Agent Knowledge Cycle (AKC)

A knowledge cycle for AI agents — one that grows with the people who shape it. AKC is a six-phase self-improvement loop (Research → Extract → Curate → Promote → Measure → Maintain) that keeps skills, rules, and documentation aligned with reality over time. Unlike one-directional optimization frameworks, AKC is bidirectional: as the human curates and promotes knowledge, their judgment about what makes good agent behavior also sharpens. AKC treats human cognitive resources — attention and judgment — as the central constraint that does not scale with the model, and shapes every phase to protect that budget: Research is signal-first, recurring decisions are promoted to rules, compliance is measured instead of…