I would make the Azure version slightly more advanced than the AWS/GCP versions because Azure now has a particularly strong enterprise story around Microsoft Foundry + Agent 365 + Entra + Defender + Purview.
I also want to update two terms in your original architecture for interview accuracy:
Use “Microsoft Foundry” / “Microsoft Foundry Agent Service” rather than treating the older Azure AI Foundry naming as the whole platform.
“Agent 365” is now a real enterprise control plane for discovering, governing, securing and managing agents across Microsoft and third-party ecosystems. It became generally available for the commercial segment on May 1, 2026. (Microsoft Learn)
Azure API Management now has dedicated AI Gateway capabilities, including token quotas, semantic caching, model routing/governance and MCP management. The dedicated AI Gateway tier is currently preview, so for production architecture I would distinguish the generally available APIM AI-gateway capabilities from the preview tier. (Microsoft Learn)
Azure AI Search now also has “agentic retrieval” and underpins Foundry IQ, Microsoft's managed, permission-aware knowledge layer for agents. (Microsoft Learn)
1. Business scenario
We'll use exactly the same financial-services problem:
“Investigate why counterparty ABC breached its exposure limit yesterday and recommend what action should be taken.”
The solution needs:
FINANCIAL AI PLATFORM
┌────────────────────────────────────────┐
│ │
│ Unstructured Knowledge │
│ ├── Risk policies │
│ ├── Regulatory documents │
│ ├── Credit policies │
│ └── Procedures │
│ │
│ Structured Data │
│ ├── Trades │
│ ├── Exposure │
│ ├── Limits │
│ ├── P&L │
│ └── Risk metrics │
│ │
│ Enterprise Tools │
│ ├── Risk APIs │
│ ├── Trade APIs │
│ ├── Workflow │
│ └── Microsoft 365 │
│ │
└────────────────────────────────────────┘This is therefore:
RAG
+
Agentic AI
+
Enterprise Data
+
Enterprise Tools
+
Security
+
Governance
+
Observability2. Complete Azure architecture
USERS
│
▼
┌─────────────────────┐
│ Azure Application │
│ Gateway + WAF │
└──────────┬──────────┘
│
Azure Firewall
/ Egress Control
│
▼
┌─────────────────────────┐
│ Azure App Service │
│ / Container Apps / AKS │
│ │
│ API │
│ UI │
│ Orchestrator │
└───────────┬─────────────┘
│
▼
┌──────────────────────────┐
│ Azure API Management │
│ AI Gateway │
│ │
│ Auth │
│ Rate limits │
│ Token quotas │
│ Model routing │
│ Semantic caching │
│ Cost tracking │
│ MCP governance │
└────────────┬─────────────┘
│
┌────────────┼──────────────┐
│ │ │
▼ ▼ ▼
Microsoft Azure AI Agent
Foundry Search Tools
│ │ │
│ │ ├── Functions
│ │ ├── Logic Apps
│ │ ├── APIs
│ │ ├── SQL
│ │ └── MCP
│ │
▼ ▼
Foundry Models RAG / Vector
/ Model Catalog / Hybrid Search
│
▼
Foundry Agent
Service
│
▼
Model Armor /
Content Safety
│
▼
Human Approval
│
▼
USERSecurity and governance wrap around the entire architecture:
Entra ID
│
├── Identity
├── RBAC
└── Managed Identity
Agent 365
│
├── Agent inventory
├── Governance
├── Ownership
├── Lifecycle
└── Risk controls
Defender
│
├── Threat detection
└── Runtime protection
Purview
│
├── Data governance
├── Classification
└── Compliance
Private Endpoints
│
└── Private connectivity3. Perimeter
The request first hits:
Internet
│
▼
Azure Application Gateway
│
▼
WAF
│
├── SQL injection
├── XSS
├── malicious payloads
├── IP rules
└── HTTP attacks
│
▼
BackendFor a larger global architecture I would consider:
Azure Front Door
│
▼
Application Gateway
│
▼
Regional workloadThat gives:
Global traffic management
+
Regional ingress
+
WAF
+
Application routing4. Compute layer
Your original proposal uses App Service.
That is perfectly reasonable for:
Frontend
REST APIs
Lightweight orchestration
Stateless servicesBut for a senior architect interview, explain the alternatives:
Azure Compute
│
┌──────────────┼──────────────┐
▼ ▼ ▼
App Service Container Apps AKS
│ │ │
Simple Serverless Complex
APIs containers KubernetesMy decision:
App Service
↓
Simple enterprise web/API application
Container Apps
↓
Microservices + event-driven agents
AKS
↓
Complex Kubernetes platform,
GPU/custom models,
advanced schedulingFor our architecture:
Application Gateway
│
▼
Azure Container Apps / App Service
│
├── API
├── UI
└── OrchestrationAnd use AKS only where justified.
5. Azure API Management as AI Gateway
This is one of the most important parts.
Do not allow:
App
│
├────► Gemini
├────► Claude
├────► GPT
└────► other modelsInstead:
Applications
│
▼
Azure API Management
AI Gateway
│
┌───────────┼───────────┐
│ │ │
Security Routing Cost
│ │ │
▼ ▼ ▼
IAM Model choice Tokens
│
┌───────────┼───────────┐
▼ ▼ ▼
Foundry Azure External
Models OpenAI ModelsAzure APIM's current AI gateway capabilities include token rate limiting, semantic caching, model routing/governance and support for model APIs from multiple providers. (Microsoft Learn)
6. Token governance
Imagine three departments:
Risk
Compliance
ResearchWithout a gateway:
Risk
│
└── consumes entire model quota
Compliance
│
└── 429 errors
Research
│
└── 429 errorsAPIM solves this with token-aware policies.
Conceptually:
Risk
│
▼
100K TPM
Compliance
│
▼
50K TPM
Research
│
▼
25K TPMAzure API Management supports token limits based on consumers such as subscription keys or arbitrary policy keys. (Microsoft Learn)
7. Semantic caching
This is particularly useful for enterprise assistants.
User asks:
“What is the counterparty limit policy?”
Another user asks:
“What is the approved exposure limit policy for counterparties?”
The prompts aren't identical but may be semantically equivalent.
Instead of:
Request
↓
LLM
↓
expensive inferencewe can have:
Request
↓
Semantic Cache
│
├── HIT ──► cached response
│
└── MISS
↓
LLMAzure APIM supports semantic caching with Azure Managed Redis or another compatible external cache. (Microsoft Learn)
8. Microsoft Foundry
Now we reach the AI platform.
Conceptually:
Microsoft Foundry
│
┌───────────────┼────────────────┐
│ │ │
▼ ▼ ▼
Models Agents Evaluation
│ │ │
▼ ▼ ▼
Model Catalog Agent Service TestingFoundry becomes the AI engineering platform.
The architecture is:
Developer
│
▼
Foundry
│
├── Model selection
├── Agent development
├── Tools
├── Evaluation
├── Deployment
└── Monitoring9. Foundry Agent Service
The agent receives:
"Investigate ABC exposure breach."It can:
PLAN
↓
retrieve information
↓
call tools
↓
reason
↓
retrieve more information
↓
validate
↓
recommend actionArchitecture:
USER
│
▼
Foundry Agent
Service
│
┌─────┴─────┐
│ Planner │
└─────┬─────┘
│
┌──────────────┼──────────────┐
▼ ▼ ▼
RAG Tools APIs
│ │ │
▼ ▼ ▼
AI Search Functions Enterprise
systems10. Agent state
Your original proposal uses Cosmos DB.
That's a good architectural choice.
Agent
│
▼
Cosmos DB
│
┌──────────────┼──────────────┐
▼ ▼ ▼
conversation state metadata
historyExample:
{
"conversation_id": "abc-123",
"user_id": "u-987",
"tenant_id": "bank-a",
"agent": "risk-investigator",
"state": {
"counterparty": "ABC",
"exposure": 125000000,
"limit": 100000000,
"breach": 25000000
}
}But I would separate:
Conversation state
↓
Cosmos DB
Financial source of truth
↓
PostgreSQL / Fabric / Databricks / core systemsNever make agent memory the financial system of record.
11. RAG
Now the knowledge layer.
Documents:
Azure Blob Storage
│
├── Credit Policy.pdf
├── Market Risk Policy.pdf
├── Counterparty Policy.pdf
├── Basel Regulations.pdf
└── Internal Procedures.pdfPipeline:
Blob Storage
│
▼
Azure AI Search
│
┌───────────┼───────────┐
▼ ▼ ▼
Chunk Embed Metadata
│ │ │
└───────────┼───────────┘
▼
Search Index
│
┌──────────┼──────────┐
▼ ▼ ▼
Keyword Vector Hybrid
Search Search SearchAzure AI Search currently supports vector, keyword, hybrid and multimodal search. (Microsoft Learn)
12. Hybrid search
Don't rely exclusively on vector search.
For financial applications:
User:
"Find policy CR-1024 regarding
BBB- rated counterparties."The exact identifier:
CR-1024is better handled by lexical search.
But:
"What happens if the counterparty's
credit quality deteriorates?"is better suited to semantic retrieval.
Therefore:
Query
│
┌────────┴────────┐
▼ ▼
Keyword Search Vector Search
│ │
└────────┬────────┘
▼
Hybrid ranking
│
▼
Top documentsAzure AI Search explicitly supports hybrid vector + keyword queries. (Microsoft Learn)
13. Agentic retrieval
This is an important newer Azure capability.
Instead of a simplistic:
question
↓
vector search
↓
documentsagentic retrieval can reason about the retrieval process itself.
Conceptually:
User Question
│
▼
Retrieval Agent
│
├── Understand question
├── Break into subqueries
├── Search
├── Filter
├── Rank
└── Produce evidence
│
▼
Main AgentAzure AI Search documentation now includes agentic retrieval and identifies Azure AI Search as the foundation for Foundry IQ's permission-aware knowledge layer. (Microsoft Learn)
14. Security-aware RAG
This is critical for banking.
Suppose:
Consultant A
↓
Client A
Consultant B
↓
Client BThe search index contains:
{
"document_id": "DOC-123",
"tenant_id": "BANK-A",
"classification": "CONFIDENTIAL",
"business_unit": "MARKET_RISK"
}The retrieval request must incorporate authorization context.
User
│
▼
Entra ID
│
▼
Claims
│
├── tenant
├── role
├── region
└── classification
│
▼
Azure AI Search
│
▼
Authorized resultsAzure AI Search supports role-based access approaches, and current Microsoft documentation emphasizes permission-aware retrieval through the Foundry IQ knowledge layer. (Microsoft Learn)
15. Microsoft Entra ID
Entra becomes the identity backbone.
Entra ID
│
┌──────────────┼──────────────┐
▼ ▼ ▼
Users Agents Services
│ │ │
▼ ▼ ▼
RBAC Agent Identity Managed IdentityAvoid:
API_KEY = "xxxxxxxx"Prefer:
Managed Identity
│
▼
Entra
│
▼
Azure resourceThis eliminates unnecessary secret distribution.
16. Azure Agent 365
This is where your Azure architecture becomes substantially different from AWS/GCP.
Think of Agent 365 as the enterprise agent control plane.
Agent 365
│
┌──────────────┼─────────────────┐
▼ ▼ ▼
Inventory Governance Security
│ │ │
▼ ▼ ▼
Agent registry Policies Threat detection
Ownership Permissions Runtime protection
Lifecycle Compliance RiskMicrosoft describes Agent 365 as a centralized control plane for discovering, managing, governing and securing agents across Microsoft and third-party ecosystems. (Microsoft Learn)
17. Agent lifecycle
This is an excellent interview concept.
CREATE
│
▼
TEST
│
▼
EVALUATE
│
▼
APPROVE
│
▼
REGISTER
│
▼
DEPLOY
│
▼
MONITOR
│
▼
AUDIT
│
▼
RETIREAgent 365 provides governance capabilities around onboarding, ownership, activation, blocking and lifecycle management. (Microsoft Learn)
18. Agent identity
A modern Azure architecture should treat an agent as an identity-bearing enterprise entity.
Human
│
▼
Entra Identity
│
▼
Agent
│
▼
Agent Identity
│
├── permissions
├── owner
├── environment
├── risk classification
└── allowed toolsThen:
Risk Agent
│
├── READ exposure
├── READ policies
├── READ trades
└── REQUEST approval
NOT:
├── DELETE trades
├── change credit limit
└── transfer money19. Defender
Add Microsoft Defender around runtime security.
Agent
│
▼
Defender
│
├── threat detection
├── malicious behavior
├── suspicious activity
└── runtime protectionMicrosoft's current Agent 365 security model integrates Defender capabilities for continuous threat detection and runtime protection. (Microsoft Learn)
20. Purview
Purview answers a different question:
"What data is this, who is allowed to access it, and what compliance rules apply?"
Architecture:
Microsoft Purview
│
┌────────────────┼────────────────┐
▼ ▼ ▼
Classification Lineage Compliance
│ │ │
▼ ▼ ▼
Confidential Data flow Policies
PII dependencies
FinancialFor a financial-services GenAI platform:
Agent
↓
Data request
↓
Purview classification/governance
↓
Authorized data21. Model safety
Use the current Microsoft AI safety stack rather than relying on one control.
Conceptually:
USER
│
▼
APIM
│
▼
Input safety
│
▼
Foundry Agent
│
▼
Model
│
▼
Output safety
│
▼
APIM
│
▼
USERFor interview purposes, distinguish:
Application security
↓
WAF / Firewall / Entra
Data security
↓
Purview / RBAC / Private Endpoint
AI safety
↓
Content safety / model safety controls
Agent security
↓
Agent 365 / Defender
LLM grounding
↓
Azure AI Search / RAG / citationsDon't claim that any single component "prevents hallucinations."
22. Data architecture
Now incorporate your Microsoft data stack.
DATA PLATFORM
│
┌───────────────────┼────────────────────┐
│ │ │
▼ ▼ ▼
Microsoft Fabric Databricks Operational DBs
│ │ │
▼ ▼ ▼
OneLake Lakehouse PostgreSQL
│ │ │
└───────────────────┼────────────────────┘
▼
Agent / AnalyticsI would use Fabric for:
Enterprise analytics
OneLake
Power BI
Data integration
Governed analyticsDatabricks for:
Advanced data engineering
Spark
ML workloads
Existing Databricks estatePostgreSQL for:
Transactional application data
Agent metadata
Business servicesCosmos DB for:
Agent state
Conversation history
High-scale JSON/session dataRedis for:
Semantic cache
Session cache
Hot data
Rate-related state23. Microsoft Fabric
For your financial architecture:
Microsoft Fabric
│
┌───────────────┼─────────────────┐
▼ ▼ ▼
OneLake Data Factory Power BI
│ │ │
▼ ▼ ▼
Historical Pipelines Executive
financial data dashboardThen:
Agent
│
├── RAG → AI Search
│
└── Analytics → FabricThis is a powerful separation.
24. Agent tools
Define deterministic tools.
async def get_counterparty_exposure(
counterparty_id: str
):
...
async def get_counterparty_limit(
counterparty_id: str
):
...
async def get_recent_trades(
counterparty_id: str,
hours: int
):
...
async def search_risk_policy(
question: str
):
...
async def create_risk_case(
counterparty_id: str,
reason: str
):
...The agent gets capabilities, not unrestricted access.
25. Azure Functions
Functions are ideal for short deterministic operations.
Agent
│
▼
Azure Function
│
├── Validate
├── Calculate
├── Transform
├── Call API
└── Return resultExample:
def calculate_breach(exposure, limit):
breach = max(exposure - limit, 0)
return {
"exposure": exposure,
"limit": limit,
"breach": breach,
"is_breach": breach > 0
}This is better than asking the LLM to perform every business calculation.
26. Logic Apps
Use Logic Apps for enterprise workflow integration.
Agent
│
▼
Logic Apps
│
├── Send email
├── Teams notification
├── Create approval
├── Update CRM
├── Create ServiceNow case
└── Trigger workflowExample:
Agent detects breach
│
▼
Logic Apps
│
▼
Risk Manager
│
▼
Microsoft Teams
│
▼
Approval27. Microsoft 365 integration
This is where Azure becomes especially attractive for Microsoft-heavy enterprises.
Agent
│
┌───────────┼────────────┐
▼ ▼ ▼
Outlook Teams SharePoint
│ │ │
▼ ▼ ▼
Email Approval DocumentsThe agent could:
Find policy
↓
Investigate breach
↓
Create Teams approval
↓
Send Outlook notification
↓
Update SharePoint caseBut permissions must remain delegated and governed rather than allowing the agent unrestricted Microsoft 365 access.
28. Complete agent workflow
User:
“Investigate ABC's exposure breach.”
USER
│
▼
Application Gateway
│
▼
WAF
│
▼
App Service / Container Apps
│
▼
API Management
│
▼
Entra authentication
│
▼
Foundry Agent Service
│
├── get_exposure()
│ │
│ ▼
│ PostgreSQL/API
│
├── get_limit()
│ │
│ ▼
│ PostgreSQL
│
├── get_recent_trades()
│ │
│ ▼
│ Databricks/Fabric/API
│
├── search_policy()
│ │
│ ▼
│ Azure AI Search
│
└── calculate_breach()
│
▼
Azure Function
│
▼
Agent reasoning
│
▼
Recommendation
│
▼
Human approval
│
▼
Logic Apps
│
▼
Teams / Outlook / workflow29. Example result
The tools return:
Counterparty: ABC
Exposure:
$125M
Approved limit:
$100M
Breach:
$25M
Primary driver:
Trade XYZ
Policy:
Counterparty Policy §4.3The agent produces:
ABC currently exceeds its approved counterparty
limit by $25M.
The breach is primarily attributable to Trade XYZ.
The applicable policy requires escalation to
Risk Control.
Recommendation:
1. Escalate the breach.
2. Review Trade XYZ.
3. Obtain Risk Control approval before changing
the exposure limit.30. Human-in-the-loop
Never allow:
Agent
↓
Change credit limitInstead:
Agent
↓
Recommendation
↓
Logic Apps
↓
Approval
↓
Risk Manager
┌───────────────┐
│ │
▼ ▼
APPROVE REJECT
│ │
▼ ▼
Execute StopFor regulated financial workloads, this is one of the strongest architecture decisions you can explain in an interview.
31. MCP
For a modern Azure architecture, add MCP.
Agent
│
▼
MCP Client
│
┌────────────────┼────────────────┐
▼ ▼ ▼
Risk MCP Trade MCP Policy MCP
│ │ │
▼ ▼ ▼
Risk API Trade API AI SearchAzure API Management can expose and govern MCP servers, and its AI gateway capabilities also include MCP-related governance. (Microsoft Learn)
This is particularly useful when you have:
Many agents
+
Many tools
+
Many teams32. Multi-agent architecture
For a more sophisticated financial platform:
Supervisor Agent
│
┌────────────────┼────────────────┐
▼ ▼ ▼
Risk Agent Compliance Agent Trade Agent
│ │ │
▼ ▼ ▼
Risk APIs Policies Trade APIs
│ │ │
└────────────────┼────────────────┘
▼
Decision Agent
│
▼
Human ApprovalBut don't use multi-agent just because you can.
A strong interview statement:
“I start with a single agent and deterministic tools. I introduce specialized agents only when responsibility boundaries, security isolation, independent evaluation or organizational ownership justify the additional orchestration complexity.”
33. Agent 365 across the architecture
This is important.
Agent 365 should not be thought of as another model or agent framework.
Think:
AGENT 365
ENTERPRISE CONTROL
PLANE
│
┌─────────────┼──────────────┐
▼ ▼ ▼
FOUNDry Copilot 3rd Party
Agents Agents Agents
│ │ │
└─────────────┼──────────────┘
▼
Common governanceMicrosoft says Agent 365 can also surface agents built on third-party platforms such as Google Vertex AI and Amazon Bedrock in the Microsoft 365 admin center. (Microsoft Learn)
That is a very important enterprise capability.
It means your enterprise architecture can be:
Azure Agent
+
AWS Agent
+
GCP Agent
+
Microsoft 365 Agent
↓
Agent 365 governance34. Evaluation
Your original list:
F1
BLEU
GLEU
METEOR
ROUGEis useful, but I would modernize the evaluation framework.
For an enterprise RAG/agent platform:
EVALUATION
│
┌─────────────┼─────────────┐
▼ ▼ ▼
Retrieval Answer Agent
│ │ │
▼ ▼ ▼
Precision Correctness Tool selection
Recall Grounding Tool arguments
MRR Relevance Trajectory
NDCG Citation Safety35. Traditional NLP metrics
Use:
BLEU
ROUGE
METEOR
GLEUwhen appropriate for text-overlap-oriented tasks.
But for modern GenAI:
BLEU
ROUGEalone are insufficient.
For RAG:
Context precision
Context recall
Faithfulness
Groundedness
Answer relevance
Citation correctnessFor agents:
Tool selection accuracy
Tool argument accuracy
Trajectory correctness
Task completion
Policy compliance
Safety
Latency
Cost36. Evaluation pipeline
Historical questions
│
▼
Evaluation Dataset
│
▼
Agent
│
▼
┌─────────────────────────┐
│ Evaluation │
│ │
│ Retrieval │
│ Grounding │
│ Answer │
│ Tool usage │
│ Safety │
│ Cost │
│ Latency │
└────────────┬────────────┘
│
▼
Scorecard
│
┌─────┴─────┐
▼ ▼
PASS FAIL
│ │
▼ ▼
Production Improve37. Observability
Use Azure Monitor ecosystem:
Application
│
├── Azure Monitor
├── Application Insights
├── Log Analytics
└── Distributed tracing
│
▼
ObservabilityCapture:
Request ID
Tenant ID
Agent ID
Model
Prompt tokens
Completion tokens
Latency
Tool calls
RAG retrieval
Search scores
Errors
Retries
Guardrail events
Cost38. Agent trajectory
Don't log only:
Request → ResponseLog:
Request
│
▼
Agent
│
├── RAG
│ └── AI Search
│
├── Tool
│ └── Exposure API
│
├── Tool
│ └── Trade API
│
├── Function
│
└── Model
│
▼
ResponseThis lets you diagnose:
Why was the answer wrong?
Why was the agent slow?
Why did it call the wrong tool?
Why did it retrieve the wrong document?
Why did cost increase?39. Scaling
This directly addresses your earlier architecture concern.
Don't autoscale GenAI applications only using CPU.
Monitor:
CPU
Memory
Request rate
Concurrent requests
Queue depth
LLM latency
RAG latency
Token rate
Search latency
Tool latency
Cache hit rateArchitecture:
Traffic
│
▼
App Gateway
│
▼
Container
Apps
│
┌──────┴───────┐
▼ ▼
Instance 1 Instance 2
│ │
└──────┬───────┘
▼
APIM
│
▼
ModelFor self-hosted models on AKS:
GPU utilization
GPU memory
batch size
tokens/sec
queue depth
KV cache
concurrencymust also be monitored.
40. Failure handling
Production AI must assume failures.
AI model failure
Primary model
│
X
│
▼
Fallback model
│
X
│
▼
Graceful degradationAI Search failure
Agent
│
▼
AI Search
│
X
│
▼
Retry
│
▼
Circuit breaker
│
▼
"Knowledge retrieval unavailable"Enterprise API failure
Agent
│
▼
Risk API
│
X
│
▼
Retry
│
▼
Timeout
│
▼
No recommendationNever hallucinate missing financial data.
41. Private networking
Your proposed architecture is correct in principle, but I would draw it more explicitly:
INTERNET
│
▼
Application Gateway
│
WAF
│
▼
Azure VNet
│
┌───────────┴────────────┐
│ │
▼ ▼
App Service / Container Apps
private access
│ │
└───────────┬────────────┘
│
API Management
│
Private Endpoint
│
┌────────────────┼─────────────────┐
▼ ▼ ▼
AI Search Cosmos DB PostgreSQL
│ │ │
└────────────────┼─────────────────┘
▼
Azure ServicesAnd outbound traffic:
Private workloads
│
▼
Azure Firewall
│
├── ALLOW approved destination
│
└── BLOCK everything else42. Complete security architecture
USER
│
▼
Application Gateway
│
WAF
│
▼
Azure VNet
│
Azure Firewall
│
▼
App Service / Container Apps
│
▼
API Management
│
Entra ID
│
▼
Foundry Agent Service
│
┌────────────────┼─────────────────┐
▼ ▼ ▼
AI Search Cosmos DB Enterprise APIs
│ │ │
└────────────────┼─────────────────┘
│
Private Endpoints
│
┌────────┴────────┐
▼ ▼
Purview Defender
│ │
└────────┬────────┘
▼
Agent 36543. Complete production architecture
This is the diagram I would memorize.
USERS
│
▼
┌────────────────────────┐
│ Azure Front Door │
│ / Application Gateway │
└───────────┬────────────┘
│
WAF
│
▼
┌───────────────────────┐
│ Azure VNet │
│ │
│ App Service │
│ Container Apps │
│ AKS │
└───────────┬───────────┘
│
▼
┌──────────────────────────┐
│ Azure API Management │
│ AI Gateway │
│ │
│ Auth │
│ Token quotas │
│ Rate limits │
│ Model routing │
│ Semantic cache │
│ MCP governance │
│ Cost tracking │
└────────────┬─────────────┘
│
┌────────────┼────────────────┐
│ │ │
▼ ▼ ▼
Microsoft Azure AI Tool Layer
Foundry Search │
│ │ │
┌─────────┼──────┐ │ ┌──────┼──────┐
│ │ │ │ ▼ ▼ ▼
Models Agent Eval RAG Functions Logic APIs
│ Service │ Apps
│ │ │
▼ ▼ ▼
Foundry Agent Vector/
Models Runtime Hybrid Search
│ │ │
└─────────┼───────────┘
│
▼
Enterprise Data
│
┌──────────┼─────────────┐
▼ ▼ ▼
Cosmos DB PostgreSQL Fabric/Databricks
│ │
└──────────┬──────────────┘
▼
Business Systems
│
▼
Human Approval
│
▼
USER
┌───────────────────────────────────────────┐
│ ENTERPRISE CONTROL PLANE │
│ │
│ Entra ID │
│ Agent 365 │
│ Defender │
│ Purview │
│ Azure Monitor │
│ Application Insights │
│ Azure Firewall │
│ Private Endpoints │
└───────────────────────────────────────────┘44. Azure mental model
For your interview, remember:
AWS GCP AZURE
CloudFront Cloud LB Front Door
WAF Cloud Armor WAF
ALB Cloud LB App Gateway
ECS/EKS Cloud Run/GKE App Service/
Container Apps/AKS
Bedrock Gemini Platform Microsoft Foundry
Bedrock KB Vertex/GCP RAG Azure AI Search
OpenSearch BigQuery Vector AI Search
Aurora Cloud SQL PostgreSQL
IAM IAM Entra ID
KMS Cloud KMS Key Vault
PrivateLink PSC Private Endpoint
CloudWatch Monitoring Azure Monitor
X-Ray Trace App Insights
S3 Cloud Storage Blob Storage
SQS/SNS Pub/Sub Service Bus/Event Grid
Redis Memorystore Azure Managed Redis
SageMaker Vertex AI Foundry/ML
API Gateway API Gateway APIM
Guardrails Model Armor AI safety controls
Agent governance Agent Platform Agent 365Azure enterprise ready
Enterprise AI
│
├── Identity
│ └── Entra
│
├── AI Gateway
│ └── APIM
│
├── AI Platform
│ └── Foundry
│
├── Knowledge
│ └── AI Search / Foundry IQ
│
├── Data
│ └── Fabric / Databricks / Cosmos /
│ PostgreSQL
│
├── Agent Governance
│ └── Agent 365
│
├── Security
│ └── Defender / WAF / Firewall
│
├── Data Governance
│ └── Purview
│
├── Networking
│ └── Private Endpoints
│
└── Observability
└── Azure MonitorThat is the architecture-level answer.
