AI Agent Architecture Patterns: ReAct, Plan-Execute, and More

When developers start building AI agents, they usually hit the same wall: a simple prompt-response loop stops being enough. The agent hallucinates a tool call, forgets what it was doing halfway through a task, or gets stuck in a loop it cannot escape. The missing ingredient is almost always architecture - a deliberate choice about how the agent thinks, acts, and corrects itself over time.

This guide covers the most important agent architecture patterns in use today. Each pattern solves a specific class of problem. Understanding them helps you choose the right design before you write a single line of code, and it helps you diagnose what is breaking when your agent starts misbehaving.

What is an agent architecture pattern?

An agent architecture pattern is a template for how an agent moves from a goal to a completed result. It defines the control flow: which component runs first, how observations feed back into planning, when the agent asks for more information, and what happens when something goes wrong.

Think of it the same way you think about software design patterns like MVC or event sourcing. The pattern does not solve the specific problem for you, but it gives you a proven structure that avoids a known category of failure. Most production AI agents today are built on one of five core patterns, sometimes combined.

Before diving in, if you are new to how agents work at a lower level, read How Do AI Agents Work? first. This guide assumes you understand the basic perception-action loop.

1. ReAct (Reasoning + Acting)

ReAct is the most widely deployed agent pattern today. The name comes from the 2022 paper "ReAct: Synergizing Reasoning and Acting in Language Models" by Yao et al. The core idea is simple: before the agent takes any action, it writes out its reasoning in plain text. That reasoning then becomes part of the context for the next step.

The loop looks like this:

1. The agent receives the task.
2. It generates a Thought - a brief internal monologue about what it needs to do next.
3. It chooses an Action from the available tools.
4. The tool runs and returns an Observation.
5. The agent reads the observation, generates another Thought, and either picks another action or declares the task complete.

This interleaving of thought and action is what separates ReAct from a naive "call a tool and return the result" loop. The agent can reason about whether the observation answered its question, decide to try a different tool, or recognize that it has enough information to give a final answer without calling another tool.

ReAct works well for tasks that are sequential and where each step depends on the result of the previous one - things like research tasks, multi-step database queries, or any workflow where the agent cannot predict upfront exactly which tools it will need.

The main weakness is that ReAct agents can get stuck in unhelpful loops, especially when a tool returns an error or an unexpected format. The agent may keep retrying the same action in slightly different ways without making progress. Adding a maximum-step counter and an explicit fallback helps here.

LangGraph is one of the most popular frameworks for implementing ReAct agents because its graph-based structure lets you model the thought-action-observation cycle as explicit nodes with controlled transitions between them.

2. Plan-Execute (Plan-and-Solve)

Where ReAct reasons one step at a time, Plan-Execute separates planning from execution entirely. The agent first produces a complete plan - a numbered list of subtasks - and then executes each subtask in order, or in parallel where steps are independent.

The two-phase structure looks like this:

Phase 1 - Planning: The agent receives the goal and generates a structured plan. This might be a list of five steps, a tree of subtasks, or a directed graph showing which steps depend on which.

Phase 2 - Execution: A separate executor (sometimes another LLM call, sometimes a deterministic function) works through the plan step by step. It can report results back to the planner, which may revise the plan based on what it learns.

Plan-Execute is stronger than ReAct when the task is long, involves many interdependent steps, or when you want the user to be able to review and modify the plan before execution begins. Software development tasks are a good fit - you plan the files to create, the tests to write, and the order of operations before touching anything.

The risk is that a bad plan produces bad results even if each individual execution step is correct. If step 3 of the plan relies on an assumption that turns out to be wrong, the entire remainder of the plan can become invalid. Good implementations add a replanning step that runs when an execution step fails or returns an unexpected result.

CrewAI uses a version of this pattern when you define an ordered crew of agents where each agent hands its output to the next - the agent roles map to plan steps, and CrewAI handles the sequencing.

3. Reflection

The Reflection pattern adds a self-evaluation loop to either ReAct or Plan-Execute. After the agent produces a result - whether that is a piece of code, a written document, or a research summary - a second pass evaluates the result against criteria and either approves it or sends it back for revision.

The simplest version is a single LLM call that acts as a critic: "Here is the output from the previous step. Does it fully address the original task? If not, what is missing?" The critique then feeds back into the generator, which produces a revised output. This loop runs until the critic approves or a maximum number of iterations is reached.

More sophisticated implementations use separate prompts for the generator and the critic - or separate models entirely. The generator is prompted to be creative and thorough, while the critic is prompted to be strict and look specifically for gaps, factual errors, or style violations.

Reflection is especially useful for tasks where quality is hard to specify upfront but recognizable in the output: writing tasks, code review, data extraction where completeness matters, and any workflow where "good enough" has a clear definition that a model can evaluate.

The cost is latency and token usage - every reflection cycle is an additional LLM call. For high-volume production use, it is worth measuring whether the reflection step actually improves the output quality enough to justify the added cost.

4. LATS (Language Agent Tree Search)

LATS extends ReAct with tree search. Instead of committing to a single chain of thought-action-observation steps, the agent explores multiple possible paths simultaneously and uses a value function to decide which branch to continue.

At each decision point, the agent generates several candidate actions. For each candidate, it simulates or evaluates the likely outcome, assigns a score, and picks the most promising branch to explore further. If a branch hits a dead end or a low-score state, it backtracks and tries a different path.

LATS is computationally expensive but produces dramatically better results on tasks where the first obvious approach is wrong and the correct solution requires backtracking. Math problem solving, code debugging, and strategic planning are where it shines most clearly.

In practice, full LATS is rarely deployed in production because of the cost. Most teams use a lighter version where the agent generates two or three candidate plans, evaluates them, and picks the best one without doing deep tree search. This captures most of the benefit at a fraction of the cost.

5. Multi-Agent Orchestration

Multi-agent architecture is not a single pattern but a category of patterns where the work is distributed across multiple specialized agents that communicate with each other.

The most common form is the orchestrator-worker model:

• An orchestrator agent receives the top-level task, breaks it into subtasks, assigns each subtask to a worker agent, and assembles the final result.
• Worker agents are specialized for specific tasks: one handles web search, one handles code execution, one handles document summarization.

This separation of concerns mirrors the way software engineering teams work. No single engineer does everything - they specialize, and a team lead coordinates their work.

The advantage is that each agent can be prompted and tuned for its specific role without that tuning conflicting with the needs of other roles. A code agent can be prompted to be very precise and conservative; a research agent can be prompted to be exploratory.

The challenge is coordination. Agents need to pass information between each other in a structured way, and the orchestrator needs to handle cases where a worker agent fails or returns an unusable result. This is harder than it looks when agents are non-deterministic.

AutoGen is built specifically for multi-agent workflows. It provides a conversation protocol that lets agents talk to each other, a framework for defining agent roles, and utilities for managing group chats where multiple agents contribute to a shared task.

LangGraph also supports multi-agent workflows using its supervisor pattern, where a supervisor node routes tasks to specialized subgraph agents.

6. Tool-Augmented Generation with Memory

This pattern is less about control flow and more about the agent's relationship to persistent state. A pure ReAct agent starts fresh on every run - it has no memory of previous sessions. A memory-augmented agent has access to a knowledge store that persists between runs: past conversations, learned facts, documents it has processed.

The two types of memory that matter most in practice are:

Working memory - the agent's in-context state during a single run. This is just the context window. Keeping working memory organized (what has been done, what is outstanding, what failed) is a design problem most teams solve with structured prompts or explicit state objects rather than leaving it to the model.

Long-term memory - a vector database, a document store, or a key-value store that the agent can query across sessions. The agent generates an embedding of a query, retrieves relevant documents, and injects them into its context before generating a response. This is the RAG (Retrieval-Augmented Generation) pattern applied inside an agent loop.

Memory changes what kinds of tasks an agent can handle. Without long-term memory, every session is stateless. With it, an agent can remember a user's preferences, build up a knowledge base over time, and avoid repeating work it has already done.

The cost is retrieval quality. If the memory retrieval step returns the wrong documents - either missing relevant information or flooding the context with irrelevant noise - the agent's performance degrades. Hybrid retrieval (combining dense vector search with sparse keyword search) is the current best practice for minimizing this problem.

How these patterns combine in practice

Real production agents almost never use a single pattern in isolation. A typical setup for a software engineering agent might look like this:

• Plan-Execute at the top level to break the feature request into a set of files and changes.
• ReAct inside each execution step to handle the actual file manipulation, running tests, and interpreting output.
• Reflection at the end of each Plan-Execute step to check whether the output matches the spec before moving to the next step.
• Multi-agent for tasks that need parallel work - for example, running linting and security analysis simultaneously on the generated code.
• Memory to persist the codebase context across sessions so the agent does not have to re-read every file from scratch.

Choosing the right combination depends on the task length, the tolerance for latency, the budget for LLM calls, and the quality bar you need to hit. Simple tasks often need nothing more than a well-structured ReAct loop. Complex autonomous workflows need the full stack.

Choosing the right pattern

A few questions that help narrow the choice quickly:

Is the task short and sequential? Start with ReAct. It is the simplest, fastest, and best understood pattern. Most agents do not need more than this.

Does the task have many interdependent steps that can be planned upfront? Add Plan-Execute. Define the plan structure clearly, and build in replanning for when steps fail.

Does output quality matter more than speed? Add Reflection. Decide upfront what "good" looks like and encode that in the critic prompt.

Do you need specialists? Use Multi-Agent. Define clear roles, define clear input/output contracts between agents, and use a framework like AutoGen or CrewAI that handles the coordination layer for you.

Does the agent need to remember past runs? Add memory. Start with a simple key-value store for structured facts and a vector database for document retrieval.

The architecture choices you make early are hard to change later because they affect how you structure prompts, how you write tests, and how you instrument the system for debugging. Spending time on the design before writing code is rarely wasted.

What comes next in agent design

The field is moving toward agents that can dynamically choose their own architecture depending on the task - starting with a simple ReAct loop and escalating to multi-agent coordination only when the task complexity demands it. The scaffolding for this kind of dynamic architecture exists in frameworks like LangGraph today, though it requires careful design to avoid the agent spinning up unnecessary complexity.

Evaluation tooling is also maturing. Testing an agent used to mean running it manually and checking the output by eye. Now there are structured evaluation frameworks that can run hundreds of test cases against an agent and report pass rates, error categories, and cost per successful completion. Building that evaluation layer alongside the agent - not as an afterthought - is becoming standard practice.

Understanding the base patterns covered here is the prerequisite for all of that work. Agents that misbehave in production almost always do so because the architecture was not matched to the task, not because the underlying model was too weak.