The increasing volume, velocity, and heterogeneity of continuously generated data have created a need for intelligent stream-processing architectures capable of adapting to changing computational and operational conditions. Conventional stream-processing systems generally depend on predefined scheduling, centralized coordination, or static optimization strategies, which can become inefficient when workloads fluctuate or processing resources experience failures. This research develops a conceptual framework for scalable AI-based multi-agent systems in which autonomous agents coordinate data acquisition, workload allocation, processing, monitoring, and recovery. The theoretical foundation combines heuristic search, learned heuristic functions, planning, pattern databases, regression-based planning, and neural decision mechanisms. The proposed architecture interprets stream-processing operations as a dynamic planning problem in which agents select actions according to workload state, resource availability, latency requirements, and resilience conditions. The framework is further motivated by recent work on AI-based multi-agent models for optimized data streaming, where scalability and resiliency are treated as simultaneous architectural objectives (Reddy et al., 2026). The analysis indicates that learned heuristics can reduce decision complexity, distributed agents can improve adaptability, and planning-oriented coordination can support graceful recovery from resource degradation. However, the approach introduces challenges involving coordination overhead, non-stationary workloads, communication costs, and the computational expense of learning. The study therefore positions multi-agent AI not merely as an automation layer but as a decision-making mechanism for adaptive stream-processing infrastructures.