From RAG to Multi-Agent Systems

You've now seen how a single agent can use tools and retrieve knowledge via RAG. But some problems are too complex, too broad, or too specialized for one agent alone.

What if you could assemble a team of agents — each with its own expertise — that collaborate to solve larger challenges? That's what multi-agent systems are about.

When and Why Multiple Agents

Think of a single AI agent as a skilled generalist. It can handle many tasks, but some problems are too large, too specialized, or too nuanced for one agent working alone. Multi-agent systems split complex work across several specialized agents, each focused on what it does best.

The Team Analogy

Consider how organizations work. You would not ask one person to research a market, write the report, design the graphics, and present to the board. You assemble a team. Multi-agent AI systems follow the same principle.

When to Use Multiple Agents

• Diverse expertise required: The task spans multiple domains (research, writing, code, analysis)
• Quality through review: One agent creates, another critiques, improving output quality
• Parallel workstreams: Independent subtasks can run simultaneously for speed
• Complex decision-making: Multiple perspectives reduce blind spots

When a Single Agent Is Enough

Not every problem needs a team. A single agent is often better when:

• The task is well-defined and narrow in scope
• Coordination overhead would exceed the benefit
• Latency and cost constraints are tight
• The domain does not require multiple specializations

Key insight: Multi-agent systems trade simplicity for capability. The decision to go multi-agent should be driven by task complexity, not by novelty.

Collaboration Patterns

Multi-agent systems are not one-size-fits-all. The way agents interact defines the system's strengths and limitations. Three fundamental topologies cover most real-world scenarios.

Sequential (Pipeline)

Agents work in a chain. Each agent's output becomes the next agent's input, like an assembly line.

• Example: Researcher gathers data, Writer drafts a report, Reviewer checks quality
• Strength: Simple to reason about, clear accountability
• Weakness: Bottleneck at the slowest agent, no parallelism

Parallel (Fan-Out / Fan-In)

A coordinator sends the same task to multiple agents simultaneously, then merges their results.

• Example: Three analysts evaluate a business proposal from financial, legal, and technical angles
• Strength: Fast for independent subtasks, diverse perspectives
• Weakness: Requires a merge strategy, potential for conflicting outputs

Hierarchical (Manager-Worker)

A supervisor agent breaks down work, delegates to specialist agents, and synthesizes results. Workers may have their own sub-workers.

• Example: Project manager agent assigns coding tasks to frontend and backend agents, who each delegate to component-level agents
• Strength: Scales to complex problems, mirrors organizational structures
• Weakness: Supervisor becomes a single point of failure, deeper hierarchies add latency

Choosing a Pattern

The right topology depends on task structure. Sequential works for linear workflows. Parallel suits independent analysis. Hierarchical handles complex, multi-layered problems. Many production systems combine patterns -- for instance, a hierarchical system where each worker runs a sequential pipeline internally.

Key Takeaways

Before you move on, here's what to remember from this section:

• Use multi-agent systems when tasks require diverse expertise, parallel processing, or separation of concerns
• Topologies define how agents connect: sequential (pipeline), parallel (independent), hierarchical (supervised)
• Orchestration coordinates the team — routers dispatch, supervisors manage, consensus aggregates
• Frameworks like CrewAI, LangGraph, and AutoGen each offer different abstractions for building multi-agent systems
• Coordination overhead grows with agent count — structure your topology to minimize unnecessary communication