AI agent architecture patterns.
The main ways to structure an AI agent, from a single tool-using loop to multi-agent supervisor and pipeline designs, and how to pick the right one for the job.
ZENKEI
ZENKEI
The main ways to structure an AI agent, from a single tool-using loop to multi-agent supervisor and pipeline designs, and how to pick the right one for the job.
AI agent architecture patterns are reusable ways to structure how an agent reasons and acts. They range from a single tool-using agent, through reflection and planning, to multi-agent designs such as supervisor-worker and pipelines. The right pattern depends on the complexity of the task, not on fashion, and choosing the simplest one that works is the whole skill.
An agent architecture pattern is a repeatable shape for an agentic system: how the model decides, which tools it can reach, how work is divided, and how state flows between steps. The same handful of parts appears in every pattern, so it helps to name them before comparing designs. If you are new to agents, start with our guide on how to build an AI agent, then come back for the patterns.
A pattern is just a particular arrangement of those parts. The sections below cover the single-agent patterns first, because most production systems need nothing more, then the multi-agent patterns for when a task genuinely splits into roles.
One model, one loop, a focused set of tools. These patterns are cheaper, faster and far easier to debug than multi-agent systems, and they solve the large majority of real problems.
The default agent pattern. The model reasons about what to do, calls a tool, observes the result, and repeats until it can answer. ReAct stands for reason plus act, and it is the loop underneath most agents. Use it as your starting point for almost any task, and only add structure when a concrete problem forces you to.
When an agent has many possible actions, a router first classifies the request and sends it to the right tool, skill or sub-prompt. Routing keeps each path small and reliable, and it is often enough on its own for support and triage use cases where the job is mostly "pick the right action and run it".
The agent generates an answer, then critiques its own work against explicit criteria and revises it before returning a result. Reflection measurably improves writing, code and multi-step reasoning, at the cost of extra model calls. Add it when quality matters more than latency, and keep the review criteria specific so the critique is useful.
For longer tasks, the agent first writes a plan, a short list of steps, then executes them one by one, replanning if something changes. Separating planning from execution keeps the agent on track over many steps and makes its behaviour easier to inspect. Reach for it when a ReAct loop starts wandering on complex, multi-part goals.
When the agent needs knowledge it was not trained on, give it retrieval as a tool: it searches your documents, reads the relevant passages, and grounds its answer in them. This is where an agent meets RAG, and it is the most reliable way to keep answers factual and current. Grounding also cuts hallucinations, which we cover in how to reduce AI hallucinations.
When a task splits into clearly distinct roles, or a single prompt and tool set has grown too large to stay reliable, several specialised agents can work together. The cost is coordination: more agents mean more ways to fail, so use these patterns deliberately.
A supervisor agent breaks the goal into subtasks and delegates each to a specialised worker (research, writing, code, review), then combines the results. The supervisor-worker pattern, sometimes called an orchestrator, is the most common multi-agent design because it keeps a single point of control while letting each worker stay focused.
Agents are chained so the output of one becomes the input of the next, like an assembly line: extract, then transform, then draft, then review. A pipeline fits work with a fixed, well-understood order of stages, and it is easy to reason about because control flows in one direction.
One agent produces work while a second evaluates it against a rubric and sends feedback, looping until the output passes. This evaluator-optimizer pair is reflection split across two agents, and it is useful when the quality bar is high and can be written down as explicit criteria.
The shape of a supervisor is small: it picks the next worker, runs it, records the result, and decides whether the goal is met. Frameworks such as LangGraph add state, retries and streaming, but the core is this.
workers = {
"research": research_agent,
"write": writer_agent,
"review": review_agent,
}
def supervisor(goal, max_rounds=6):
state = {"goal": goal, "notes": []}
for _ in range(max_rounds):
nxt = route(state) # decide who acts next
if nxt == "done":
return compose(state) # goal met
result = workers[nxt](state) # delegate to a worker
state["notes"].append((nxt, result))
return compose(state) # stop: round limitMatch the architecture to the task, and add complexity only when a real problem demands it. In practice the decision follows a short order.
One model, a small set of tools, a clear goal and a step limit. This handles most tasks and is the easiest thing to debug and ship.
If answers are sloppy, add a reflection step. If the agent wanders on long tasks, add plan-and-execute. Both are still one agent.
When the task depends on your documents or live data, give the agent retrieval as a tool rather than stuffing everything into the prompt.
Reach for a supervisor or pipeline when the work truly divides into separate specialisations, or when one agent's tool set has become unreliable. Not before.
Whatever the pattern, four things decide whether an agent is safe to run in production, and they apply to single and multi-agent designs alike:
Choosing and building the pattern that fits your task, with the tools, guardrails and evaluation to run it in production, is our AI agent development service. Pair it with RAG development when the agent needs your knowledge, or explore the full AI hub.
An AI agent architecture pattern is a reusable way to structure how an agent reasons and acts. Patterns range from a single tool-using agent, through reflection and planning, to multi-agent designs like supervisor-worker and pipelines. The right pattern depends on the complexity of the task, not on fashion.
A single-agent architecture uses one model with a set of tools and one reasoning loop. A multi-agent architecture splits the work across several specialised agents coordinated by a supervisor or a pipeline. Single agents are simpler and easier to debug; multi-agent designs help when a task genuinely divides into distinct roles.
ReAct, short for reason and act, is the most common agent pattern: the model reasons about what to do, calls a tool, observes the result, and repeats until the task is done. It is the default single-agent loop and solves most problems on its own.
Use a supervisor or multi-agent pattern when a task splits into clearly distinct roles, such as research, writing and review, or when a single prompt and tool set has grown too large to stay reliable. Otherwise a single agent is cheaper, faster and easier to maintain.
Reflection is a pattern where the agent critiques its own output and revises it before returning a final answer. A generate step is followed by a review step against explicit criteria, which improves quality on writing, code and reasoning at the cost of extra model calls.
Start with the simplest pattern that could work, usually a single ReAct agent, then add reflection or planning if quality demands it, retrieval if it needs your knowledge, and multiple agents only when the task has distinct roles. Choosing the simplest design that works is the core skill.
Tell us the task. We choose the architecture and build the agent around it.