8 Proven Multi‑Agent Collaboration Patterns for Smarter AI Systems

Overview of Multi‑Agent Collaboration Patterns

Multi‑Agent Systems (MAS) enable complex tasks by distributing work among several specialized agents. The following sections describe four concrete collaboration patterns that are widely supported by frameworks such as CrewAI , AutoGen and OpenAI’s Swarm . Each pattern includes a brief workflow description, typical use‑cases, and implementation hints.

1. Reflection (Two‑Agent Loop) Mode

Workflow :

Agent A receives the original task and produces an initial output.

Agent B receives A’s output, evaluates or executes it, and returns feedback.

Agent A incorporates the feedback and generates a revised output.

Steps 2‑3 repeat until either a maximum iteration count is reached (e.g., max_iters=5) or Agent B signals approval.

Typical Parameters : max_iters: upper bound on loop cycles. stop_condition: custom predicate (e.g., confidence > 0.9) that lets Agent B terminate early.

Common Applications :

Code generation – one agent writes code, a second agent reviews style, correctness, or security.

Code interpreter – one agent produces code, a second agent runs it in a sandbox and returns execution results.

This pattern can be expressed as a “two‑person dialogue” where the agents assume distinct roles (producer vs. reviewer).

2. Sequential Mode

Workflow : A fixed chain of agents processes the task in a predetermined order. The output of each agent becomes the input of the next agent.

# Pseudocode example (Python‑like)
agents = [IdeaGenerator(), OutlineBuilder(), DraftWriter(), Polisher()]
input_data = user_prompt
for agent in agents:
    input_data = agent.run(input_data)
final_result = input_data

Typical Scenarios :

Marketing copy creation: idea → outline → article draft → polishing.

Market research: web search → report draft → PowerPoint generation.

HR resume screening: download resumes → filter candidates → draft and send reply emails.

This pattern suits deterministic pipelines and is the most common collaboration style in CrewAI‑style crews.

3. Hierarchical (Manager‑Worker) Mode

Workflow : A manager agent orchestrates a set of worker agents. The manager decides which sub‑tasks to assign, possibly using LLM reasoning over the current context. Workers operate without a fixed order; they may run in parallel or be invoked on demand.

Decision Strategies (examples from existing frameworks):

CrewAI : manager uses an LLM to infer the most appropriate worker based on task description.

AutoGen : supports random, sequential, automatic, or custom routing policies.

Use‑Cases :

Team brainstorming: manager leads a discussion, aggregates ideas from workers.

Distributed research: manager splits a complex research question into sub‑questions, assigns each to a worker, then aggregates the findings.

Intelligent Retrieval‑Augmented Generation (RAG): manager coordinates multiple workers that query different data sources and merges the results to answer cross‑source queries.

If the manager assigns a task to a single worker, the pattern collapses to a simple routing operation.

4. Transfer (Swarm) Mode

Workflow : Any agent can hand off a task to another agent by invoking a dedicated “transfer” tool. There is no global schedule or manager; each agent decides locally whether to continue processing or to transfer based on LLM‑driven judgment.

Key Characteristics :

No predetermined sequence – agents act opportunistically.

Each agent may expose multiple transfer tools (e.g., forward_to_product_info(), forward_to_after_sales()).

The LLM evaluates the current context and selects the appropriate tool at runtime.

Illustrative Scenario : A classification agent receives a customer query. Based on the query intent, it forwards the request to a product‑information agent or an after‑sales agent. Those agents may further transfer the request back to the classifier or to a human operator if needed.