LangChain (engineering framework and LangGraph for stateful agent orchestration and chaining LLMs into trading/monitoring agents); AutoGPT (autonomous agent workflows for automated data collection, backtesting and trade decision pipelines); Windsurf (AI-native IDE for rapid prototyping, agentic coding and multi-model experiments); GPTConsole (SDK/API/CLI for building, managing and monetizing production agents with lifecycle and memory controls); Tabnine (enterprise coding assistant emphasizing private/self-hosted setups and governance). The Deci.ai site audit (NVIDIA takeover) highlights ongoing hardware/ML ops consolidation that affects inference cost and latency for live trading. Outcome: combining these components yields reproducible, auditable quantitative workflows that fuse prediction‑market probabilities with traditional signals, while emphasizing deployment governance, latency control and privacy for crypto trading use cases.