Multi-Agent AI Systems: Orchestrating Multiple LLMs to Solve Hard Problems

A single LLM call has fundamental limitations: a fixed context window, a single reasoning thread, no ability to parallelise, and no self-verification. Multi-agent systems solve these limitations by decomposing complex tasks across multiple specialised agents — each with its own role, context, and tools — coordinated by an orchestrator. This is how you build AI systems that tackle problems no single prompt can solve.

Why multi-agent?

Consider a task like: "Research competitor pricing, analyse our cost structure, and produce a pricing recommendation." A single agent would produce a shallow result — it would attempt all three things in one context window, losing depth. A multi-agent system assigns each task to a specialist: a researcher agent (with web search tools), an analyst agent (with data processing tools), and a strategist agent (that synthesises both). Each agent focuses on one thing and does it well.

Multi-agent systems shine for: complex research pipelines, code generation + review workflows, document processing at scale, and any task requiring adversarial verification (one agent generates, another critiques).

Patterns for multi-agent coordination

There are four main coordination patterns:

• Sequential pipeline — Agent A's output feeds Agent B's input. Simple, predictable, but no parallelism.
• Parallel fan-out — An orchestrator sends the same task to multiple specialist agents simultaneously, then merges results. Best for research and analysis tasks.
• Supervisor pattern — A supervisor LLM decides which worker agent to call next based on intermediate results. Most flexible but hardest to debug.
• Debate/adversarial pattern — Two agents argue opposing positions on a question; a judge agent evaluates and produces a final answer. Dramatically reduces hallucination rates on high-stakes decisions.

Building a multi-agent system with LangGraph

from langgraph.graph import StateGraph, END
from typing import TypedDict, List

class AgentState(TypedDict):
    query: str
    research: str
    analysis: str
    final_answer: str

def research_agent(state: AgentState) -> AgentState:
    # Search the web and gather facts
    research = web_search(state["query"])
    return {**state, "research": research}

def analysis_agent(state: AgentState) -> AgentState:
    # Analyse the gathered research
    prompt = f"Research:
{state['research']}

Analyse this and extract key insights."
    analysis = call_llm(prompt)
    return {**state, "analysis": analysis}

def synthesis_agent(state: AgentState) -> AgentState:
    prompt = f"Research: {state['research']}
Analysis: {state['analysis']}

Write a final recommendation."
    answer = call_llm(prompt)
    return {**state, "final_answer": answer}

# Build the graph
graph = StateGraph(AgentState)
graph.add_node("research", research_agent)
graph.add_node("analysis", analysis_agent)
graph.add_node("synthesis", synthesis_agent)

graph.set_entry_point("research")
graph.add_edge("research", "analysis")
graph.add_edge("analysis", "synthesis")
graph.add_edge("synthesis", END)

app = graph.compile()

Microsoft AutoGen vs LangGraph vs CrewAI

• LangGraph — explicit graph-based control flow. Best when you need to see exactly what happens at each step and handle errors gracefully. Our preferred choice at DanixSoft.
• Microsoft AutoGen — conversation-based multi-agent framework. Agents talk to each other in a group chat until a task is complete. Best for code generation and debugging workflows (the AssistantAgent + UserProxyAgent pattern is powerful for automated coding tasks).
• CrewAI — role-based agents with tasks and tools defined declaratively. Fastest to prototype; can be opaque for complex workflows.

The adversarial verification pattern

The most impactful multi-agent pattern we use at DanixSoft is adversarial verification. For any decision with real consequences — a code change, a contract clause, a financial recommendation — we run two agents in parallel: a Generator that produces the answer, and a Critic that is explicitly prompted to find flaws, mistakes, and missing edge cases. A third Resolver agent synthesises both. This pattern reduces error rates on complex reasoning tasks by 30–50% compared to a single-agent approach.

async function adversarialVerify(task) {
  const [generated, critique] = await Promise.all([
    agent("Complete this task: " + task),
    agent("Find every flaw in this approach: " + task)
  ]);

  return agent(
    `Task: ${task}
Initial answer: ${generated}
Critique: ${critique}

Produce a corrected final answer.`
  );
}

Cost and latency considerations

Multi-agent systems can be expensive if poorly designed. Keys to keeping costs manageable: use the cheapest model capable of each sub-task (GPT-4o for complex reasoning, GPT-4o-mini for routing and classification), cache intermediate results aggressively, limit maximum iterations per agent, and monitor token usage per workflow run. A well-designed multi-agent system at DanixSoft typically costs $0.05–0.15 per complex task — comparable to a human spending 30–60 seconds on the same task. Contact our AI development team to design a system for your use case.