InfoQ proposes context engine for agentic AI architectures

The author argues that building reliable agentic AI systems requires treating context, memory, and knowledge as distinct, systematically managed components rather than relying on massive, unstructured prompts. By introducing a context engine that acts like a compiler, developers can filter, compress, and structure real-time and historical data before it reaches the language model. This approach minimizes hallucinations, reduces token costs, and prevents performance degradation caused by unmanaged context windows. Open article →