AgentProphet: Source-Aware Multi-Agent Emerging Technology Forecasting for Upstream Decision-Making in AI-Based IoT Systems

AI-based internet of things (IoT) systems increasingly require upstream decision-making mechanisms to identify emerging technologies that may shape future sensing–memory–communication–computation capabilities (SMCC). However, early technology signals are often weak, fragmented, and distributed across heterogeneous sources with different reliability levels, making reliable capability planning difficult. This paper proposes AgentProphet, a source-aware multi-agent framework for emerging AI technology forecasting in AI-based IoT systems. AgentProphet integrates evidence from papers, patents, policy documents, and reports into a unified concept space, and combines role-specialized agent reasoning, source-aware confidence calibration, and critic-guided refinement to generate target-year technology rankings. In the main balanced weak-signal forecasting task, AgentProphet achieves a Growth-Aware NDCG@10 of $0.410\pm 0.076$, improving over GRU, DirectLLM, DLinear, and ARIMA by 58.3%, 91.6%, 108.1%, and 314.1%, respectively. It also obtains the highest E-Gain@10 of $0.305\pm 0.060$, E-MAP@10 of $0.056\pm 0.008$, and NDCG@10 of $0.474\pm 0.039$. Cross-task robustness analysis shows that DirectLLM remains competitive, and can be stronger in sparser or more mature signal regimes. A qualitative case study maps the forecasted capability directions to representative SMCC concerns as a data-level interpretation of possible planning implications. These findings suggest that AgentProphet is most suitable for balanced weak-signal settings where early evidence is available but incomplete, rather than serving as a universally superior emerging technology forecaster.