Azure reference architectureResearch and benchmarking
A Azure reference architecture for research and benchmarking in Professional Services. Triggered by new client intake or status checkpoint, Padiso orchestrates 3 specialist agents on Azure AI Foundry, reaching professional services automation (psa) tool and your tools through the Model Context Protocol, with budgets, approvals and an audit trail throughout.
AzureRuntime: Azure AI FoundryTrigger
New client intake or status checkpoint
Professional Services
Orchestration
Padiso Orchestrator
goals · tasks · heartbeat
Governance
budgets · approvals · audit
Runtime & agents
Agent runtime
Azure AI Foundry
Delivery agent
agent
Research agent
agent
Client ops agent
agent
Integrations · MCP
MCP gateway
Your systems
Professional Services Automation (PSA) tool
CRM
Document repository
How the data flows
Trigger: New client intake or status checkpoint
- 1
Client-ops agent collects intake details and sets up the project
- 2
It compiles a status update from project data
- 3
Schedules checkpoints and chases outstanding inputs
- 4
Anything off-track is flagged to the engagement lead
Agent team
- Delivery agent
- Research agent
- Client ops agent
Integrations
Reached through the Model Context Protocol, scoped and governed.
Runtime & data
Azure AI Foundry
Professional Services Automation (PSA) tool
CRM
Document repository
Security & operational considerations
- Scoped access per client
- Approvals on scope or timeline changes
- Audit trail on status reporting
Azure deployment
Deployed in your Azure tenant on AI Foundry, with Entra ID scoping, Private Endpoints into your VNet, and Azure Monitor for audit and observability.
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