Section 9: Cost Governance & Resilience Controls

Q9.1: How do you monitor and control Azure AI agent execution costs?

Answer: Scope Cost Analysis to the Azure AI Foundry project or resource group, group spending by meter to see per-model token consumption, and automate cleanup of disposable agents, threads, and vector stores after each run. Combine budget alerts with workload-level quotas so planners throttle high-volume tasks before they exceed cost limits.

Detailed Explanation:

• Cost Analysis: In the Azure portal, open Cost Management → Cost analysis, scope it to the resource group that hosts the Azure AI Foundry project, then group by Meter to identify model families with the highest input/output token spend.
• Budgets & Alerts: Configure monthly budgets with alerts that trigger when 50%, 75%, and 90% thresholds are hit so teams can pause expensive scenarios or downgrade models.
• Usage Quotas: For long-running planners, enforce concurrency caps and request quotas in your orchestration layer to prevent runaway token usage.
• Operational Hygiene: After every workflow, delete threads, agents, uploaded files, and temporary vector stores using the SDK helpers to avoid accumulating billable artifacts.
• Reporting: Export cost data to a Log Analytics workspace or storage account for historical trending and cross-team chargeback.

Q9.2: What resiliency strategies keep agents responsive under variable load?

Answer: Use autoscaling on the surrounding compute (Logic Apps, Azure Functions, or managed endpoints), layer retries with exponential backoff for transient Azure AI errors, and configure fallback models or planners so critical tasks continue when a preferred deployment hits throttling.

Detailed Explanation:

• Autoscale Rules: Leverage Azure Monitor autoscale on managed online endpoints or serverless backends, scaling on CPU, latency, or custom metrics that reflect agent queue depth; apply schedule-based rules for known peaks.
• Retry Policies: Wrap client.runs.create and tool calls with retry handlers that respect HTTP 429/5xx semantics, log correlation IDs, and emit telemetry when retries occur.
• Fallback Paths: Register secondary model deployments (e.g., GPT-4o → GPT-4o-mini) and guard them with policy so the planner can downgrade gracefully while notifying downstream systems.
• Planner Telemetry: Feed Semantic Kernel planner metrics (success/failure counts, performance buckets) into Application Insights to spot deteriorating tool performance before incidents.
• Disaster Recovery: Store prompt templates, memory stores, and tool metadata in infrastructure-as-code so you can redeploy quickly in an alternate region if necessary.

Q9.3: How do you prevent resource leakage in multi-agent workflows?

Answer: Automate teardown of every temporary asset—threads, agents, files, vector stores, and connected agent handles—once the workflow output is persisted. Track cleanup success as a reliability metric and escalate failures that could accumulate hidden costs or stale credentials.

Detailed Explanation:

• Lifecycle Hooks: Encapsulate agent orchestration in try/finally blocks or workflow cleanup steps that call deleteAgent, deleteThread, and deleteVectorStore SDK methods.
• Idempotent Deletes: Ensure cleanup operations are idempotent so reruns of failed jobs do not error if the asset is already gone.
• Audit Logging: Record cleanup events with timestamps and resource IDs for compliance and to prove adherence to data-retention policies.
• Security: Removing temporary agents limits the attack surface and prevents stale authorization tokens from persisting longer than necessary.
• Automation: Integrate cleanup checks into CI/CD pipelines and scheduled governance jobs that report orphaned assets across subscriptions.