Agentic AI Platform Architecture
Overview
The Agentic AI Platform is a unified platform that enables autonomous AI agents to perform complex tasks. It is designed to address challenges encountered when building GenAI services: model serving complexity, lack of framework integration, autoscaling difficulties, absence of MLOps automation, and cost optimization. The platform provides agent orchestration, intelligent inference routing, vector search-based RAG, LLM tracing and cost analysis, horizontal autoscaling, and multi-tenant resource isolation as core capabilities. For detailed analysis of each challenge, see the Technical Challenges document.
This document is intended for solution architects, platform engineers, and DevOps engineers. A basic understanding of Kubernetes and AI/ML workloads is required.
Overall System Architecture
The Agentic AI Platform consists of 6 major layers. Each layer has clear responsibilities and enables independent scaling and operation through loose coupling.
Core Design Principles:
- Self-hosted + External AI Hybrid: Unified management of self-hosted LLMs and external AI Provider APIs through the same gateway
- 2-Tier Cost Tracking: Dual tracking at infrastructure level (model unit price × tokens) and application level (per-agent-step costs)
- MCP/A2A Standard Protocols: Standardized communication between agents and tools (MCP) and between agents (A2A) for interoperability
Layer Roles
Core Components
Agent Runtime
The Agent Runtime is the environment where AI agents execute. Each agent runs as an independent container, with its lifecycle managed by the Agent Controller.
| Feature | Description |
|---|---|
| State Management | Maintains conversation context and task state, checkpointing |
| Tool Execution | Asynchronous execution of registered tools via MCP protocol |
| Memory Management | Combines short-term memory (session) with long-term memory (vector DB) |
| Inter-Agent Communication | Multi-agent collaboration via A2A protocol |
| Error Recovery | Automatic retry and fallback for failed tasks |
Tool Registry
Centrally manages tools available to agents in a declarative manner. Each tool is exposed as an MCP server, allowing agents to invoke them via the standard protocol.
| Tool Type | Purpose | Example |
|---|---|---|
| API Tools | External REST/gRPC service calls | CRM lookup, order processing |
| Search Tools | Vector DB search, document search | RAG context augmentation |
| Code Execution | Code execution in sandbox environments | Data analysis, calculations |
| A2A Tools | Delegating tasks to other agents | Specialist agent collaboration |
Vector DB (RAG Store)
The Vector DB is the core of the RAG system. It converts documents into embedding vectors for storage and provides relevant context via similarity search upon agent requests.
Design Considerations:
- Multi-tenant isolation: Data separation per tenant using Partition Keys
- Index strategy: High-performance Approximate Nearest Neighbor search with HNSW index
- Hybrid search: Improved search quality by combining Dense Vector + Sparse Vector (BM25)
Inference Gateway
The Inference Gateway is a core component that intelligently routes model inference requests. It unifies self-hosted LLMs and external AI providers into a single endpoint.
Routing Strategies:
| Strategy | Description |
|---|---|
| Model-based routing | Distributes to appropriate model backends based on request headers/parameters |
| KV Cache-aware routing | Minimizes TTFT by considering LLM Prefix Cache state |
| Cascade routing | Tries low-cost model first → automatically switches to high-performance model on failure |
| Weight-based routing | Traffic ratio splitting for Canary/Blue-Green deployments |
| Fallback | Automatic failover to alternative provider on outage |
Deployment Architecture
Namespace Structure
Namespaces are separated by function for separation of concerns and security.
| Namespace | Components | Pod Security | GPU |
|---|---|---|---|
| ai-gateway | Inference Gateway, Auth | restricted | - |
| ai-agents | Agent Controller, Agent Pods, Tool Registry | baseline | - |
| ai-inference | LLM Serving Engine, GPU Nodes | privileged | Required |
| ai-data | Vector DB, Cache | baseline | - |
| observability | Tracing, Metrics, Dashboard | baseline | - |
Scalability Design
Horizontal Scaling Strategy
Each component can be horizontally scaled independently.
| Component | Scaling Trigger | Method |
|---|---|---|
| Agent Pod | Message queue length, active session count | Event-driven Autoscaling |
| LLM Serving | GPU utilization, queue depth | HPA + GPU Node Auto-provisioning |
| Vector DB | Query latency, index size | Independent Query/Index Node scaling |
| Cache | Memory utilization | Cluster expansion |
Multi-Tenant Support
Supports multi-tenancy through a combination of namespace isolation, resource quotas, and network policies, enabling multiple teams or projects to share the same platform.
Security Architecture
The Agentic AI Platform applies a 3-layer security model covering external access, internal communication, and data protection.
Agent-Specific Security Considerations:
- Prompt injection defense: Block malicious prompts with an input validation layer (Guardrails)
- Tool execution permission limits: Declaratively define callable tools per agent, applying the principle of least privilege
- PII leakage prevention: Block sensitive information exposure through output filtering
- Execution time limits: Timeout and maximum step count settings to prevent agent infinite loops
- Always enable mTLS in production environments
- Store API keys and tokens in Secrets Manager
- Perform regular security audits and patch vulnerabilities
Data Flow
The complete flow of how user requests are processed through the platform.
Monitoring and Observability
Key Monitoring Areas
| Area | Target Metrics | Purpose |
|---|---|---|
| Agent Performance | Request count, P50/P99 latency, error rate, step count | Agent performance tracking |
| LLM Performance | Token throughput, TTFT, TPS, queue wait time | Model serving performance |
| Resource Usage | CPU, memory, GPU utilization/temperature | Resource efficiency |
| Cost Tracking | Per-tenant/per-model token cost, infrastructure cost | Cost governance |
Example Alert Rules:
- Agent P99 latency > 10s → Warning
- Agent error rate > 5% → Critical
- GPU utilization < 20% (sustained 30 min) → Cost Warning
- Token cost reaches 80% of daily budget → Budget Warning
Platform Requirements
| Area | Required Capability | Description |
|---|---|---|
| Container Orchestration | Managed Kubernetes | GPU node auto-provisioning, declarative workload management |
| Networking | Gateway API support | Intelligent model routing, mTLS, Rate Limiting |
| Model Serving | LLM inference engine | PagedAttention, KV Cache optimization, distributed inference |
| External AI Integration | API Gateway / Proxy | External AI provider integration, Fallback, cost tracking |
| Agent Framework | Workflow engine | Multi-step execution, state management, MCP/A2A protocols |
| Data Layer | Vector DB + Cache | RAG search, session state storage, long-term memory |
| Observability | LLM tracing + metrics | Token cost tracking, Agent Trace analysis, quality evaluation |
| Security | Multi-layer security model | OIDC/JWT, RBAC, NetworkPolicy, Guardrails |
For specific technology stacks and implementation methods, see AWS Native Platform or EKS-Based Open Architecture.
Conclusion
Core principles of the Agentic AI Platform architecture:
- Modularity: Each component can be independently deployed, scaled, and updated
- Hybrid AI: Unified management of self-hosted LLMs and external AI providers
- Standard Protocols: Standardized tool connections and inter-agent communication via MCP/A2A
- Observability: Integrated monitoring of traces, costs, and quality across the entire request flow
- Security: Multi-layer security model + agent-specific security (Guardrails, tool permission limits)
- Multi-tenancy: Multi-team support through namespace isolation, resource quotas, and network policies
Specific methods for implementing this platform architecture are covered in the following documents:
- Technical Challenges — Key challenges faced when building the platform
- AWS Native Platform — Managed service-based implementation
- EKS-Based Open Architecture — EKS + open-source based implementation
References
Official Documentation
- Kubernetes Gateway API — K8s official gateway API
- MCP (Model Context Protocol) — MCP protocol specification
- CNCF Cloud Native Architecture — Cloud native architecture patterns
- OpenTelemetry — Observability standard
Papers / Technical Blogs
- A2A (Agent-to-Agent Protocol) — Google multi-agent communication protocol
- LangChain Architecture Patterns — Agent architecture patterns
- Building Production-Ready LLM Applications — Production LLM engineering
- AWS Well-Architected Framework for AI/ML — AI/ML workload design principles
Related Documents (Internal)
- Technical Challenges — 5 key challenge analysis
- AWS Native Platform — Managed service implementation
- EKS-Based Open Architecture — Self-hosting implementation
- Inference Gateway Routing — 2-Tier Gateway details