- A Minimal Viable Theory
-
WunderOS is built on and for substrate-recursion. Every agent action, every classifier output, every plan execution writes back to the substrate with full provenance. These folds compress operational experience into a representation that subsequent agent-substrate interaction cycles consume as context. Cycle n's experience becomes n+1's compressed prediction surface.
The substrate gets smarter over operational time, not because the LLM does, but because the substrate’s compression of experience improves with each cycle.
Further consideration of these and related matters may be found in Pentad Labs Research Notes.
- Software Platform
-
WunderOS combines agentic harness engineering, systems engineering, systolic-array compute, knowledge representation, lightweight RSI, and control theory with bare metal, HFT-grade latency, telecoms-grade concurrency primitives, durability, and traceabilty.
Autonomic systems are defined rigorously in the MAPE-K (Monitor, Plan, Analyze, Execute over shared Knowledge) reference model. WunderOS is
MAPE-K for enterprise agents
: a platform where substrate-recursion makes the write path the structural through-line. In WunderOS, every assertion carries provenance; every classification carries lineage; and every plan execution writes back into knowledge with full audit-grade traces.An explicit discussion of the ways in which WunderOS is autonomic is the business of PLRN-021.
In WunderOS, customer agents run unchanged. Agentic frameworks issue native primitives unawares. WunderOS intercepts them invisibly, where it can provably improve upon them, answering them as syscalls. No LLM calls in any internal hot path. Agentic frameworks are the new userland.
- Unification
-
One binary-vector substrate absorbs data streams that otherwise demand separate stores. All become Pentads; all participate in the same bind/bundle/resonate algebra.
- Agent exhaust: observations, tool outputs, session logs.
- Enterprise facts: RDBMS, ERP, CRM, HRIS. See §4 (Institutional).
- Dense embeddings: what a vector database (HNSW, IVF-PQ) would hold.
- Multimodal features: images, audio, via learned VSA projectors.
- Case-based analogies: relational patterns, reasoning cases.
- Principle
-
LLM Minimality. Every primary path is LLM-free. Frontier calls are a fallback, never the default. They decay as per-tenant small models distill from them. Each is opt-in, metered, and recorded in Lineage. A fuller treatment is in PLRN-022.
- Core Operation
-
Single-hop resonance: ~100ns. Binding: ~200ns. Multi-hop CSR query and inference linear in beam width, not query depth—a 10-hop query costs the same as a 3-hop query. 16,384-bit binary vectors, AVX-512 Hamming distance. No GPU required.