Skip to content
Cloud Mechanics
Cloud Engineering Agent iconAI Agent

Cloud Engineering Agent

Cloud Engineering Agent that automates it & engineering workflows using your data and tools.

The challenge

Every environment request becomes a ticket, and the engineer who picks it up writes infrastructure code from scratch or copies the last project. Standards drift between teams, policy failures surface only after deployment, and cost arrives as a surprise on the monthly bill.

The outcome

Azure AI Search matches the request to an approved reference pattern, a Microsoft Foundry agent generates the infrastructure code, and policy, security, quota and Azure Cost Management checks all run before a pull request opens. Standard patterns auto-approve; anything new reaches the platform team with the estimate.

At a glance

Type
ai agents
Category
technical

Next step

Move from solution to engagement.

Build This Solution

01 — Architecture

End-to-end architecture

A workload request enters through intake, is matched to a reference pattern, and turned into infrastructure as code by the Foundry agent. Policy, security, quota and cost checks run before a pull request is raised — the agent never applies directly to production, and a platform engineer merges every change.

ENGINEERCloud engineerrequest & reviewSOURCEGitHubIaC & pull requestsAzure Logic Appsrequest intakePLATFORMAzure APIManagementplatform APIsAzure Kuberneteslanding zonesAI & AGENTMicrosoft FoundryAgentplan · draft · checkAzure OpenAImodelsIaC & runbooksAzure AI Searchstandards & patternsAzure AI ContentSafetyguardrail policyDATA & CONTROLAzure Cosmos DBrequest stateCost Managementspend & forecastAzure Key Vaultsecrets & identitiesOPERATE & INSIGHTPlatform teamreviewapprove & mergeAzure Monitorhealth & driftPower BI / Fabriccost & complianceDEVOPS & DELIVERYGitHubsource control & CIDockercontainer buildContainer Registryversioned imagesPlatform releasedeploy with rollback
Figure 1 — End-to-end reference architecture for a cloud platform engineering agent on the Microsoft stack.
  • Engineer: Cloud engineer
  • Source: GitHub, Azure Logic Apps
  • Platform: Azure API Management, Azure Kubernetes
  • AI & agent: Microsoft Foundry Agent, Azure OpenAI models, Azure AI Search, Azure AI Content Safety
  • Data & control: Azure Cosmos DB, Cost Management, Azure Key Vault
  • Operate & insight: Platform team review, Azure Monitor, Power BI / Fabric

02 — Workflow

Process & decision workflow

How a workload request becomes a provisioned, governed environment — design, draft, validate and review, then branch. A standard pattern inside budget auto-approves, while a new pattern or an over-budget estimate goes to the platform team with the diff, the policy result and the cost.

1RequestWorkload, environment and constraintscaptured2DesignReference architecture matched to therequirement3DraftInfrastructure as code generated to thestandard4ValidatePolicy, security, cost and quota checksapplied5ReviewPull request raised with the rationaleattached6DeployPipeline provisions and registers theworkloadCompliant& in budget?Path 1 · standard patternAuto-approve the changeProvisioned, tagged and registeredPath 2 · new pattern or over budgetPlatform team reviewDiff, policy result and cost estimateshownWorkload registeredHealth, owner and alertsconfiguredCost trackedBudget, tags and forecastappliedCloseenvironment live
Figure 2 — Request → design → draft → validate → review → compliance branch → auto-approve or platform review.
  1. Request: Workload, environment and constraints captured
  2. Design: Reference architecture matched to the requirement
  3. Draft: Infrastructure as code generated to the standard
  4. Validate: Policy, security, cost and quota checks applied
  5. Review: Pull request raised with the rationale attached
  6. Deploy: Pipeline provisions and registers the workload
  7. Path 1 · standard pattern — Auto-approve the change: Provisioned, tagged and registered
  8. Path 2 · new pattern or over budget — Platform team review: Diff, policy result and cost estimate shown

03 — Components

Key Microsoft components

Platform engineering is governed change — generation, validation, approval and observation all stay on the Microsoft stack.

  • GitHub iconGitHubInfrastructure as code, pull requests and CI workflows.
  • Azure Logic Apps iconAzure Logic AppsRequest intake, approval routing and notifications.
  • Azure API Management iconAzure API ManagementSecure gateway for platform and self-service APIs.
  • Azure Kubernetes Service iconAzure Kubernetes ServiceLanding zones and container platform for workloads.
  • Microsoft Foundry Agent Service iconMicrosoft Foundry Agent ServiceDesign, drafting and validation orchestration with tools.
  • Azure OpenAI models iconAzure OpenAI modelsInfrastructure code, runbooks and change rationale.
  • Azure AI Search iconAzure AI SearchRetrieval over architecture standards and approved patterns.
  • Azure AI Content Safety iconAzure AI Content SafetyGuardrails on generated configuration and privileged actions.
  • Azure Automation iconAzure AutomationApproved provisioning and remediation runbooks.
  • Azure Monitor iconAzure MonitorWorkload health, drift detection and alerting.
  • Azure Cost Management iconAzure Cost ManagementBudgets, tagging, forecast and chargeback.
  • Azure Key Vault iconAzure Key VaultSecrets, certificates and managed identity configuration.
  • Azure Cosmos DB iconAzure Cosmos DBRequest state, approvals and change history.
  • Power BI / Microsoft Fabric iconPower BI / Microsoft FabricCost, compliance and platform adoption dashboards.

04 — AI

What the agent consumes

The capabilities the agent applies to every change, and the line it does not cross.

AI capabilities embedded in the agent

  • Requirement extraction
  • Reference-pattern matching
  • Infrastructure code generation
  • Policy-as-code validation
  • Cost estimation
  • Quota and capacity checks
  • Retrieval-augmented generation
  • Drift detection
  • Runbook drafting
  • Change summarisation
  • Approval routing
  • Workflow orchestration

AI responsibility boundaries

The agent drafts and validates change; a platform engineer approves and merges anything new, privileged or over budget. It never applies directly to production, bypasses policy-as-code or provisions outside the landing zone. Every change carries the diff, the policy result, the cost estimate and the approver.

05 — Personalization

Personalization & evolving process

The same methodology applies to every agent in the catalog. Tune the estate, the change template, the guardrails and the value model — the page structure stays identical.

Estate & landing zone profile

Define the subscriptions, landing zones, regions, naming and tagging standards and the approved reference patterns. The personas are the requesting engineer, the platform team, security and finance.

06 — Impact

Key outcomes & business impact

Starting targets for the value case — validate each one against the customer baseline during discovery.

  • Provisioning timeHoursStandard workloads land the same day they are requested.
  • Standard compliance95%+Environments are generated from approved patterns.
  • Review effort−50%Engineers review a validated diff, not a blank template.
  • Cost visibilityPre-mergeThe estimate is on the pull request before approval.

Illustrative improvement index

Manual baseline = 100. Illustrative targets, not a commitment — confirm against the customer baseline.

10025Provisioning time10050Review hours10035Policy exceptionsManual baselineAI-assisted target
  • Provisioning time: manual baseline 100, AI-assisted target 25
  • Review hours: manual baseline 100, AI-assisted target 50
  • Policy exceptions: manual baseline 100, AI-assisted target 35

Related & recommended

Derived automatically from our solution knowledge graph.

Related AI agents

Other agents that pair well with this one.

Marketing Campaign Compliance iconAI Agent

Marketing Campaign Compliance

Agent that checks the consent basis and suppression state behind every recipient, screens creative for unsubstantiated claims and missing disclosures, and hands a release decision to a named marketer

Order Returns Resolution iconAI Agent

Order Returns Resolution

Agent that verifies who owns an order before disclosing anything about it, separates a statutory remedy from a discretionary one, and authorises only a reversible return that a named agent settles

Patient Appointment Scheduling iconAI Agent

Patient Appointment Scheduling

Agent that verifies identity and eligibility, proposes clinically appropriate slots, holds one for a human scheduler to confirm and drafts consented reminders

Property Viewing Coordination iconAI Agent

Property Viewing Coordination

Agent that qualifies a viewing enquiry, checks access constraints and occupier notice, proposes slots on objective availability alone and holds one for a human agent to confirm

Related professional services

How we design, build and secure it.

AI Agent Development iconService

AI Agent Development

AI Agent Development delivered by Cloud Mechanics certified experts.

AI Integration iconService

AI Integration

AI Integration delivered by Cloud Mechanics certified experts.

AI Security iconService

AI Security

AI Security delivered by Cloud Mechanics certified experts.

Data Engineering iconService

Data Engineering

Data Engineering delivered by Cloud Mechanics certified experts.

Related quick wins

Ready-made Azure AI to start fast.

AI Chat iconQuick Win

AI Chat

Grounded AI chat over your data

Content Read iconQuick Win

Content Read

Read printed & handwritten content

Contract Processing iconQuick Win

Contract Processing

Extract clauses & terms

Customer Support Assistant iconQuick Win

Customer Support Assistant

Assistant that drafts & resolves

Technologies

What powers this solution.

Azure AI Foundry iconTechnology

Azure AI Foundry

Platform to design, evaluate and operate AI apps and agents.

Microsoft Copilot Studio iconTechnology

Microsoft Copilot Studio

Low-code platform to build custom copilots and agents.

Azure Functions iconTechnology

Azure Functions

Serverless compute for event-driven workloads.

Azure OpenAI iconTechnology

Azure OpenAI

Enterprise access to GPT models with governance.

Related managed services

Keep it running and optimised.

AI Managed Services iconManaged

AI Managed Services

AI Managed Services from our UAE-based 24/7 Cloud Operations Center.

Cloud Managed Services iconManaged

Cloud Managed Services

Cloud Managed Services from our UAE-based 24/7 Cloud Operations Center.

DevOps Managed Services iconManaged

DevOps Managed Services

DevOps Managed Services from our UAE-based 24/7 Cloud Operations Center.

FinOps / Cloud Cost Management (OpsNow) iconManaged

FinOps / Cloud Cost Management (OpsNow)

FinOps / Cloud Cost Management (OpsNow) from our UAE-based 24/7 Cloud Operations Center.

Ready to move from challenge to solution?

Talk to a Cloud Mechanics expert or build your solution in minutes.

Talk to an ExpertBuild This Solution