atheryon / system / reference-architecture

System

Reference architecture (core proof).

Atheryon designs and delivers production-grade capital markets systems and data platforms using AI agents.

§01 / Architecture

Architecture diagram

Data Sources feed two classes of specialist agent: ETL agents build the Operational Data Store (ODS), and Operations agents run workflows on it, coordinated by the Orchestrator (built on Claude), gated by Expert sign-off, and logged to the Directorial archive, producing Operational Outputs.

§03Orchestratorroutes · types · retries · audits· built on Claude ·
§01
Data Sources
S&P Global · enterprise bank systems · counterparty feeds
ETL AGENTS · CDM-native modelling · validation · ingestion
agent
agent
agent
agent
agent
§02
Operational Data Store (ODS)
the CDM-typed foundation — validated · field-level lineage
1,019 type defs · 42 ISO 20022 · 14 FpML
OPERATIONS AGENTS · per business unit — run the workflows on the ODS
Front Office
trade lifecycle
Risk & Analytics
P&L · limits · anomalies
Operations
confirms · settlement
Compliance
reg reporting · surveillance
Treasury / Finance
liquidity · funding
Expert sign-offsenior capital-markets expert selects · edits · signs off every output
§04 → §05
Operational Outputs
capital-markets systems · risk · trading · regulatory reporting
Directorial archiveevery agent decision replayable & auditable · deployed on Azure (Container Apps · Entra ID · Postgres)
data flowcontrol / auditnavy = orchestratordouble-rule = expert sign-off
Specialist agents — independently deployable / licensable. Detailed reference-architecture briefing — agent clusters, deployment topology, operational evidence — available under MNDA.
§02 / Data Flow Layer

Data Flow Layer

A bespoke capital-markets data model, industry-anchored (ISDA, ISO 20022, FpML conventions) and shaped by 20+ years of front-to-back banking experience. 1,019 type definitions, 42 ISO 20022 message types, and 14 FpML schemas — all parseable, queryable, and validatable at runtime. Source feeds from S&P Global, internal ledgers, and counterparty channels are mapped to typed payloads on ingest, with field-level lineage tracked from origin through every transformation. The Schema Editor (extend / wrap patterns) lets domain experts model real bank-specific extensions on top of the canonical core without forking.

§03 / AI Agent Layer

AI Agent Layer

Two classes of specialist agent, coordinated by a multi-agent orchestrator. ETL agents build the CDM-typed Operational Data Store — CDM-native modelling, validation, and ingestion with field-level lineage. Operations agents run the workflows on top of it, one set per business unit (front office, risk & analytics, operations, compliance, treasury / finance). The agents run on Anthropic’s Claude (Claude Agent SDK) — model-agnostic by design; the orchestrator owns routing, payload typing, retry, and audit. Each agent generates candidate implementations against the loaded schemas; a senior capital-markets expert selects, edits, and signs off. Every prompt, context, correction, and resulting code change is captured to the directorial archive — every agent decision is replayable and auditable.

§04 / Workflow Examples

Workflow examples

  • 01
    Trade lifecycle automation

    Match firm-vs-counterparty confirmations on economic terms; surface exceptions with field-level diffs. Electronic confirmation via MarkitWire and DTCC CTM; affirmation T+0, confirmation T+1/T+2. Aging analysis with SLA breach alerts.

  • 02
    Risk reporting generation

    Score each trade against per-regime field-completeness rules (EMIR Refit, MiFID II, ASIC, CFTC 43/45, SFTR, Dodd-Frank — six regulatory regimes). Generate the report payload in the regime’s prescribed format. Scheduled daily/T+1 runs into the submission queue with one-click trade-repository submission.

  • 03
    Portfolio analytics pipeline

    Aggregate live positions; attribute P&L; detect anomalies in trade quality and counterparty data. KPI tiles, trend charts (7d / 30d / 90d), and anomaly feed with severity and recommended action. Drill-down into anomaly detection, data quality, and pattern mining.

  • 04
    Financial data ingestion workflow

    Map source fields (S&P Global, internal ledgers, counterparty feeds) to ISDA CDM types. Validate against CDM, ISO 20022, and FpML schemas. Per-counterparty data-quality scoring with longitudinal trend; field-level lineage from origin to operational data store.

§05 / Deployment Model

Deployment model

Azure-native. Claude (Anthropic) as the agent runtime, Postgres for the operational data store, Container Apps for the service mesh, Microsoft Entra ID for identity. APRA CPS 234-aligned operational-controls baseline. Marketplace-bound reference implementation: deployable into your Azure tenant, licensed as a reference platform, or operated under managed-service terms. Fully inspectable, extendable, and externally testable today — no demoware.

§06 / Proof Artifacts

Proof artifacts

Reference system described as a working architecture (not screenshots or UI gallery).

The reference system runs at labs.atheryon.ai. 26 themes across 111 pages span six operational surfaces: the Operational Data Store (schemas, validators, lineage, entity intelligence) plus five business units (Front Office, Risk & Analytics, Operations, Compliance, Treasury / Finance). Every surface is reachable, browsable, and verifiable — a working architecture, not a screenshot gallery. A detailed reference-architecture briefing — covering core services, agent clusters, deployment topology, and operational evidence — is available under MNDA.

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