WEO Contribution Architecture: A Three-Phase Framework for Distributed Participation in AI Infrastructure Coordination Intelligence

Three-phase framework for distributed participation in Coordination Intelligence production at the Warmth Engine Observatory, governed end-to-end by a single methodology invariant.

The Warmth Engine Observatory tracks AI infrastructure coordination dynamics across geopolitical blocs through verified events classified by event type, bloc, tier, and domain, enriched by industry tags and Environmental Nexus Tags, with Coordination Connections mapping typed relationships between events, Infrastructure Threads recording documented material links between events, and Sovereign Capability Profiles for assessed actors. This paper presents the architecture through which participation in that production extends beyond the platform itself.

Phase 1 (Data Publication) establishes the platform as a queryable MCP server with a tiered access and commercial model, enabling AI systems to consume Coordination Intelligence programmatically. This phase has been operational since May 2026, and the commercial access tier is live. Phase 2 (Observer Network) opens the database to contributions from verified human analysts operating through the WEO processing pipeline. Contributions accrue credit against a calibrated rate card; the near-term reward is recognition and non-transferable access to the corpus, with cash revenue-share a deferred, contingent rail. Phase 3 (Agent Economy) enables autonomous AI agents to contribute verified events through a consensus verification mechanism with reputation-based quality assurance — a design intended to produce a self-reinforcing dynamic in which AI systems both consume and contribute to verified data about the infrastructure they are built upon.

The architecture maintains a single invariant across all three phases: the WEO Methodology Manual governs what qualifies. The processing output of every contributor — whether the platform itself, external Observers, or autonomous agents — is evaluated against the same verification standards, classification criteria, evidence thresholds, and enrichment layer requirements. The architecture commits this invariant to executable form through a write-path tool architecture: the methodology's qualification gates encoded as submission tools on the platform's programmatic interface, so that validation precedes review on every contribution pathway.

The paper specifies the conceptual framework, the access and credit architecture, the reward architecture, the write-path tool architecture, and the adversarial resilience design. The framework's constituent mechanisms have documented prior art — programmatic data access via the Model Context Protocol, human OSINT crowdsourcing with revenue-share, and autonomous-agent contribution under consensus and reputation systems — as does the integrated pattern itself. The contribution recorded here is the governed application of that pattern to a specific domain, verified AI-infrastructure coordination intelligence across geopolitical blocs, under a methodology invariant in which the verification standard is held constant and the contributor type is the only variable. Prior art is documented in full in the paper's terminology and provenance section, with bounded observations stating the scope of the search behind them.

This document can be read independently. Readers seeking the qualification standards it defers to should consult the WEO Methodology Manual; readers seeking calibration justifications for those standards may consult the companion Methodology Rationale. A residual set of implementation-specific calibration parameters — including the self-update lockout duration, detection thresholds, and Phase 3 consensus parameters — is maintained in a separate internal operational specification and will be empirically determined through milestone-triggered calibration against accumulated corpus data.