Context engineering moves AI systems beyond one-off prompting

An InfoQ presentation by Adi Polak focuses on what comes after simple prompting: context engineering for AI systems that need memory, state, and real-time orchestration. The talk covers patterns using Kafka, Flink, dynamic memory tiering, and MCP-based tool calls.

The practical issue is that production AI agents cannot rely on a single prompt stuffed with everything. They need systems that decide what context to retain, route, retrieve, and discard under latency and cost constraints.

For engineering leaders, the message is clear: AI quality is increasingly an architecture problem, not only a prompt-writing problem.