Core Concepts
1. What is a multi-agent system (MAS)?
A multi agent ai 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.
2. What is an AI agent?
An AI agent is an autonomous software entity that can:
- Perceive its environment (receive inputs and context)
- Reason about what to do (make decisions using AI models)
- Act on those decisions (execute tasks, call APIs, manipulate data)
- Learn from outcomes (improve over time).
Unlike traditional software that follows rigid if-then rules, AI agents use language models to understand context, make intelligent decisions, and adapt to new situations.
Key difference from regular automation: An agent can handle unexpected situations and make judgments, not just follow predefined scripts.
3. What’s the difference between an agent and a copilot?
Copilot:
- Assistant-oriented: Helps humans complete tasks
- Human-in-the-loop: Requires human direction and approval
- Reactive: Responds to user requests
- Example: GitHub Copilot suggests code, but you decide whether to accept it.
Agent:
- Task-oriented: Completes tasks autonomously
- Autonomous execution: Makes decisions and takes actions independently
- Proactive: Can initiate actions based on conditions
- Example: A load analysis agent automatically evaluates shipments and makes recommendations without waiting for human input.
In practice: The line blurs. Some copilots have agent-like capabilities, and some agents work alongside humans. The key distinction is the level of autonomy and whether the system is augmenting human work or executing independently.
4. What is orchestration in multi-agent systems?
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).
5. What are the main orchestration patterns?
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.
Technical Components
6. What is MCP (Model Context Protocol) and how does it work?
MCP (Model Context Protocol) is an open standard that defines how AI applications connect to external data sources and tools. Think of it as a universal adapter that lets AI agents access resources in a standardized way.
How it works:
- MCP Server: Exposes resources (data, tools, prompts) through a standard interface
- MCP Client: Connects AI agents to MCP servers
- Standard Protocol: Both sides speak the same language, ensuring compatibility.
Why it matters: Instead of building custom integrations for every data source or tool, you build one MCP server and any MCP-compatible agent can use it. This dramatically reduces integration complexity.
Example: We used MCP to connect our agents to Dataverse, solving connectivity issues that plagued earlier approaches. The MCP server handled authentication, data access, and error handling, while our agents simply made standardized requests.
Analogy: MCP is like USB for AI agents. Just as USB lets any device connect to any computer with a standard cable, MCP lets any agent connect to any resource through a standard protocol.
7. How do agents communicate with each other?
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.
8. What is a system prompt?
A system prompt is the foundational instruction set that defines an AI agent’s behavior, personality, capabilities, and constraints. It’s the “DNA” of your agent—the instructions that shape how it thinks and acts.
What it includes:
- Role definition: “You are a load analysis specialist…”
- Capabilities: What the agent can and cannot do
- Behavioral guidelines: How the agent should reason and respond
- Context and constraints: Business rules, safety guidelines, formatting requirements
- Examples: Sample inputs and expected outputs.
Why it matters: The system prompt is the single most important factor determining agent behavior. A well-crafted system prompt produces reliable, predictable agents. A poor prompt leads to inconsistent, unreliable behavior.
Example from our system:
You are a Load Analysis Agent specializing in commodity matching and opportunity detection for truck brokering. Your role is to:
- 1. Analyze shipment characteristics (weight, volume, commodity type)
- 2. Identify optimal opportunities based on profitability and feasibility
- 3. Provide structured recommendations to the Matching Agent.
Ready to implement multi-agent systems in your business? Explore our multi-agent AI solution.
