Presentation: Beyond Prompting: Context Engineering and Memory Management for AI Systems at Scale
Adi Polak discusses the architecture required to transition from stateless prompts to state-aware, context-rich AI agents. Drawing on 15 years in distributed systems, she shares how engineering leaders can leverage Apache Kafka and Flink for real-time stream processing, dynamic memory tiering, and tool orchestration via MCP to solve token limits, cost spikes, and latency bottlenecks. By Adi Polak
The rapid development and widespread adoption of large language models have exposed significant challenges in managing AI context and memory, leading to an immediate need for sophisticated engineering solutions.
Achieving state-aware, context-rich AI agents at scale is critical for AI systems to move beyond basic prompting, enabling more complex and autonomous decision-making capabilities.
The focus is shifting from simple, stateless prompts to a more architectural approach involving real-time stream processing, dynamic memory management, and tool orchestration for AI.
- · AI platform developers
- · Cloud infrastructure providers
- · Data engineering professionals
- · Enterprises adopting advanced AI
- · Companies relying on basic prompt-based AI
- · Inefficient AI infrastructure
- · AI solutions without context management
Improved performance and cost-efficiency of large-scale AI applications.
Acceleration of autonomous AI agent development and deployment across various industries.
Potential for AI systems to handle increasingly complex tasks currently requiring significant human oversight, leading to industry restructuring.
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Read at InfoQ