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Multi-Agent Systems: AI That Works Together

Stop throwing more compute at complex problems. Start building intelligent teams

Traditional AI hits a wall when business challenges involve multiple specialized tasks, conflicting data sources, or decisions that need diverse expertise. Multi-Agent Systems (MAS) solve this by coordinating specialized AI agents—each handling what it does best—to tackle problems no single model can handle efficiently. 

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At Reach, we help businesses leverage the power of Microsoft Dynamics 365 products combined with AI-driven intelligence to streamline operations, enhance decision-making, and drive business growth. Our expertise ensures you maximize Dynamics 365’s capabilities with smart automation, predictive analytics, and AI-powered efficiency.

Three Ways to Build MAS

Hierarchical (Manager-Worker) – One orchestrator delegates to specialized agents. Use when you need centralized control and clear task delegation.

Peer-to-Peer (Collaborative) – Agents share state through Dataverse, working autonomously toward shared goals. Use when agents need to collaborate without a central controller.

Event-Driven (Pub-Sub) – Agents react to events independently. Use when you need loose coupling and scalable async processing.

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Choose Your Tech Stack

Copilot

Best for business users who need speed 

  • Learning curve: Gentle visual interface
  • Time to build: Days to weeks
  • Flexibility: Constrained by platform
  • Ideal use cases: Conversational interfaces, M365/Dataverse integrations, business logic orchestration
  • Cost model: Subscription + usage
  • Maintenance: Business users can manage

Azure AI Foundry

Best for developers who need control 

  • Learning curve: Steep, code required
  • Time to build: Weeks to months
  • Flexibility: Maximum customization
  • Ideal use cases: Heavy computation, custom ML models, complex decision engines
  • Cost model: Consumption-based
  • Maintenance: Dev team needed

The Hybrid Approach (Recommended)

Use Copilot Studio for user-facing conversational layers and quick integrations. Use Azure AI Foundry for heavy computation agents and custom ML models. Connect them via REST APIs, Power Automate, and Azure Functions.

Result: Speed to market + technical power where you need it.

Who This Is For

  • Business analysts mapping processes that currently have too many handoffs or stuck decisions
  • Developers building AI solutions that need to integrate multiple data sources or specialized models
  • Solution architects designing systems where a single AI model creates bottlenecks or inaccuracies
  • Operations teams dealing with manual coordination between disconnected systems

Start This Week

Week 1: Map your process—identify where decisions get stuck or require multiple handoffs
Week 2: Build one agent focused on a single task
Week 3: Add collaboration between agents using shared state or orchestration
Week 4: Measure time saved, errors reduced, and cost per transaction

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Find out more with FAQ

A multi-agent system is a collection of autonomous AI agents that work together to solve complex problems. Instead of one large system trying to do everything, you have multiple specialized agents—each excellent at specific tasks—collaborating to achieve a common goal. 

Think of it like a company: you don’t have one person doing sales, engineering, finance, and operations. You have specialists in each area who communicate and coordinate. Multi-agent systems bring this organizational wisdom to AI. 

Example: In our truck brokering system, we have six specialized agents handling intake, load analysis, matching, geographic optimization, scoring, and route analysis. Each focuses on what it does best while working toward the goal of optimal truck-load assignments. 

Orchestration is the coordination mechanism that manages how multiple agents work together. It defines: 

  • Who does what: Which agent handles which tasks 
  • When they act: Sequencing and timing of agent actions 
  • How they communicate: Data flow and message passing between agents 
  • What happens when things go wrong: Error handling and recovery 

Without orchestration, you have a bunch of capable agents with no coordination—like musicians warming up individually instead of playing a symphony together. 

Analogy: Think of orchestration as conducting an orchestra. The conductor (orchestrator) ensures all musicians (agents) play their parts at the right time, in harmony, to create beautiful music (solve the problem). 

There are four primary orchestration patterns: 

Centralized Orchestration (The Conductor Model): 

  • One orchestrator coordinates all agents 
  • Sequential workflow with clear dependencies 
  • Easy to understand and debug 
  • Can become a bottleneck at scale 
  • Best for: Workflows with clear stages and dependencies 

Decentralized Orchestration (The Team Model): 

  • Agents coordinate peer-to-peer without central authority 
  • High scalability and fault tolerance 
  • More complex to design and debug 
  • Best for: Systems requiring autonomy and resilience 

Hierarchical Orchestration (The Organization Model): 

  • Multiple levels of coordination (orchestrators managing orchestrators) 
  • Natural for large-scale systems with organizational boundaries 
  • Balances control and scalability 
  • Best for: Complex systems with natural hierarchies 

Hybrid Patterns (The Best of All Worlds): 

  • Combines elements of multiple patterns 
  • Centralized for strategic coordination, decentralized for real-time data 
  • Most flexible but most complex 
  • Best for: Real-world production systems with diverse requirements 

Agents communicate through several mechanisms, depending on the orchestration pattern: 

Message Passing: 

  • Agents send structured messages (typically JSON) to each other 
  • Can be synchronous (wait for response) or asynchronous (fire and forget) 
  • Example: Load Analysis agent sends shipment data to Matching agent 

Shared State/Memory: 

  • Agents read from and write to a common data store 
  • Good for sharing context without direct communication 
  • Example: All agents access a shared workflow state in a database 

API Calls: 

  • Agents expose APIs that other agents can call 
  • Clear request-response pattern 
  • Example: Geographic Optimization agent provides a route calculation API 

Event-Driven: 

  • Agents publish events; other agents subscribe to relevant events 
  • Loose coupling and high scalability 
  • Example: Route Analysis agent publishes “truck arrived” events that multiple agents listen to 

The critical requirement: Whatever mechanism you use, you need clear data contracts—explicit agreements about message formats, semantic meanings, and expected behaviors. 

Use this decision tree:

Choose Copilot Studio if:

  • You need a POC in days, not weeks
  • Your team is primarily business users/power users, not developers
  • You’re deeply integrated with Microsoft 365/Dynamics/Power Platform
  • You need built-in governance and compliance
  • Your workflow is relatively straightforward
  • You want visual, low-code development

Choose Azure AI Foundry if:

  • You need complex custom logic and algorithms
  • You have professional developers available
  • You’re implementing advanced orchestration patterns
  • You need to integrate with non-Microsoft systems
  • Performance optimization is critical
  • You want full control and flexibility
  • You have mature DevOps/CI/CD practices

The hybrid option: Many teams prototype in Copilot Studio for speed, then rebuild critical components in Azure AI Foundry for production. Or use Copilot Studio for user interfaces while Azure AI Foundry handles complex backend orchestration.

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