Secure • scalable • product-neutral
Build your own AI infrastructure up and running in DataCenter
A complete reference architecture for enterprise Agentic AI: user access, identity, segmented networking, Kubernetes orchestration, CPU/GPU compute, model serving, data and MCP integration, SAN storage, backup, observability, security and lifecycle operations — designed to stay under enterprise control.
🛡️Data controlKeep sensitive workloads inside organizational boundaries.
⚙️Enterprise computeSeparate general compute and accelerated GPU workloads.
↻ResilienceRedundant fabric, replication and immutable backup patterns.
◉GovernanceIdentity, policy, audit and observability across the stack.
Agents & workflows
GPU / CPU compute
SAN + backup
01 / Complete reference architecture
One design, two views: logical platform and physical infrastructure
The logical view is derived from the platform-layer guidance in the supplied reference image. The physical view is anchored to the attached tender baseline, including the exact server, switching, SAN and backup capacities shown there.
Interactive architecture canvas
Click any module to read what it does and where it belongs.
Applications & accessUsers, apps, APIs, event sources
Enterprise usersWeb / desktop / internal portals
Admin & Ops UIControl and support surface
API / headless clientsREST / service integrations
Events & systemsWorkflow triggers
↓
Identity, policy & secure entryCross-tenant access boundary
SSO / OAuth / API identityRBAC • tenant isolation
Policy enforcementLimits • audit • network rules
Bastion / jump accessManagement plane
↓
Intelligence & orchestrationAgent runtime and workflow execution
Agent orchestrationAgents • routing • workflows
MCP & tool layerTools • connectors • actions
Session & memoryContext • state
Intelligent cachingLatency and cost control
↓
Configuration & platform controlChange behavior without coupling every change to code
Prompt managementTemplates • versions
Retrieval configurationIndexes • schemas • ranking
Action rulesIntent → approved action
MCP source registryApproved endpoints
↓
Data, retrieval & enterprise systemsPrivate context for AI workloads
Vector storeEmbeddings / search
SQL / document DBStructured context
Knowledge layerEntities • relations
Enterprise tools & APIsActions and systems
↓
Model serving & safetyPluggable inference layer
LLM / reasoningGPU inference
Embeddings & rerankingRetrieval models
Speech / visionOptional modalities
GuardrailsSafety • masking • policy
↓
Observability & operationsCross-cutting across every layer
TelemetryTraces • logs • metrics
Dashboards & alertsHealth • latency • failures
Audit trailWho • what • when
Utilization visibilityCapacity • showback
User / application zoneEnterprise users, APIs and application consumers
Users / ApplicationsBusiness workloads
Layer-3 ToR fabricHigh-speed data network
4 ToR switches25G / 40G / 100G
Management accessBastion / jump path
General computeCPU / VM / platform services
7 rack serversGeneral compute / VM cluster
Accelerated computeAI model and GPU workloads
2 GPU servers4 enterprise GPUs total
Storage fabricRedundant block storage paths
Dual 32G FC SANSAN switch A + B
Primary & replica storageApplication and AI data
80 TB SANPrimary storage
40 TB SANSecondary / replication
Backup & recoveryIndependent recoverability layer
20 TB + 10 TBImmutable backup • dedupe • encryption
4ToR switches
7 + 2general + GPU servers
80 / 40 TBprimary / secondary SAN
20 + 10 TBbackup appliances
02 / Platform architecture
Six operating layers around the physical data center
The attached platform diagram is used as a design guide, but the wording and grouping below are original and product-neutral. This keeps the conceptual strengths — identity, orchestration, configuration, observability, enterprise data and model services — without reproducing the source layout verbatim.
01
Users & access
- Admin and operations UI
- API / headless consumers
- Enterprise events
- SSO / OAuth / API identity
- Tenant-aware RBAC
02
Intelligence & orchestration
- Agent runtime
- MCP and tool execution
- Deterministic workflow paths
- Session and memory
- Ingestion and caching
03
Configuration plane
- Prompt templates
- Data / schema mapping
- Action rules
- Retrieval configuration
- MCP source registration
04
Data & context
- Vector index
- Relational / document data
- Knowledge relationships
- Enterprise APIs
- Private ingestion pipelines
05
Observability
- Open telemetry patterns
- Logs / traces / metrics
- Latency and health
- Audit history
- Capacity and utilization
06
Model & safety layer
- LLM / reasoning serving
- Embedding / reranking
- Optional speech / vision
- Data masking
- Guardrails and action policy
03 / Network & security zoning
Translate cloud-style subnet discipline into a product-neutral data-center layout
The second attached diagram suggests a strong separation of firewall, management, gateway, private-service and application-cluster zones. For an on-premises deployment, the same design principle can be implemented with enterprise firewalls, a bastion or jump path, load balancers, private service endpoints, cluster networks and controlled storage networks.
Secure edge / DMZNorth-south entry and remote enterprise connectivity
Enterprise firewallVPN / remote access gatewayExternal or internal load balancerWAF where required
Management zoneSeparated from workload traffic
Bastion / jump hostOut-of-band managementAdmin endpointsSecrets / certificate services
Container / orchestration zonePlatform and AI workload execution
Kubernetes control planeCPU worker poolGPU worker poolPrivate registry
AI service zoneModel, agent and retrieval services
Model servingAgent runtimeMCP toolsVector / retrieval services
Data & storage zonePersistent enterprise and AI data
Primary SANReplication targetDatabasesBackup network / appliances
04 / Dynamic request path
Watch an enterprise AI request move across the architecture
This simulator is intentionally conceptual: it illustrates architectural responsibility boundaries, not a claim that every implementation must use the exact same sequence.
User / App→Identity & Policy→Ingress / LB→Agent Runtime→Context / MCP→Model Service→Action / Response→Telemetry & Audit
Data paths may branch to vector stores, databases, enterprise tools or action targets. The model should not directly bypass identity, policy or tool authorization boundaries.
05 / Deployment roadmap
Build the platform in controlled phases
Do not start by buying GPUs. Begin with workload, data, security and availability requirements; then size the hardware and platform around measurable needs.
Define AI use cases, concurrency, model sizes, context requirements, data volumes, latency targets, uptime, retention, regulatory constraints and growth assumptions. These drive CPU, RAM, GPU, network, SAN and backup sizing.
Validate rack space, power, cooling, cabling, out-of-band management, high-speed switching, redundant SAN paths and backup connectivity before platform deployment.
Implement enterprise identity, RBAC, management access, segmentation, certificate and secret handling, ingress policy, audit requirements and tenant isolation rules.
Deploy the orchestration layer, general worker capacity, GPU workers, private registry, scheduling rules, persistent volumes, service discovery and recovery mechanisms.
Add model serving, agent runtime, embeddings, retrieval, approved MCP/tool services, enterprise data connectors and controlled action interfaces.
Instrument logs, traces, metrics, model usage, latency, failures and agent actions. Add dashboards, alerts, audit retention, security review and operational ownership.
Test node failure, switch or path maintenance, SAN failover, replication recovery, backup restore and controlled platform rollback. A backup that has never been restored is not a validated recovery design.
Complete runbooks, access ownership, patching, capacity thresholds, security controls, support escalation, change management and regular disaster-recovery tests.
06 / Cross-cutting controls
Security, governance and observability are not separate boxes at the end
They need to span users, network, orchestration, models, data, storage and agent actions. On-premises infrastructure gives you more control, but it also makes your team responsible for designing and operating those controls correctly.
Enterprise identity & RBAC
Tenant / workload isolation
Encryption in transit & at rest
PII / sensitive-data controls
Agent action auditability
Model & prompt versioning
Telemetry across services
Backup restore testing
Accuracy boundary. The exact tender quantities represented on this page are limited to what is visible in the supplied infrastructure diagram: 4 Layer‑3 ToR switches with 25G/40G/100G, 7 general rack servers, 2 GPU servers with 4 enterprise GPUs total, dual 32G Fibre Channel SAN fabrics, 80 TB primary SAN, 40 TB secondary/replication SAN and 20 TB + 10 TB backup appliances with immutable backup, deduplication and encryption. The platform and zoning sections are reference-design guidance derived from the attached architecture images and supplied source material; they are not claims about a specific vendor product.