Multi-Agent Framework

Multi-Agent Architecture

Coordinate specialist agents without losing control

Multi-agent systems become valuable when complex work is divided across specialist agents and governed through a coordinator, policy boundary, human review and measurable outcomes.

CoordinateSpecializeReviewMeasure

Agentic AI ArchitectureAgent FactoryCustomer Pattern

Agent TeamSpecialists with control

01CoordinatorDecomposes work, assigns agents and consolidates response.02KnowledgeRetrieves policies, history, reference assets and enterprise context.03TaskExecutes workflow, ticket, CRM, proposal or system actions.04ReviewChecks quality, completeness, compliance and business fit.

Control pointMulti-agent design should reduce complexity for users, not hide complexity from governance owners.

Executive Summary

Enterprise AI is rapidly evolving from single-agent experiences toward coordinated multi-agent systems.

A single agent can perform individual tasks, but complex enterprise processes typically require multiple specialized agents working together.

Microsoft's long-term vision for Agentic AI includes coordinated agents that collaborate across Microsoft 365, business applications, enterprise knowledge repositories and external systems.

Multi-Agent Framework provides the architectural pattern for building scalable, governed and reusable enterprise AI ecosystems.

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Why Multi-Agent?

A single agent often becomes overloaded.

Typical enterprise requests require:

• Research
• Validation
• Compliance review
• Business processing
• Reporting
• Approval

Attempting to perform all of these tasks with one agent creates complexity, risk and maintenance challenges.

A multi-agent architecture distributes responsibilities across specialized agents.

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Single Agent vs Multi-Agent

Capability	Single Agent	Multi-Agent
Simple FAQ	Excellent	Excellent
Knowledge Search	Good	Excellent
Workflow Execution	Good	Excellent
Enterprise Scale	Limited	Strong
Governance	Moderate	Strong
Reusability	Limited	High
Maintainability	Difficult	Easier
Complex Decision Support	Limited	Strong

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Core Architecture

Core ArchitectureUser request to governed response

01User RequestBusiness question, workflow request or executive task.

02CoordinatorPlans work, assigns specialists and owns final orchestration.

03SpecialistsKnowledge, task, review, compliance and reporting agents.

04Human ReviewApproves sensitive actions, exceptions and external-facing outputs.

05Control PlaneIdentity, DLP, audit, telemetry, cost and evaluation signals.

06Final ResponseConsolidated answer, action result, report or decision support.

Responsibility ModelEach specialist should have a narrow purpose and an explicit boundary.The coordinator should not become an uncontrolled super-agent. It should route work, enforce policy and explain how final outputs were assembled.

CoordinateTask decompositionBreak complex requests into specialist work packets.GroundEnterprise knowledgeRetrieve approved information from Microsoft 365 and business systems.ActWorkflow executionTrigger tools, APIs, approvals and business actions within scope.ReviewQuality and consistencyCheck completeness, format, accuracy and business alignment.ControlCompliance reviewEvaluate policy, data handling and risk before delivery.ReportExecutive outputCreate summaries, dashboards and stakeholder-ready narratives.

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Coordinator Agent

The Coordinator Agent is the brain of the system.

Responsibilities:

• Understand user intent
• Break down tasks
• Assign work
• Consolidate outputs
• Resolve conflicts
• Produce final response

Without a coordinator, agent-to-agent communication becomes difficult to manage.

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Knowledge Agent

Purpose:

Retrieve and summarize enterprise knowledge.

Data Sources

• SharePoint Online
• OneDrive
• Microsoft Graph
• Teams Knowledge
• Microsoft Fabric
• Dataverse
• External Knowledge Bases

Typical Tasks

• Policy lookup
• Architecture retrieval
• SOP retrieval
• Proposal reference
• Project history lookup

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Task Agent

Purpose:

Execute business actions.

Typical Actions

• Create ticket
• Update CRM
• Generate proposal
• Trigger approval
• Create project
• Schedule meeting
• Generate report

Technology

• Power Automate
• Copilot Studio Tools
• REST APIs
• Logic Apps
• MCP Servers

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Review Agent

Purpose:

Validate quality before delivery.

Review Areas

• Completeness
• Accuracy
• Formatting
• Consistency
• Business alignment

Example

Proposal Draft Agent creates proposal.

Review Agent verifies:

• Executive Summary exists
• Scope defined
• Assumptions included
• Risks identified
• Timeline included

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Compliance Agent

Purpose:

Reduce organizational risk.

Responsibilities

• Regulatory review
• Security validation
• DLP validation
• Privacy review
• Governance enforcement

Example

Before sharing a document:

• Check sensitivity label
• Check external sharing
• Verify retention policy
• Verify approval process

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Reporting Agent

Purpose:

Produce executive outputs.

Deliverables

• Dashboard
• Executive Summary
• KPI Report
• Risk Report
• Adoption Report

Example

Generate:

• Weekly AI Adoption Report
• Monthly Security Dashboard
• Executive Steering Committee Pack

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Enterprise Agent Taxonomy

Enterprise Agent TaxonomyGroup agents by business capability and control needs

01StrategicExecutive briefing, planning, portfolio and decision-support agents.

02OperationalService desk, HR, finance and repeatable work execution agents.

03KnowledgePolicy, architecture, standards, proposal and reusable asset agents.

04ComplianceSecurity, privacy, audit, exception and control-review agents.

05DeliveryProject, migration, proposal, handover and reporting agents.

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Microsoft Agent Platform Mapping

Agent Type	Microsoft Technology
Personal Agent	Microsoft Scout
Team Agent	Agent Builder
Department Agent	Copilot Studio
Enterprise Agent	Microsoft Foundry
Workflow Agent	Power Automate
Autonomous Agent	Copilot Studio Autonomous Agents
Multi-Agent System	Foundry + Copilot Studio

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Microsoft Scout Integration

Microsoft Scout introduces the concept of an always-on personal agent.

Scout can:

• Track work
• Monitor priorities
• Prepare meetings
• Surface risks
• Coordinate actions

In future architectures:

Microsoft Scout IntegrationPersonal orchestration layer for specialist agents

01ScoutTracks personal work, priorities, meetings, risk and next actions.

02CoordinatorRoutes intent to the right specialist agent and keeps task context.

03SpecialistsKnowledge, task, compliance and reporting agents execute scoped work.

04ReviewHuman approval and governance controls apply before sensitive actions.

05OutcomeMeeting prep, risk summary, draft output or work package is returned.

Scout becomes the user's personal orchestration layer.

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Agent Communication Patterns

Pattern 1

Sequential

Pattern 1Sequential handoff

AResearchCollect facts, source material and business context.

BDraftCreate the first output from the research package.

CReviewCheck quality, policy, risk and missing assumptions.

DDeliverPublish the approved output or hand it to the next workflow.

Example:

Research → Draft → Review → Deliver

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Pattern 2

Parallel

Pattern 2Parallel specialist review

AShared inputOne work package is distributed to multiple specialist agents.

BPolicyPolicy and governance review.

CSecuritySecurity and risk review.

DArchitectureTechnical fit and integration review.

EConsolidateCombine findings into one decision-ready output.

Example:

Policy Review

Security Review

Architecture Review

Compliance Review

then consolidate.

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Pattern 3

Hierarchical

Pattern 3Hierarchical orchestration

01Master agentOwns goal, decomposition, dependency handling and final synthesis.

02Team agent AExecutes a domain-specific workstream.

03Team agent BHandles a second workstream with its own tools and knowledge.

04Team agent CReviews risk, governance or quality before completion.

05Final outputThe master agent combines workstreams into one governed result.

Used for enterprise orchestration.

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MCP in Multi-Agent Systems

Model Context Protocol enables agents to share tools.

Benefits:

• Reusable integrations
• Standardized tool access
• Reduced API complexity
• Cross-agent consistency

Example:

MCP IntegrationShared tool layer for consistent agent actions

01Agent ARequests a business system action through a standard interface.

02Agent BUses the same tool contract for a different workflow.

03MCPStandardizes tool access, permission checks and integration patterns.

04SystemsSAP, ServiceNow, Salesforce and internal APIs stay behind governed connectors.

05ResultAgents act consistently without duplicating integration logic.

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Agent Memory Architecture

Short-Term Memory

Conversation context

Examples:

• Current discussion
• Session variables
• Temporary state

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Long-Term Memory

Persistent knowledge

Examples:

• Customer history
• Project history
• Prior decisions
• Business preferences

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Organizational Memory

Shared enterprise intelligence

Examples:

• Architecture standards
• Governance models
• Project templates
• Proposal repositories

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Human-in-the-Loop Architecture

Human-in-the-LoopSensitive actions stay reviewable and auditable

01Agent proposalThe agent prepares a recommendation, draft, action or exception request.

02Policy reviewRules, confidence, data sensitivity and business risk are checked.

03Human approvalNamed owner approves, rejects or requests changes.

04ExecuteThe approved action runs with audit trail and rollback path.

Critical actions should remain reviewable.

Examples:

• Financial approval
• Contract generation
• External communication
• Security exceptions

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Multi-Agent Governance

Governance Layers

Layer	Purpose
Identity	Entra ID
Data	Purview
Security	Defender
Compliance	Audit
Operations	Agent365
Analytics	Power BI

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Agent Ownership Model

Every agent must have:

Business Owner

Responsible for:

• Business value
• Requirements
• KPI

Technical Owner

Responsible for:

• Platform
• Security
• Maintenance

Governance Owner

Responsible for:

• Compliance
• Policy
• Audit

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Enterprise Operating Model

Enterprise Operating ModelBusiness idea to continuously improved agent portfolio

01IdeaBusiness unit submits an agent opportunity with expected value.

02AssessReview feasibility, data, permissions, security, cost and owner model.

03DesignDefine agent roles, tools, memory, guardrails and human review points.

04Build and testValidate accuracy, safety, latency, UX and escalation behavior.

05OperateDeploy, monitor, improve, retire or scale the agent portfolio.

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Multi-Agent Use Cases

Proposal Factory

Agents:

• Opportunity Agent
• Architecture Agent
• Pricing Agent
• Review Agent
• Executive Summary Agent

Output:

Complete Proposal Package

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Security Operations Center

Agents:

• Alert Agent
• Investigation Agent
• Compliance Agent
• Reporting Agent

Output:

Incident Report

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AI PMO

Agents:

• Project Agent
• Risk Agent
• Resource Agent
• Reporting Agent

Output:

Project Governance Dashboard

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Microsoft 365 Consulting Factory

Agents:

• Discovery Agent
• Assessment Agent
• Architecture Agent
• Proposal Agent
• Delivery Agent

Output:

Customer Engagement Package

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KPI Framework

KPI	Description
Agent Utilization	Usage
Completion Rate	Success
Escalation Rate	Human involvement
Accuracy	Quality
Cost Reduction	Efficiency
Cycle Time	Speed
Adoption	User acceptance
Satisfaction	Experience

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Maturity Model

Level 1

Single Copilot Usage

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Level 2

Department Agents

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Level 3

Business Process Agents

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Level 4

Multi-Agent Coordination

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Level 5

Enterprise AI Operating System

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Recommended Microsoft Stack

Layer	Technology
Experience	Microsoft 365 Copilot
Personal Agent	Scout
Team Agent	Agent Builder
Business Agent	Copilot Studio
Enterprise Agent	Microsoft Foundry
Automation	Power Automate
Integration	Logic Apps
Security	Defender
Compliance	Purview
Identity	Entra ID
Analytics	Power BI
Governance	Agent365

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Executive Recommendations

1. Start with business outcomes.
2. Avoid building a single mega-agent.
3. Design reusable specialist agents.
4. Implement governance before scale.
5. Establish an Agent Factory model.
6. Apply Purview and Defender controls.
7. Monitor agent quality continuously.
8. Build toward an Enterprise AI Operating System.

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Deliverables

A Multi-Agent engagement should produce:

• Multi-Agent Reference Architecture
• Agent Interaction Model
• Agent Governance Framework
• Agent Ownership Matrix
• Enterprise Agent Catalog
• Agent Factory Model
• Security Baseline
• KPI Framework
• Operating Model
• Roadmap

검색 키워드

• Microsoft 365 Copilot
• Copilot Studio
• AI Agent governance
• Copilot adoption
• Copilot readiness
• Copilot 도입
• AI Agent 운영 모델

Contact / Asset Request

For Copilot readiness workbooks, adoption roadmaps, agent governance templates, prompt libraries or executive AI value materials, use Contact and Asset Request.