AI AgentIT Operations Agent
IT Operations Agent that automates operations workflows using your data and tools.
The challenge
The service desk drowns in tickets that all look urgent, and the same password, access and restart requests consume the whole first line. Alerts fire in duplicate across tools, major incidents are reconstructed after the fact, and the fix that worked last time was never written down.
The outcome
Azure Monitor and Log Analytics enrich each alert with the service, its owner and recent changes, and a Microsoft Foundry agent sets priority from real impact and applies a known-error runbook through Azure Automation. Major and novel incidents reach an engineer with the timeline already assembled.
At a glance
- Type
- ai agents
- Category
- technical
01 — Architecture
End-to-end architecture
Alerts and tickets are deduplicated and enriched with the service, its owner, recent changes and the users affected. The Foundry agent sets priority from real impact and applies a known-error runbook where one exists — major and novel incidents go to an engineer with the timeline already assembled.
- Users: User / Service desk
- Channels: Microsoft Teams, Communication Services
- Signals: Azure Monitor, Log Analytics
- AI & agent: Microsoft Foundry Agent, Azure OpenAI models, Azure AI Search, Azure AI Content Safety
- Data & records: Azure SQL, Azure Cosmos DB, Access governance
- Action & insight: Automation runbooks, Engineer decision, Power BI / Fabric
02 — Workflow
Process & decision workflow
How an alert becomes a restored service — enrich, triage, resolve and verify, then branch. A known error with a low-risk fix is remediated and confirmed automatically, while a major or novel incident goes to an engineer with the timeline, the evidence and the options.
- Detect: Alert or ticket raised and deduplicated
- Enrich: Service, owner, change history and impact attached
- Triage: Priority set from impact, urgency and blast radius
- Resolve: Known-error runbook applied or steps proposed
- Verify: Health rechecked and the user confirmed
- Learn: Cause, fix and prevention written to knowledge
- Path 1 · known error, low risk — Auto-remediate: Runbook executed and health verified
- Path 2 · major or novel incident — Engineer takes control: Timeline, evidence and options presented
03 — Components
Key Microsoft components
Operations work is judged on restoration time and evidence — telemetry, triage, remediation and knowledge all stay on the Microsoft stack.
Microsoft TeamsSelf-service channel and major-incident collaboration.
Azure Communication ServicesStatus pages, user updates and escalation notifications.
Azure MonitorMetrics, alert rules, health and service-level tracking.
Azure Log AnalyticsLog queries and correlation across the estate.
Azure Application InsightsApplication traces, dependencies and user impact.
Microsoft Foundry Agent ServiceTriage, remediation and escalation orchestration.
Azure OpenAI modelsIncident summaries, resolution steps and comms drafting.
Azure AI SearchRetrieval over runbooks, known errors and prior incidents.
Azure AI Content SafetyGuardrails on generated commands and privileged actions.
Azure AutomationPre-approved remediation runbooks with a full audit trail.
Microsoft Entra permissionsEntitlement checks before any access or action.
Azure SQLTickets, configuration items, causes and resolutions.
Azure Cosmos DBLive incident state, timelines and correlation data.
Power BI / Microsoft FabricService level, backlog and recurring-issue dashboards.
04 — AI
What the agent consumes
The capabilities the agent applies to every incident, and the line it does not cross.
AI capabilities embedded in the agent
- Alert deduplication
- Incident correlation
- Impact and urgency scoring
- Retrieval-augmented generation
- Log and trace analysis
- Root-cause suggestion
- Automated remediation
- Entitlement checking
- Change-risk assessment
- Major-incident summarisation
- Service-level analytics
- Escalation routing
AI responsibility boundaries
The agent runs only pre-approved runbooks against known errors with an assessed blast radius. Major incidents, novel failures, privileged access and anything customer-facing at scale go to an engineer. Every automated action logs the trigger, the command, the approval basis and the verification result.
05 — Personalization
Personalization & evolving process
The same methodology applies to every agent in the catalog. Tune the estate, the incident template, the runbooks and the value model — the page structure stays identical.
Service & estate profile
Define the services, configuration items, support hours, service levels and priority matrix in scope. The personas are the end user, the service desk analyst, the resolver group and the service owner.
Incident template
One consistent flow for every operations agent: detect, enrich, triage, resolve or escalate, verify, communicate, close and write the learning to knowledge.
Runbooks & risk rules
Declare the approved runbooks, the blast radius of each, the priority matrix and the major-incident trigger. An action outside the approved set is proposed to a person, never executed.
Value model
Capture baseline metrics first, then map the expected benefits: first-line resolution, time to restore, ticket volume, alert noise, repeat incidents and user satisfaction.
06 — Impact
Key outcomes & business impact
Starting targets for the value case — validate each one against the customer baseline during discovery.
- First-line resolution55%+Routine incidents resolved without a resolver group.
- Time to restore−45%Triage and enrichment happen before a human picks it up.
- Alert noise−60%Correlated, deduplicated alerts instead of a wall of pages.
- Action evidence100%Every automated action logs its basis and verification.
Illustrative improvement index
Manual baseline = 100. Illustrative targets, not a commitment — confirm against the customer baseline.
- Time to restore: manual baseline 100, AI-assisted target 55
- Alert noise: manual baseline 100, AI-assisted target 40
- Manual toil: manual baseline 100, AI-assisted target 45
Related & recommended
Derived automatically from our solution knowledge graph.
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Technologies
What powers this solution.
TechnologyMicrosoft Copilot Studio
Low-code platform to build custom copilots and agents.
TechnologyAzure AI
Managed AI services for vision, speech, language and document.
TechnologyAzure AI Foundry
Platform to design, evaluate and operate AI apps and agents.
TechnologyAzure Functions
Serverless compute for event-driven workloads.
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