Domain 1 of 5 · Chapter 3 of 4

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 loopCapacity and costModel behaviorIngestion and index healthIdentity and network access
What you watchTokens and requests per minute against quota, 429 rate, spend against forecastAnswer quality, safety events, grounding quality, latency per spanIndexer run outcomes, documents in the index, storage and vector quota, relevanceWhich principal called, with what credential, from which network
Primary instrumentFoundry Quota page, Cost Management budgets, API Management gateway metricsScheduled and continuous evaluation, scheduled red teaming, Application Insights tracesIndexer execution history, debug sessions, search unit sizingEntra ID sign-in data and role assignments, diagnostic setting Audit logs
Reports or enforcesQuota enforces rate; budgets only reportEvaluators only report; they change no behavior on their ownFailure thresholds report; storage and vector quotas enforce hard write limitsRoles and disableLocalAuth enforce; logs report
What it cannot seeA monetary ceiling, which quota cannot expressSlow drift, which an adaptive threshold absorbs into its bandDeleted source documents, which no indexer detects unaidedAny call made with a key, which carries no principal to attribute
First move on a symptomBack off on Retry-After, then reallocate quota, add spillover, or pool behind a gatewayReplay the fixed evaluation dataset before changing the model or promptRead that execution's specific errors, then run a single-document debug sessionCheck the assignment scope and the auth path before granting more permissions

Decision tree

Is the request rejected?yesno, it answeredWhich rejection is it?429, or 403 forbiddenAnswer wrong, or missing?the call returned normally429403answer wrongcontent missing429 rate limitbackoff on retry-after,then quota or spillover403 forbiddencheck the Foundry roleand the auth pathAnswer qualityreplay the heldevaluation datasetDeleted at the source?the document is missingyesnoDeletion policynative soft delete ora soft-delete column,in place from run oneSkipped documentsthat execution's specificerrors, then a debugsession on one documentRead the requirement's verb: report, prevent, attribute, detect, or reach.

Cheat sheet

  • Foundry quota is expressed in tokens per minute and requests per minute and is a throughput control, not a spending control
  • Onboarded models pool quota across the subscription, and the Quota page Scope column tells you which system applies
  • An HTTP 429 means the token or request rate limit was exceeded, and the documented remedy is backoff honoring Retry-After plus quota reallocation
  • Spillover routes overflow from a saturated provisioned deployment to a standard deployment in the same Foundry resource
  • Scaling past one deployment's quota ceiling is done by fronting several deployments with an AI gateway backend pool, which is also where per-consumer token limits and chargeback metrics live
  • Cached input tokens do not consume PTU capacity, so cache rate directly changes how many PTUs a deployment needs
  • Agentic retrieval bills on tokens across two services, unlike the classic pipeline's per-query semantic ranker charge
  • Azure Reservations discount provisioned capacity relative to hourly billing for steady production volume
  • Cost Management budgets are evaluated periodically and are notification-only by default, so they cannot stand in for an approval gate
  • Continuous evaluation scores live production traffic at a configured sampling rate and only belongs after the evaluator is trusted
  • Scheduled evaluation replays a fixed test dataset on a cadence to expose system drift, and scheduled red teaming probes adversarially
  • Foundry tracing follows OpenTelemetry GenAI semantic conventions and records inputs, outputs, tool calls and results, token consumption, and latency per span
  • Azure Monitor dynamic thresholds adapt to recent history, so a gradual multi-week climb never triggers them
  • Traces do not yet support a network-isolated Application Insights resource
  • Centralizing AI resource logs requires an Azure Monitor diagnostic setting on each Foundry resource, which is a different mechanism from Application Insights GenAI tracing
  • An indexer must be set to the private execution environment to traverse private endpoints, or it fails silently and leaves the index empty
  • An indexed knowledge source needs a search index carrying a semantic configuration, because the pipeline reranks every subquery with the semantic ranker
  • The @search.score value is an unbounded relevance score for a query match, not a confidence you can threshold
  • The retrieve response can return source references and an execution activity log showing which subqueries hit which sources with what parameters
  • Indexers detect changed source content automatically but never detect deletions, so without a deletion detection policy removed documents keep grounding answers
  • Replicas buy query throughput and the SLA, partitions buy storage and indexing throughput, and an exhausted partition quota fails indexing rather than validation
  • A shared private link is the search service's own outbound private connection, created on the search service and approved by the target resource's owner
  • Raising the failed-item thresholds lets an indexer run finish by skipping documents, so a missing document is found in that execution's specific errors and a debug session, never in the run's overall status
  • Foundry User is the least-privilege role for developers building and testing agents: reader access plus project data actions and nothing more
  • Foundry Agent Consumer is the least-privilege role for principals that only call agent endpoints, and it can be scoped to a single agent
  • Roles beginning with Cognitive Services and the Azure AI Developer role are documented as wrong for Foundry project scenarios
  • Entra ID authentication issues short-lived per-principal tokens and RBAC only applies when you use it; disabling local auth is what forces it
  • Inbound isolation is governed by the public network access flag, and adding or removing a private endpoint does not change it
  • A user-assigned managed identity can be created and granted downstream roles before the Foundry resource exists and outlives it; a system-assigned identity cannot

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References

  1. Microsoft Foundry Models quotas and limits
  2. Manage quota for Foundry Models deployments
  3. Manage traffic with spillover for provisioned deployments
  4. AI gateway capabilities in Azure API Management
  5. Limit large language model API token usage (llm-token-limit policy)
  6. Understand costs associated with provisioned throughput units
  7. Prompt caching with Foundry Models
  8. Agentic retrieval in Azure AI Search
  9. Tutorial: Create and manage Azure budgets
  10. Tracing for AI applications and agents
  11. Create diagnostic settings in Azure Monitor
  12. Observability for generative AI in Microsoft Foundry
  13. Continuously evaluate your AI agents
  14. Dynamic thresholds in Azure Monitor metric alert rules
  15. Create an indexer in Azure AI Search
  16. Debug Sessions in Azure AI Search
  17. Index changed and deleted blobs in Azure AI Search
  18. Estimate and manage capacity of an Azure AI Search service
  19. Configure private link for Microsoft Foundry
  20. Make outbound connections through a shared private link
  21. Relevance and scoring in Azure AI Search
  22. Role-based access control for Microsoft Foundry
  23. Authentication and authorization in Microsoft Foundry
  24. Disable local authentication for Azure AI services
  25. What are managed identities for Azure resources?