This topic covers the practical compliance, security and monitoring toolset for AI systems used in prediction markets and crypto‑native applications. As of 2026-02-16, organizations building agentic or multi‑agent workflows, market‑making models, oracle integrations and automated smart‑contract agents face rising regulatory scrutiny, adversarial threats (market manipulation, oracle attacks), and operational risk from opaque model behavior. Effective controls combine policy and vendor governance, runtime observability, model lifecycle controls, and domain‑specific QA. Key tool categories include governance and policy platforms (e.g., Monitaur’s policy, monitoring and vendor governance tooling for regulated industries), agentic‑AI infrastructure with observability (e.g., Xilos’s visibility into connected services and agent activity; Kore.ai’s multi‑agent orchestration with governance features), conversation and QA platforms for operational assurance (e.g., Observe.AI’s VoiceAI, real‑time assist and auto QA for conversational flows), and enterprise assistant/agent platforms used to automate workflows (IBM watsonx Assistant, Microsoft 365 Copilot). Cloud ML platforms and models—Vertex AI for end‑to‑end model deployment and monitoring, and foundation model providers such as Anthropic’s Claude family, Mistral, and Google Gemini—supply the runtime and model primitives that must be instrumented and governed. Practical implementations instrument provenance, drift and safety telemetry at deploy time, combine automated QA and adversarial testing for market signals, and integrate vendor‑risk controls for third‑party models and oracles. The intersection of prediction markets and crypto AI makes hard requirements for auditability, explainability, and rapid incident detection: monitoring must span on‑chain events, oracle feeds and agent actions. Selecting tools should be driven by compliance needs, integration surface (cloud vs on‑prem), and the specific threat model of market and smart‑contract exposure.