Skip to main content
The Agentic Platform provides a production-ready, multi-cloud infrastructure for building and deploying AI agents. This architecture uses Kubernetes, GitOps, and enterprise-grade observability to deliver 99.9% availability across any cloud provider.

​
Architecture overview

Agentic Platform multi-cloud architecture diagram

​
Platform layers

The architecture diagram shows five foundational layers that work together:
  • Kubernetes: Container orchestration across any cloud provider
  • Service Mesh: Istio for traffic management, security, and observability
  • Container Runtime: Docker for containerization
  • Packaging: Helm charts for deployment configuration
  • Vector Storage: PostgreSQL with pgvector for embeddings and RAG
  • Caching: Redis for session state and fast lookups
  • Object Storage: Cloud-native storage for artifacts and models
  • Message Bus: Azure Service Bus for async workflows
  • Registries: Container registries (ACR/ECR/GCR) and package registries (GitHub Packages)
  • Provider Abstraction: Unified interface to Azure AI Foundry, OpenAI, Gemini, Anthropic, or BYO models
  • Policy Enforcement: Guardrails, rate limiting, cost management, and content safety
  • Traffic Management: Semantic caching, load balancing, and intelligent request routing
  • PII Protection: Automatic detection and sanitization
  • SDK Access: BB AI SDK for standardized integration
  • Agent Workloads: FastAPI-based APIs with background workers
  • Orchestration: Agno and LangGraph for multi-step workflows
  • MCP Integration: Model Context Protocol servers for tool access
  • Banking Services: Pre-integrated domain services (deposits, payments, loans, fraud)
  • Guardrails and Safety: Real-time evaluations, content safety filters, PII detection/sanitization, jailbreak prevention, prompt validation
  • Observability: OpenTelemetry traces, Langfuse runs, metrics, and logs
  • Ingress: API Management (APIM) with DNS, WAF, SSL termination
  • GitOps: Argo CD for declarative, automated deployments
  • CI/CD: Automated pipelines with PR checks, builds, and releases
  • Self-Service: Repository provisioning and infrastructure automation
  • Monitoring: Grafana dashboards, PagerDuty alerts, real-time evaluations

​
Runtime flow

Agentic Platform runtime request flow
When a client makes a request:
  1. Client → APIM → Agent API: Request enters through API Management with authentication and rate limiting
  2. Agent → AI Gateway: Agent calls LLM through gateway with guardrails and traffic control applied
  3. AI Gateway → LLM Provider: Request routed to selected provider (Azure AI Foundry or BYO) with policies enforced
  4. Tool Execution: Agent calls MCP servers (banking domains) or direct REST/GraphQL APIs
  5. Observability: OTel traces and Langfuse runs capture every step; logs and metrics flow to platform sinks
  6. Real-time Evaluation: Automated evaluations flag risks (safety, PII, jailbreaks) and feed continuous improvements

​
Next steps

Technology stack

Explore the detailed technology stack and implementation deep dive

Getting started

Start building your first AI agent on the platform