AI & Cloud Infrastructure
Running AI Agents in Production with Azure App Platform - Microsoft Ignite 2025
By Technspire Team
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November 28, 2025
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6808 views
Agent Inventory and Requirements (1-2 weeks)
- • Catalog existing agents (experimental, staging, production)
- • Document tool dependencies (what APIs/data does each agent need?)
- • Define SLAs (uptime, response time, error rate targets)
- • Assess security requirements (data sensitivity, compliance rules)
- • Estimate resource needs (expected traffic, autoscaling requirements)
2
Infrastructure Setup (2-3 weeks)
- • Deploy Azure App Service plans (choose tier based on SLA requirements)
- • Configure Azure AI Foundry for agent lifecycle management
- • Set up Azure API Management for MCP tool gateway
- • Enable Application Insights for observability
- • Configure Azure Entra for agent identities and RBAC
- • Implement network security (VNets, private endpoints, firewalls)
3
Tool Integration via MCP (3-4 weeks)
- • Build or adopt MCP servers for required tools
- • Register tools in Azure API Center
- • Configure authentication flows (OAuth, managed identities)
- • Set rate limits and quotas per agent/tool combination
- • Test tool invocations from staging agents
- • Document tool capabilities for agent developers
4
Pilot Agent Deployment (4-6 weeks)
- • Select 1-2 high-value agents for initial production deployment
- • Deploy to staging environment, run load tests
- • Validate observability (can you debug agent decisions?)
- • Test failure scenarios (what happens if tools are down?)
- • Deploy to production with canary rollout (5% traffic → 100%)
- • Monitor for 2 weeks, gather feedback
5
Scale to Full Agent Fleet (8-12 weeks)
- • Migrate remaining agents to Azure App Service
- • Implement CI/CD pipelines for agent deployments (GitHub Actions, Azure DevOps)
- • Configure autoscaling rules based on observed traffic patterns
- • Set up alerting for SLA violations (uptime, error rates)
- • Train teams on agent operations (deployment, monitoring, troubleshooting)
- • Establish governance review process (quarterly policy audits)
6
Continuous Improvement (Ongoing)
- • Analyze agent performance data weekly (identify slow tools, high-error agents)
- • Run A/B tests on agent improvements (new prompts, different models)
- • Optimize costs (switch to smaller models where quality is sufficient)
- • Expand tool catalog (add new capabilities based on agent needs)
- • Review compliance (ensure audit logs meet regulatory requirements)
- • Measure ROI (cost savings, efficiency gains, revenue impact)