Hi, thanks for maintaining this excellent Context Engineering resource.
I wanted to share a related independent practice that may be useful as a practical comparison point for the attractor-related material already present in this repository.
I do not want to over-interpret this repository's own theoretical framing. My understanding is simply that this project already discusses attractor-related ideas in the context of AI systems and context engineering.
Attractor-Guided Engineering (AGE) applies the attractor idea at a different level: the evolving software repository. It asks:
What should this repository keep converging toward as humans and AI change it over time?
In this framing, the attractor is the stable product/design/architecture structure that a repository should repeatedly return to during fast AI-assisted iteration.
References
Template repository:
https://github.com/entropy-cloud/attractor-guided-engineering-template
Related methodology articles:
Core idea
AGE is not just another agent skill or prompt pattern. It treats the repository itself as the persistent coordination surface.
Plans, tests, audits, logs, and bug notes are not the attractor. They are harnesses that check whether a change moved the repository toward the attractor rather than merely completing a checklist.
The template gives a concrete repository structure for this idea:
docs/context/ for project context, AI autonomy, source-of-truth rules, and codebase mapdocs/requirements/ for implementation-ready requirement interpretationdocs/design/ and docs/architecture/ for stable product/design/architecture owner docsdocs/plans/ for non-trivial execution slices and closure conditionsdocs/audits/, docs/logs/, docs/bugs/, and docs/testing/ for audit evidence, trajectory memory, defect diagnosis, and verification records
This makes AGE a repository-level context engineering practice: instead of only preparing the right context for the current model call, it structures the repository so future model calls can recover the right context, authority, and proof obligations.
Why it may be relevant here
AGE may provide a practical example of attractors at repository scale:
- from model/context behavior to repository trajectory convergence
- from temporary context state to durable repo state
- from prompt or memory carriers to owner-doc carriers
- from local agent guidance to closure/audit harnesses
This may be useful as a comparison point for the broader question of how context persists across sessions through repository structure, rather than only through the current prompt or memory mechanism.
Thanks again for the work on this project. I think AGE and Context Engineering are addressing adjacent layers of the same broader problem: how AI systems and AI-assisted workflows maintain stable convergence under repeated context changes.
Hi, thanks for maintaining this excellent Context Engineering resource.
I wanted to share a related independent practice that may be useful as a practical comparison point for the attractor-related material already present in this repository.
I do not want to over-interpret this repository's own theoretical framing. My understanding is simply that this project already discusses attractor-related ideas in the context of AI systems and context engineering.
Attractor-Guided Engineering (AGE) applies the attractor idea at a different level: the evolving software repository. It asks:
In this framing, the attractor is the stable product/design/architecture structure that a repository should repeatedly return to during fast AI-assisted iteration.
References
Template repository:
https://github.com/entropy-cloud/attractor-guided-engineering-template
Related methodology articles:
state space -> attractor -> trajectory -> control, and why harnesses, audits, and verification only become meaningful after the attractor is defined.Core idea
AGE is not just another agent skill or prompt pattern. It treats the repository itself as the persistent coordination surface.
Plans, tests, audits, logs, and bug notes are not the attractor. They are harnesses that check whether a change moved the repository toward the attractor rather than merely completing a checklist.
The template gives a concrete repository structure for this idea:
docs/context/for project context, AI autonomy, source-of-truth rules, and codebase mapdocs/requirements/for implementation-ready requirement interpretationdocs/design/anddocs/architecture/for stable product/design/architecture owner docsdocs/plans/for non-trivial execution slices and closure conditionsdocs/audits/,docs/logs/,docs/bugs/, anddocs/testing/for audit evidence, trajectory memory, defect diagnosis, and verification recordsThis makes AGE a repository-level context engineering practice: instead of only preparing the right context for the current model call, it structures the repository so future model calls can recover the right context, authority, and proof obligations.
Why it may be relevant here
AGE may provide a practical example of attractors at repository scale:
This may be useful as a comparison point for the broader question of how context persists across sessions through repository structure, rather than only through the current prompt or memory mechanism.
Thanks again for the work on this project. I think AGE and Context Engineering are addressing adjacent layers of the same broader problem: how AI systems and AI-assisted workflows maintain stable convergence under repeated context changes.