Why This Exists
Designing agentic AI systems is accelerating faster than shared architectural understanding.
Patterns often remain implicit, tribal, or buried inside specific implementations.
This experiment exists to increase velocity and clarity by making architecture patterns discoverable, evidence-based, and reusable — not theoretical or framework-bound.
The goal is to democratise architectural reasoning and make design decisions more transparent across technical and non-technical collaborators.
What This Experiment Explores
- Can architecture patterns in agentic AI systems be systematically extracted from real code and execution signals?
- Can those patterns be structured in a reusable, evidence-backed format?
- Does making patterns explicit reduce design friction and improve decision quality?
- Can a pattern system evolve into an agentic decision-support tool?
What Was Built
- A structured schema for extracting agentic architecture patterns from real repositories.
- A repeatable prompt-driven workflow for mining patterns from codebases.
- An initial pattern selector artefact to explore pattern discovery and recommendation.
- A publishing structure for cataloguing patterns as reusable learning artefacts.
Focus is on the pattern system itself, not on any single implementation instance.
Key Feature
- Structured pattern schema
- Evidence-backed extraction workflow
- Selector interface
- Agentic evolution path
- Publishing and reuse structure
- Test reusable patterns, not one-off implementations.
Current Status
This experiment is currently in the Exploring phase.
Progress at this stage means validating that patterns can be extracted consistently from real agentic systems and structured without drift.
Moving to Validating will require evidence that the pattern catalogue improves clarity or velocity in at least one real design context.