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Help Desk Chatbot Agent iconAI Agent

Help Desk Chatbot Agent

Help Desk Chatbot Agent that automates it & engineering workflows using your data and tools.

The challenge

The service desk answers the same password, access and how-do-I questions all day, and every one arrives as a ticket that a person has to read, categorise and assign. Recurring issues are solved from scratch each time, tickets land on the wrong tier, and users chase updates because nobody tells them what is happening.

The outcome

A Microsoft Foundry agent resolves recurring issues directly from the knowledge base in Azure AI Search, and Azure Logic Apps opens, categorises and routes anything it cannot close to the right support tier. The same agent answers on the portal, in Teams and on mobile, and Azure Automation runs the safe fixes.

At a glance

Type
ai agents
Category
specialized

Next step

Move from solution to engagement.

Build This Solution

01 — Architecture

End-to-end architecture

A request arrives on whichever channel the user prefers. Microsoft Entra confirms who they are and what they are entitled to before anything runs, the Foundry agent looks for a known fix, and Azure Automation executes only pre-approved actions. Everything else becomes a correctly categorised ticket.

USEREmployee / Userportal · Teams · appCHANNELSMicrosoft Teamsin-flow help deskAzure Static WebAppsself-service portalAPPLICATIONAzure APIManagementservice desk APIsAzure Logic Appsticket & routingAI & AGENTMicrosoft FoundryAgentanswer · fix · routeAzure OpenAImodelsreplies & summariesAzure AI Searchknowledge & ticketsAzure AI ContentSafetyaction guardrailsDATA & ACCESSAzure SQLtickets & assetsAzure Cosmos DBconversation stateAccess governanceentitlement checksACTION & INSIGHTAutomationrunbookssafe self-healingSupport tier twocomplex ticketsPower BI / Fabricdeflection & SLADEVOPS & DELIVERYGitHubsource control & CIDockercontainer buildContainer Registryversioned imagesAgent releasedeploy with rollback
Figure 1 — End-to-end reference architecture for a help desk chatbot agent on the Microsoft stack.
  • User: Employee / User
  • Channels: Microsoft Teams, Azure Static Web Apps
  • Application: Azure API Management, Azure Logic Apps
  • AI & agent: Microsoft Foundry Agent, Azure OpenAI models, Azure AI Search, Azure AI Content Safety
  • Data & access: Azure SQL, Azure Cosmos DB, Access governance
  • Action & insight: Automation runbooks, Support tier two, Power BI / Fabric

02 — Workflow

Process & decision workflow

How a request is resolved or routed — verify, search, resolve and ticket, then branch. Known issues with a safe, pre-approved action are fixed inside the conversation, while anything new or privileged is raised to the right tier with the whole exchange attached.

1AskUser asks on the portal, in Teams or onmobile2VerifyIdentity and entitlement confirmedbefore any action3SearchKnowledge base and past tickets checkedfor a known fix4ResolveAnswer given or an approved runbookexecuted5TicketAnything unresolved raised, categorisedand assigned6UpdateUser told what happened and what comesnextKnown issue& safe to fix?Path 1 · known and safeResolve in the chatFixed and verified without raising aticketPath 2 · new or privilegedRaise to the right tierTicket assigned with the fullconversationTicket recordedCategory, action and outcomestoredKnowledge updatedNew fix published for the nextuserCloseissue resolved
Figure 2 — Ask → verify → search → resolve → ticket → safety branch → fix in chat or route to a tier.
  1. Ask: User asks on the portal, in Teams or on mobile
  2. Verify: Identity and entitlement confirmed before any action
  3. Search: Knowledge base and past tickets checked for a known fix
  4. Resolve: Answer given or an approved runbook executed
  5. Ticket: Anything unresolved raised, categorised and assigned
  6. Update: User told what happened and what comes next
  7. Path 1 · known and safe — Resolve in the chat: Fixed and verified without raising a ticket
  8. Path 2 · new or privileged — Raise to the right tier: Ticket assigned with the full conversation

03 — Components

Key Microsoft components

Deflection only counts if the action is safe and the routing is right — identity, knowledge, automation and ticketing all stay on the Microsoft stack.

  • Microsoft Teams iconMicrosoft TeamsIn-flow help desk where people already work.
  • Azure Static Web Apps iconAzure Static Web AppsSelf-service portal and embedded chat widget.
  • Azure API Management iconAzure API ManagementSecure gateway for service desk, directory and asset APIs.
  • Microsoft Foundry Agent Service iconMicrosoft Foundry Agent ServiceAnswering, safe action and routing orchestration.
  • Azure OpenAI models iconAzure OpenAI modelsGrounded replies, multilingual answers and ticket summaries.
  • Azure AI Search iconAzure AI SearchRetrieval over the knowledge base and resolved tickets.
  • Azure AI Content Safety iconAzure AI Content SafetyGuardrails on generated instructions and privileged actions.
  • Microsoft Entra permissions iconMicrosoft Entra permissionsIdentity and entitlement checks before any action runs.
  • Azure Automation iconAzure AutomationPre-approved self-service runbooks with a full audit trail.
  • Azure Logic Apps iconAzure Logic AppsTicket creation, categorisation, tier routing and updates.
  • Azure Communication Services iconAzure Communication ServicesStatus updates and resolution notifications.
  • Azure SQL iconAzure SQLTickets, categories, assets and resolution records.
  • Azure Cosmos DB iconAzure Cosmos DBConversation state across portal, Teams and mobile.
  • Power BI / Microsoft Fabric iconPower BI / Microsoft FabricDeflection, service level and recurring-issue dashboards.

04 — AI

What the agent consumes

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

AI capabilities embedded in the agent

  • Intent detection
  • Identity and entitlement verification
  • Retrieval-augmented generation
  • Answer citation
  • Ticket classification
  • Priority and tier routing
  • Approved self-healing actions
  • Duplicate detection
  • Multilingual answering
  • Status notification
  • Deflection analytics
  • Escalation routing

AI responsibility boundaries

The agent runs only pre-approved actions against a verified identity, and never grants privileged access, changes an entitlement it was not asked to change, or closes a ticket the user has not confirmed. Anything new, privileged or affecting more than one person is raised to a human tier with the full conversation attached.

05 — Personalization

Personalization & evolving process

The same methodology applies to every agent in the catalog. Tune the services, the help desk template, the action set and the value model — the page structure stays identical.

Service & channel profile

Define the services in scope, the supported channels, languages, hours of cover, tier definitions and the service desk platform in use. The personas are the employee, the first-line analyst, the tier-two engineer and the service owner.

06 — Impact

Key outcomes & business impact

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

  • Ticket deflection45%+Recurring requests resolved inside the conversation.
  • First responseInstantNo queue before a user gets an answer or an action.
  • Routing accuracyImprovedTickets reach the right tier first time, with context.
  • Action evidence100%Every automated fix logs its identity check and result.

Illustrative improvement index

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

10055First-line ticket volume10010Time to first response10040Misrouted ticketsManual baselineAI-assisted target
  • First-line ticket volume: manual baseline 100, AI-assisted target 55
  • Time to first response: manual baseline 100, AI-assisted target 10
  • Misrouted tickets: manual baseline 100, AI-assisted target 40

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