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Product Management Agent iconAI Agent

Product Management Agent

Product Management Agent that automates it & engineering workflows using your data and tools.

The challenge

Feedback arrives through support tickets, sales calls, reviews and chat, and only the loudest channel gets read. Themes are assembled by hand before each planning cycle, the same request is logged five times under different words, and shipped features are rarely measured against why they were built.

The outcome

Azure AI Search retrieves across research, tickets and call notes, and a Microsoft Foundry agent clusters them into themes that keep a link to the raw evidence. Azure OpenAI frames the problem before proposing a solution and drafts the requirements; the product manager still decides what gets built.

At a glance

Type
ai agents
Category
business

Next step

Move from solution to engagement.

Build This Solution

01 — Architecture

End-to-end architecture

Feedback, tickets, call notes and usage data are gathered continuously and clustered into themes that keep a link to the raw evidence. The Foundry agent frames the problem before proposing a solution and drafts requirements — but the product manager decides what gets built, and can overrule the data knowingly.

PRODUCTPM / Stakeholderdiscovery & deliverySIGNALSTeams /SharePointresearch & feedbackGitHubissues & roadmapAPPLICATIONAzure APIManagementproduct data APIsAzure Functionsaggregate & scoreAI & AGENTMicrosoft FoundryAgentsynthesise & draftAzure OpenAImodelsspecs & summariesAzure AI Searchresearch & ticketsAzure AI ContentSafetyclaim guardrailsDATA & RECORDSAzure SQLbacklog & outcomesAzure Cosmos DBdraft stateAzure BlobStorageresearch artefactsDECISION & INSIGHTProduct decisionscope & priorityBacklog workflowssync & notifyPower BI / Fabricadoption & valueDEVOPS & DELIVERYGitHubsource control & CIDockercontainer buildContainer Registryversioned imagesAgent releasedeploy with rollback
Figure 1 — End-to-end reference architecture for a product discovery agent on the Microsoft stack.
  • Product: PM / Stakeholder
  • Signals: Teams / SharePoint, GitHub
  • Application: Azure API Management, Azure Functions
  • AI & agent: Microsoft Foundry Agent, Azure OpenAI models, Azure AI Search, Azure AI Content Safety
  • Data & records: Azure SQL, Azure Cosmos DB, Azure Blob Storage
  • Decision & insight: Product decision, Backlog workflows, Power BI / Fabric

02 — Workflow

Process & decision workflow

How raw feedback becomes an evidenced decision — gather, synthesise, frame, draft and prioritise, then branch. Well-evidenced themes land in the backlog with the raw feedback linked, while thin evidence or a strategic call goes to the product manager with the trade-offs laid out.

1GatherFeedback, tickets, calls and usage datacollected2SynthesiseThemes and pain points clustered withevidence3FrameProblem statement written before anysolution4DraftRequirements, acceptance criteria andrisks5PrioritiseValue, effort and confidence scoredconsistently6TrackOutcome measured against the statedhypothesisEvidencesupports it?Path 1 · well-evidenced themeAdd to the backlogItem created with the evidence linkedPath 2 · thin evidence or strategicProduct decisionThemes, evidence and trade-offspresentedBacklog updatedItem, evidence and hypothesisstoredOutcome trackedAdoption measured against thegoalClosedecision recorded
Figure 2 — Gather → synthesise → frame → draft → prioritise → evidence branch → backlog or product decision.
  1. Gather: Feedback, tickets, calls and usage data collected
  2. Synthesise: Themes and pain points clustered with evidence
  3. Frame: Problem statement written before any solution
  4. Draft: Requirements, acceptance criteria and risks
  5. Prioritise: Value, effort and confidence scored consistently
  6. Track: Outcome measured against the stated hypothesis
  7. Path 1 · well-evidenced theme — Add to the backlog: Item created with the evidence linked
  8. Path 2 · thin evidence or strategic — Product decision: Themes, evidence and trade-offs presented

03 — Components

Key Microsoft components

Discovery is only trustworthy when every theme traces back to raw feedback — aggregation, synthesis and tracking all stay on the Microsoft stack.

  • Microsoft Teams / SharePoint iconMicrosoft Teams / SharePointResearch notes, call recordings and stakeholder input.
  • GitHub iconGitHubIssues, roadmap items and delivery signal.
  • Azure API Management iconAzure API ManagementSecure gateway for product analytics and support APIs.
  • Azure Functions iconAzure FunctionsAggregation, deduplication and prioritisation scoring.
  • Microsoft Foundry Agent Service iconMicrosoft Foundry Agent ServiceSynthesis, framing and drafting orchestration.
  • Azure OpenAI models iconAzure OpenAI modelsTheme clustering, problem framing and specification drafts.
  • Azure AI Search iconAzure AI SearchRetrieval across research, tickets and prior decisions.
  • Azure AI Content Safety iconAzure AI Content SafetyGuardrails against unsupported claims and invented quotes.
  • Azure Logic Apps iconAzure Logic AppsBacklog synchronisation, notification and review routing.
  • Azure SQL iconAzure SQLBacklog, hypotheses, decisions and measured outcomes.
  • Azure Cosmos DB iconAzure Cosmos DBDraft state, theme membership and review history.
  • Azure Blob Storage iconAzure Blob StorageResearch artefacts, transcripts and supporting evidence.
  • Azure Container Apps iconAzure Container AppsSynthesis workers that scale with feedback volume.
  • Power BI / Microsoft Fabric iconPower BI / Microsoft FabricAdoption, value realisation and theme dashboards.

04 — AI

What the agent consumes

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

AI capabilities embedded in the agent

  • Feedback aggregation
  • Theme clustering
  • Sentiment analysis
  • Retrieval-augmented generation
  • Problem framing
  • Requirement drafting
  • Acceptance-criteria generation
  • Duplicate detection
  • Value and effort scoring
  • Hypothesis tracking
  • Adoption analytics
  • Workflow orchestration

AI responsibility boundaries

The agent synthesises evidence and drafts; a product manager decides what gets built and what does not. It does not invent customer quotes, weight a theme by volume alone, or present an opinion as research. Every theme links to the raw feedback behind it so a strategic call can overrule the data knowingly.

05 — Personalization

Personalization & evolving process

The same methodology applies to every agent in the catalog. Tune the product, the discovery template, the evidence rules and the value model — the page structure stays identical.

Product & audience profile

Define the products, segments, feedback channels, personas and the strategic goals in scope. The people involved are the product manager, the designer, engineering and the commercial stakeholders.

06 — Impact

Key outcomes & business impact

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

  • Research synthesis−70%Themes are clustered continuously, not before a planning cycle.
  • Feedback coverage100%Every channel is read, not just the loudest one.
  • Time to specificationDaysA drafted, evidenced spec instead of a blank document.
  • Evidence traceabilityPer themeEach theme links to the raw feedback behind it.

Illustrative improvement index

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

10030Synthesis hours10045Time to specification10035Duplicate backlog itemsManual baselineAI-assisted target
  • Synthesis hours: manual baseline 100, AI-assisted target 30
  • Time to specification: manual baseline 100, AI-assisted target 45
  • Duplicate backlog items: manual baseline 100, AI-assisted target 35

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