This topic covers the emerging class of AI-native prediction-market platforms and “fair-value” agents — systems that generate, price and trade probabilistic forecasts or outcomes — and the marketplaces/frameworks used to build, deploy and monetize them. Interest in this area has grown as autonomous agents and agent marketplaces have matured: developers need stateful orchestration, reproducible evaluation, and monetization pathways for agents that can supply calibrated forecasts, provide liquidity, or act on market signals. Key infrastructure spans multiple layers. Agent frameworks and engineering platforms (for example LangChain, with its stateful LangGraph emphasis) provide the tools to build, debug and evaluate agentic LLM applications. Autonomous-agent runtimes (AutoGPT and similar) automate multi-step workflows and continuous execution, while cloud marketplaces and deployment platforms (Agentverse) enable listing, discovery, hosting and monitoring. Developer-focused SDKs and CLIs (GPTConsole) and no-code/low-code builders (MindStudio, Anakin.ai) lower the bar for creating production agents. Specialized developer tooling and IDEs (Warp’s Agentic Development Environment) and focused open-source agents (Cline for coding tasks) help teams iterate faster and instrument behavior for evaluation and safety. As of late 2025, practical considerations dominate: how to define and measure “fair value” for agent predictions, ensure calibration and auditability, manage state and memory across agent lifecycles, and integrate on‑chain/off‑chain settlement or governance when markets are involved. Important evaluation axes include calibration, robustness to manipulation, latency, cost, and clear monetization/permission models. For practitioners, success in this space depends on composable agent frameworks, reproducible evaluation pipelines, and marketplaces that support transparent pricing, monitoring and governance rather than just distribution.