What Are AI Agent Orchestration Patterns? Coordinating Multiple Agents

As you move from single agents to multi-agent systems, orchestration patterns determine how agents coordinate. The right pattern can mean the difference between an efficient pipeline and a tangled mess. This guide covers the four main orchestration patterns and when to use each.

Pattern 1: Sequential orchestration

Agents work in sequence — Agent A's output becomes Agent B's input, which becomes Agent C's input. This is the simplest and most common pattern.

Example

Sales prospecting: Researcher finds companies → Enricher finds contacts → Writer drafts emails → Sender executes sequence.

Pros

• Simple to build and debug
• Clear flow of data
• Easy to add or remove steps

Cons

• Slow (each agent waits for the previous)
• Single point of failure (one agent fails, whole pipeline stops)
• Doesn't scale well for high volume

When to use

Most business workflows. Start here — it's the easiest pattern to implement correctly.

Pattern 2: Parallel orchestration

Multiple agents work simultaneously on different aspects of a task, then results are combined. This is faster but more complex.

Example

Research: Agent A searches news sources, Agent B searches academic papers, Agent C searches social media — all in parallel. A synthesis agent combines the results.

Pros

• Much faster than sequential
• Better coverage (multiple perspectives)
• More resilient (one agent failing doesn't stop others)

Cons

• More complex to build
• Requires result synthesis
• Higher cost (multiple agents running simultaneously)

When to use

Research tasks, data gathering, any workflow where speed matters and aspects are independent.

Pattern 3: Hierarchical orchestration

A "manager" agent coordinates "worker" agents. The manager decides what to do, delegates to workers, and synthesizes results.

Example

Content creation: Manager agent receives "write a blog post about X," delegates research to Researcher, drafting to Writer, editing to Editor. Manager reviews and approves.

Pros

• Natural for complex tasks
• Manager can adapt to failures
• Specialized workers excel at their tasks

Cons

• Most complex pattern
• Manager is a bottleneck and single point of failure
• Higher cost (manager + workers)

When to use

Complex workflows requiring judgment and adaptation. Most Relevance AI and Lindy multi-agent deployments use this pattern.

Pattern 4: Collaborative orchestration

Agents work together as peers, communicating and coordinating without a central manager. This is the most advanced pattern.

Example

Software development: A coding agent, a testing agent, and a review agent collaborate on a feature. They communicate directly, each contributing their expertise.

Pros

• Most flexible and adaptive
• No single point of failure
• Agents can dynamically adjust roles

Cons

• Extremely complex to build
• Hard to debug (emergent behavior)
• Can be unpredictable

When to use

Advanced use cases where flexibility is more important than predictability. Mostly experimental in 2026.

Choosing the right pattern

Implementation platforms

• Relevance AI — Supports all patterns, best for sequential and hierarchical
• Lindy.ai — Best for sequential patterns with Lindy-to-Lindy handoffs
• CrewAI — Open-source, best for hierarchical patterns
• LangGraph — Most flexible, supports all patterns

Common mistakes

• Starting too complex. Begin with sequential, move to parallel or hierarchical only when needed.
• Not handling failures. Every pattern needs error handling — what happens when one agent fails?
• Over-orchestrating. Don't use multi-agent when a single agent would work.
• Not monitoring. Multi-agent systems need observability even more than single agents.

Next steps

See our multi-agent orchestration guide for broader context, and our Relevance AI setup guide for building your first multi-agent workflow.