Managing, Monitoring, and Securing AI Systems
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Included in this chapter:
- Four control loops, and which symptom belongs to each
- How Foundry quota is scoped and enforced
- Absorbing overflow above one deployment's ceiling
- Where the token spend accrues
- Reporting spend versus enforcing it
- Two observability switches: traces and resource logs
- Evaluation cadences and what each one can prove
- Alert thresholds and the drift they miss
- What a completed indexer run hides
- Keeping the index true to its source
- Sizing the search service: replicas and partitions
- Inbound and outbound are separate network objects
- Reading relevance and retrieval signals
- Choosing the role that fits the principal
- Keyless credentials and the identity you attach
- Exam-pattern recognition on this objective
The four control loops after go-live
| Control loop | Capacity and cost | Model behavior | Ingestion and index health | Identity and network access |
|---|---|---|---|---|
| What you watch | Tokens and requests per minute against quota, 429 rate, spend against forecast | Answer quality, safety events, grounding quality, latency per span | Indexer run outcomes, documents in the index, storage and vector quota, relevance | Which principal called, with what credential, from which network |
| Primary instrument | Foundry Quota page, Cost Management budgets, API Management gateway metrics | Scheduled and continuous evaluation, scheduled red teaming, Application Insights traces | Indexer execution history, debug sessions, search unit sizing | Entra ID sign-in data and role assignments, diagnostic setting Audit logs |
| Reports or enforces | Quota enforces rate; budgets only report | Evaluators only report; they change no behavior on their own | Failure thresholds report; storage and vector quotas enforce hard write limits | Roles and disableLocalAuth enforce; logs report |
| What it cannot see | A monetary ceiling, which quota cannot express | Slow drift, which an adaptive threshold absorbs into its band | Deleted source documents, which no indexer detects unaided | Any call made with a key, which carries no principal to attribute |
| First move on a symptom | Back off on Retry-After, then reallocate quota, add spillover, or pool behind a gateway | Replay the fixed evaluation dataset before changing the model or prompt | Read that execution's specific errors, then run a single-document debug session | Check the assignment scope and the auth path before granting more permissions |
Decision tree
Cheat sheet
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References
- Microsoft Foundry Models quotas and limits
- Manage quota for Foundry Models deployments
- Manage traffic with spillover for provisioned deployments
- AI gateway capabilities in Azure API Management
- Limit large language model API token usage (llm-token-limit policy)
- Understand costs associated with provisioned throughput units
- Prompt caching with Foundry Models
- Agentic retrieval in Azure AI Search
- Tutorial: Create and manage Azure budgets
- Tracing for AI applications and agents
- Create diagnostic settings in Azure Monitor
- Observability for generative AI in Microsoft Foundry
- Continuously evaluate your AI agents
- Dynamic thresholds in Azure Monitor metric alert rules
- Create an indexer in Azure AI Search
- Debug Sessions in Azure AI Search
- Index changed and deleted blobs in Azure AI Search
- Estimate and manage capacity of an Azure AI Search service
- Configure private link for Microsoft Foundry
- Make outbound connections through a shared private link
- Relevance and scoring in Azure AI Search
- Role-based access control for Microsoft Foundry
- Authentication and authorization in Microsoft Foundry
- Disable local authentication for Azure AI services
- What are managed identities for Azure resources?