AI Agent Architectures Compared
Compare the main AI agent architectures — ReAct, plan-and-execute, multi-agent, and more — so you can pick the right pattern for your production system.
Picking the wrong agent architecture costs you weeks. You build the system, hit a wall (hallucinated tool calls, runaway loops, outputs that drift from the original goal) and realize the structure itself is the problem. We've seen this at Laxaar across dozens of production deployments, and the pattern is consistent: teams choose an architecture based on demos they saw, not on the constraints of their actual workload.
Agent architectures aren't just patterns on a whiteboard. They determine how your system reasons, how it recovers from errors, and how much compute you burn per task. The right choice depends on task length, tool count, latency budget, and how much you trust the model to self-direct.
This article cuts through the noise. We compare the four architectures that show up repeatedly in production (ReAct, plan-and-execute, multi-agent, and hierarchical) with honest notes on when each one breaks.
What you'll learn
- What makes an agent architecture matter
- ReAct: the baseline single-loop pattern
- Plan-and-execute: separating thinking from acting
- Multi-agent systems: parallel workers with a coordinator
- Hierarchical agents: nested control structures
- Architecture comparison at a glance
- Frequently Asked Questions
What makes an agent architecture matter
An agent architecture is the control structure that determines how an LLM decides what to do next, calls tools, processes results, and terminates. It's distinct from the model itself or the tools available: two systems can use GPT-4o and the same tool set but behave completely differently depending on how the loop is wired.
Three variables drive architecture choice in practice: task complexity (how many steps, how much branching), error tolerance (can a bad intermediate step be recovered automatically), and latency budget (sequential plans are slow; parallel agents are fast but harder to coordinate).
Get this choice right early. Retrofitting an architecture onto a system that's already in production is painful.
ReAct: the baseline single-loop pattern
ReAct (Reasoning + Acting) is the single-agent, single-loop pattern described by Yao et al. (2022). The model alternates between producing a Thought, choosing an Action (tool call), and observing the Observation returned by the tool. This continues until the model produces a final answer.
# Minimal ReAct loop with LangChain (v0.2+)
from langchain.agents import create_react_agent, AgentExecutor
from langchain_openai import ChatOpenAI
from langchain import hub
llm = ChatOpenAI(model="gpt-4o", temperature=0)
prompt = hub.pull("hwchase17/react")
agent = create_react_agent(llm, tools=tools, prompt=prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True, max_iterations=10)
result = executor.invoke({"input": "What is the current EUR/USD rate and how does it compare to last week?"})
ReAct works well for tasks that need 2–6 tool calls, where each call informs the next. It's easy to debug because the thought-action-observation chain is fully visible. The max_iterations guard is not optional. Without it, a confused model loops indefinitely.
ReAct's real limitation is that it decides what to do one step at a time. For tasks with 15+ steps, it tends to lose context of the overall goal partway through. It also doesn't parallelize naturally: step N always waits for step N-1.
Plan-and-execute: separating thinking from acting
Plan-and-execute splits the agent into two distinct phases. A planner LLM call produces a full ordered list of steps before any tools are called. An executor then runs each step in sequence, passing results forward.
# Plan-and-execute with LangGraph (v0.2+)
from langgraph.prebuilt import create_react_agent
from langgraph.graph import StateGraph, END
from typing import TypedDict, List
class PlanExecuteState(TypedDict):
input: str
plan: List[str]
past_steps: List[tuple]
response: str
# Planner node: produce the step list
async def plan_step(state: PlanExecuteState):
plan = await planner.ainvoke({"objective": state["input"]})
return {"plan": plan.steps}
# Executor node: run one step at a time
async def execute_step(state: PlanExecuteState):
task = state["plan"][0]
result = await agent_executor.ainvoke({"input": task, "context": state["past_steps"]})
return {
"past_steps": state["past_steps"] + [(task, result["output"])],
"plan": state["plan"][1:],
}
The planning phase lets you inspect the full task decomposition before any tools run. That's valuable for expensive or irreversible tool calls: you can gate the execution on human review of the plan.
The downside is rigidity. The plan is fixed at step 0, and real-world tasks often reveal information mid-execution that changes what steps are needed. You can add a replanning node that runs when a step fails, but now you're adding latency and complexity. Our experience at Laxaar is that plan-and-execute shines for structured, well-specified workflows (document analysis pipelines, data transformation jobs) and struggles with open-ended research tasks where the path depends on what you find.
Multi-agent systems: parallel workers with a coordinator
Multi-agent systems use a coordinator that dispatches tasks to specialized sub-agents running in parallel. Each sub-agent has its own context, tool set, and LLM call budget. The coordinator collects their outputs and synthesizes a final answer.
# Coordinator dispatching to specialist agents with LangGraph
from langgraph.graph import StateGraph
from langgraph_sdk import get_client
async def coordinator_node(state):
# Identify subtasks from the user goal
subtasks = await coordinator_llm.ainvoke(state["input"])
# Dispatch each subtask to a specialist agent in parallel
results = await asyncio.gather(*[
run_specialist_agent(task) for task in subtasks
])
return {"specialist_results": results}
async def run_specialist_agent(task: str):
# Each specialist has a focused tool set
client = get_client()
run = await client.runs.create(
thread_id=None,
assistant_id=task.agent_type, # e.g. "web-search-agent", "code-agent"
input={"messages": [{"role": "user", "content": task.description}]}
)
return await client.runs.join(run["thread_id"], run["run_id"])
Parallel execution cuts wall-clock time dramatically on tasks that decompose cleanly. A research task that needs web search, database lookup, and code execution can run all three at once rather than sequentially.
The coordination overhead is real, though. The coordinator has to write good subtask descriptions, and when a specialist agent returns a partial or malformed result, the coordinator needs logic to retry or compensate. State synchronization between agents also adds engineering work. We don't recommend this pattern for teams new to agent systems. Start simpler, and reach for multi-agent when you've hit a concrete parallelism bottleneck.
Hierarchical agents: nested control structures
Hierarchical agents extend multi-agent systems by allowing sub-agents to spawn their own sub-agents. A top-level orchestrator manages high-level goals; mid-level managers handle subtask coordination; leaf agents execute individual tool calls.
This matches how large human engineering teams are organized, and it scales to genuinely complex long-horizon tasks. An orchestrator planning a software project might spawn a research manager (who spawns web-search agents and a summarization agent), an implementation manager (who spawns a coding agent and a testing agent), and a review manager.
Hierarchical systems are hard to debug. When a leaf agent fails, tracing the failure back through multiple coordination layers takes real tooling: structured logging at every layer, correlation IDs, and ideally a trace viewer. They also amplify cost: every coordination layer is an LLM call, and a misconfigured orchestrator can trigger an exponential number of sub-calls.
The honest position: hierarchical agents are the right architecture for a narrow class of tasks (long-horizon automation, multi-domain research, autonomous software development pipelines). For anything shorter, they're over-engineering.
Architecture comparison at a glance
| Architecture | Best for | Typical step count | Parallelism | Failure recovery | Complexity |
|---|---|---|---|---|---|
| ReAct | Short tool-use tasks | 2–8 | None (sequential) | Mid-loop retry | Low |
| Plan-and-execute | Structured workflows | 5–15 | None (sequential) | Replan on failure | Medium |
| Multi-agent | Parallelizable research/analysis | 10–30 (across agents) | High | Per-agent retry | High |
| Hierarchical | Long-horizon automation | 30+ | High | Complex | Very high |
One thing Laxaar's team has found consistent across projects: teams almost always underestimate the value of the simpler architecture. ReAct with a well-chosen tool set and a tight system prompt handles 70% of production use cases. Reach for plan-and-execute when you need auditability. Reach for multi-agent when you've measured a parallelism bottleneck. Reach for hierarchical only when multi-agent isn't enough.
For a deeper look at how these architectures handle context and memory between steps, see agent memory systems explained. And if you're evaluating which framework to build on, our agent frameworks comparison covers LangGraph, CrewAI, AutoGen, and others with benchmark data.
Our AI agents expertise page covers how we approach architecture selection in client engagements, and it's worth reading if you're scoping a new project.
Frequently Asked Questions
What's the simplest agent architecture I can start with?
ReAct is the right starting point for almost every project. It's well-documented, supported by every major framework, and easy to debug because the thought-action-observation chain is fully visible. Add complexity only when you hit a concrete problem that simpler structures can't solve.
When does plan-and-execute outperform ReAct?
Plan-and-execute wins when tasks have more than 8–10 steps, when you need a human-in-the-loop approval gate before execution starts, or when the task is well-specified enough that a complete plan can be written up front. It tends to lose on open-ended tasks where the next step depends on what prior steps found.
Do multi-agent systems cost more to run?
Yes, significantly. Each agent maintains its own context window and makes its own LLM calls. A coordinator that spawns five specialist agents running 10 steps each generates roughly 50+ LLM calls plus coordinator overhead. You should measure cost per task in your specific setup before committing to multi-agent in production.
What tracing tools work well for debugging hierarchical agents?
LangSmith (for LangChain/LangGraph systems), Arize Phoenix (model-agnostic), and Weights & Biases Weave are the three tools we see most in production. All three support nested trace views that let you follow a call chain from orchestrator down to leaf tool calls. Structured logging with shared correlation IDs is a prerequisite regardless of which tool you use.
Can I mix architectures in one system?
Yes, and it's often the right call. A common pattern is a plan-and-execute outer loop with a ReAct inner agent handling each step. The planner gets auditability; the step executor gets flexibility. LangGraph makes this straightforward because each node in a graph can itself be an agent graph.
How does architecture choice affect latency?
ReAct and plan-and-execute are fully sequential: each step waits for the previous one. A 10-step task with 2-second tool calls takes at least 20 seconds. Multi-agent systems run sub-tasks in parallel, so the same workload might complete in 5–6 seconds if the subtasks decompose cleanly. The coordination overhead (coordinator LLM call, result synthesis) typically adds 2–4 seconds on top.
If you're building an agent system and want a second opinion on architecture before you commit to an approach, talk to the Laxaar team. We've shipped production agent systems across finance, legal, and e-commerce and can help you avoid the expensive mistakes.
