Dyson Sphere

Systems Architecture

A self-improving infrastructure geometry where every interaction generates evidence, every failure feeds improvement, and every improvement is gated by verification.

Live Panels

Currently deployed and verified modules.

01Live
Live

KORE Orchestrator

13 microservices for agent infrastructure — routing, memory, PIL optimization, embeddings, retrieval, guardrails, billing. The governing layer for all service calls and artifact production.

13 servicesStripe billingMCP compatible
02Live
Live

PIL Optimization

Prompt improvement and execution tuning module. 12 evaluation scenarios with 92.3% pass rate. Public demo via governed Worker proxy with rate limiting and artifact capture.

12 scenariosWorker proxyRate limited
03Live
Live

Run-Contract Emitter

Canonical artifact format for every KORE service call. 8 schema sections including cost telemetry, scoring gates, failure/recovery, replay, and promotion eligibility.

8 sections25+ validationsArtifact store
04Live
Live

DoneGate & Harness

Multi-state completion verification with hard gates, soft signals, and DONE/PARTIAL/BLOCKED/UNDECIDABLE verdicts. Every promotion decision grounded in measured evidence.

4 verdictsHard + soft gatesDrift tracking
05Live
Live

Memory Keeper

Pattern and risk store fed by run-contract artifacts. Success/failure patterns and risk entries with failure classification, queryable by service and failure class.

Patterns ingestRisk trackingQuery API
06Live
Live

PIL Simulator

12-scenario evaluation workload covering easy, medium, hard, and failure cases. Stub mode for testing, API mode for real PIL endpoint verification.

12 scenarios92.3% passIntentional failure

Panels & Flow

Every call produces evidence. Every failure feeds improvement. Every improvement is gated.

The Dyson Sphere architecture connects five layers into a self-improving loop:

1. Orchestration
KORE services handle requests through governed routes. Every call produces a run-contract artifact.
2. Evidence
Artifacts capture request, response, cost, latency, gates, failure class, and recovery data in a canonical schema.
3. Learning
Memory Keeper ingests artifacts into patterns and risks. Simulator replays scenarios. Failures cluster into improvement candidates.
4. Gating
DoneGate/Harness scores every improvement against hard gates and soft signals. No change promotes without measured evidence.
5. Compounding
Seeds become datasets. Patterns inform routing. Recovery traces train failure detection. The loop produces cumulative value.