Agent Knowledge Cycle

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Agent Knowledge Cycle (AKC)

[](https://doi.org/10.5281/zenodo.19200726) [](https://deepwiki.com/shimo4228/agent-knowledge-cycle)

**A knowledge cycle for AI agents — agent behavior compounds, human judgment sharpens.**

Agent Knowledge Cycle (AKC) is a six-phase growth cycle for persistent AI agents. It turns repeated agent experience into reusable knowledge — skills, rules, and documentation — under human approval, keeping the agent aligned with the operator's evolving intent while spending the scarce resource — human attention and judgment — only where it matters. The cycle changes the human too: operating it sharpens the judgment that steers it. Built for people who operate coding agents or persistent AI harnesses day to day, it runs inside Claude Code or any comparable harness.

Companion paper: *Harness Alignment and Harness Drift: Why Intent, Unlike Correctness, Resists Automation* — doi:[10.5281/zenodo.20578272](https://doi.org/10.5281/zenodo.20578272)

**Try it first**: one `cp` of the standalone [akc-cycle rules file](https://github.com/shimo4228/akc-cycle) gives any AI agent the six-phase behavior — see [Install](#install).

Why AKC

**The bottleneck has moved.** Most agent frameworks optimize the agent side: more tools, memory, context, or automation. AKC starts from the inverse constraint: as agent capability grows, the scarce resource is the human attention and judgment required to steer the loop ([ADR-0010](docs/adr/0010-human-cognitive-resource-as-central-constraint.md)). Skills go stale, rules accumulate residency cost, and documentation drifts — every phase of the cycle exists to keep that maintenance from consuming the operator's fixed budget.

**Intent alignment, not just correctness.** Tests and linters can check whether one output passes a specification; they cannot check whether a changing