Multi-Agent Systems: Orchestrating AI for Complex Enterprise Workflows

Beyond Single Agents

A single agent with many tools becomes slow and unreliable. The solution is specialization — multiple agents, each expert in a narrow domain, coordinated by an orchestrator.

Patterns We Use

Supervisor Pattern: A supervisor agent delegates tasks to worker agents and aggregates results. Pipeline Pattern: Agents form a chain where each processes the output of the previous. Debate Pattern: Multiple agents generate competing solutions; a judge agent selects the best.

Microsoft AutoGen Example

import autogen

config = {"model": "gpt-4o", "api_key": "..."}

researcher = autogen.AssistantAgent("Researcher", llm_config=config)
analyst = autogen.AssistantAgent("Analyst", llm_config=config)
writer = autogen.AssistantAgent("Writer", llm_config=config)

groupchat = autogen.GroupChat(agents=[researcher, analyst, writer], messages=[], max_round=10)
manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=config)

researcher.initiate_chat(manager, message="Research and write a market analysis for EV adoption in India")