Future State Platform

Design Principles

  • Deploy inside the client’s Azure tenant so identity, data, network, and operations stay aligned with client controls.
  • Separate control-plane change from runtime execution so platform governance does not require redeploying every workload concern.
  • Treat knowledge grounding, evaluation, and observability as platform capabilities rather than optional add-ons.
  • Reuse Agent Primitives as the shared execution substrate instead of duplicating orchestration logic inside each domain solution.
  • Split repositories only after service contracts and delivery boundaries are stable enough to avoid premature fragmentation.

Platform Planes

PlanePurposePrimary Containers
Experience PlaneOperator, builder, and approval experienceMission Control Portal
Control PlaneTenant, policy, solution pack, and release managementPlatform API, Governance and Registry Service
Runtime PlaneAgent execution, tool invocation, and escalation controlRuntime Orchestrator, Tool and Action Gateway
Knowledge PlaneIngestion, curation, and grounded retrievalKnowledge Pipeline, Context and Retrieval Service
Operations PlaneQuality gates, experimentation, auditability, and value trackingEvaluation and Experiment Service, Observability and Value Service
Data PlanePersistent state and event backbonePostgreSQL, Blob Storage, AI Search, Service Bus

Relationship To Agent Primitives

NeuralOps should not own every low-level agent concern itself. The future-state model assumes that Agent Primitives continues to provide reusable agent lifecycle and execution capabilities, while NeuralOps adds tenant-aware governance, domain solution packaging, knowledge operations, and measurable business value on top.

Container View

This view shows the future-state platform decomposition inside NeuralOps.

Architecture diagrams for NeuralOps are being migrated to Mermaid/Excalidraw C4 format. Placeholders will be replaced with embedded diagrams in a future update.

Deployment View

The ideal runtime target is a client-owned Azure landing zone with separate application, data, and control concerns.

Governed Execution Flow

This dynamic view highlights the design intent for grounded, policy-aware, human-reviewable execution.

What The Model Optimises For

  • Faster separation of policy and configuration changes from runtime scaling concerns
  • Cleaner packaging of domain accelerators as reusable solution packs
  • Better support for tenant-specific approval, audit, and operational workflows
  • A platform shape that can evolve from the current accelerator codebase without pretending the current system is a clean-sheet build