Agentic Context Management reduces AI agent costs

AI agents often suffer from degraded accuracy, rising costs, and self-contradiction due to unmanaged context windows and unmanaged conversation histories. The author argues for Agentic Context Management, a unified discipline covering memory architecture, ingestion, scoping, anticipation, and compaction to maintain accuracy while keeping costs linear. Open article →