You don’t have to decide the entire architecture upfront. In a graph-native system, structure can emerge from accumulation.
Describe, Embed, Cluster, Verify
You describe functions in natural language, with schemas and examples. Those descriptions can be embedded into a semantic space. Functions cluster by conceptual proximity. This produces a landscape of related capabilities.
From that landscape, the system proposes connections that are likely compatible, then verifies them via schema checks or sample runs. Connections become discovered rather than hand-wired.
Grow Wild, Compress Later
You can allow a graph to grow dense with redundant or overlapping nodes. Later, you can analyze patterns:
- Nodes that transform similar inputs to similar outputs
- Repeated sequences that suggest templates
- Clusters that reveal new conceptual domains
An abstraction agent can then propose consolidation or templates based on actual usage patterns rather than guesses.
Naming Becomes Optional
Because you can search by intent rather than by file name, naming is no longer the primary index. The graph holds meaning, not the filesystem.
Emergence as a Workflow
1) Add a new function for a real need. 2) Connect it by declared inputs and outputs. 3) Let the graph record lineage and usage. 4) Periodically analyze clusters and redundancies. 5) Create templates or translators where patterns stabilize.
This approach replaces premature abstraction with evidence-based structure.
Why This Matters
- Creativity: You can explore without committing to rigid architecture.
- Discovery: The system shows you where coherence is forming.
- Adaptability: You can re-shape the graph based on real usage.
The result is a system that behaves more like an ecosystem than a blueprint: it grows, adapts, and becomes more coherent over time.